Description

In this course we are going to look at advanced NLP.

Previously, you learned about some of the basics, like how many NLP problems are just regular machine learning and data science problems in disguise, and simple, practical methods like bag-of-words and term-document matrices.

These allowed us to do some pretty cool things, like detect spam emails, write poetry, spin articles, and group together similar words.

In this course I’m going to show you how to do even more awesome things. We’ll learn not just 1, but 4 new architectures in this course.

First up is word2vec.

In this course, I’m going to show you exactly how word2vec works, from theory to implementation, and you’ll see that it’s merely the application of skills you already know.

Word2vec is interesting because it magically maps words to a vector space where you can find analogies, like:

• king - man = queen - woman
• France - Paris = England - London
• December - Novemeber = July - June

We are also going to look at the GLoVe method, which also finds word vectors, but uses a technique called matrix factorization, which is a popular algorithm for recommender systems.

Amazingly, the word vectors produced by GLoVe are just as good as the ones produced by word2vec, and it’s way easier to train.

We will also look at some classical NLP problems, like parts-of-speech tagging and named entity recognition, and use recurrent neural networks to solve them. You’ll see that just about any problem can be solved using neural networks, but you’ll also learn the dangers of having too much complexity.

Lastly, you’ll learn about recursive neural networks, which finally help us solve the problem of negation in sentiment analysis. Recursive neural networks exploit the fact that sentences have a tree structure, and we can finally get away from naively using bag-of-words.

All of the materials required for this course can be downloaded and installed for FREE. We will do most of our work in Numpy, Matplotlib, and Theano. I am always available to answer your questions and help you along your data science journey.

This course focuses on "how to build and understand", not just "how to use". Anyone can learn to use an API in 15 minutes after reading some documentation. It's not about "remembering facts", it's about "seeing for yourself" via experimentation. It will teach you how to visualize what's happening in the model internally. If you want more than just a superficial look at machine learning models, this course is for you.

See you in class!

NOTES:

All the code for this course can be downloaded from my github: /lazyprogrammer/machine_learning_examples

In the directory: nlp_class2

Make sure you always "git pull" so you have the latest version!

TIPS (for getting through the course):

• Watch it at 2x.
• Take handwritten notes. This will drastically increase your ability to retain the information.
• Write down the equations. If you don't, I guarantee it will just look like gibberish.
• Ask lots of questions on the discussion board. The more the better!
• Realize that most exercises will take you days or weeks to complete.
• Write code yourself, don't just sit there and look at my code.

USEFUL COURSE ORDERING:

• (The Numpy Stack in Python)
• Linear Regression in Python
• Logistic Regression in Python
• (Supervised Machine Learning in Python)
• (Bayesian Machine Learning in Python: A/B Testing)
• Deep Learning in Python
• Practical Deep Learning in Theano and TensorFlow
• (Supervised Machine Learning in Python 2: Ensemble Methods)
• Convolutional Neural Networks in Python
• (Easy NLP)
• (Cluster Analysis and Unsupervised Machine Learning)
• Unsupervised Deep Learning
• (Hidden Markov Models)
• Recurrent Neural Networks in Python
• Artificial Intelligence: Reinforcement Learning in Python
• Natural Language Processing with Deep Learning in Python

HARD PREREQUISITES / KNOWLEDGE YOU ARE ASSUMED TO HAVE:

• calculus
• linear algebra
• probability (conditional and joint distributions)
• Python coding: if/else, loops, lists, dicts, sets
• Numpy coding: matrix and vector operations, loading a CSV file
• neural networks and backpropagation
• Can write a feedforward neural network in Theano and TensorFlow
• Can write a recurrent neural network / LSTM / GRU in Theano and TensorFlow

Who is the target audience?

• Students and professionals who want to create word vector representations for various NLP tasks
• Students and professionals who are interested in state-of-the-art neural network architectures like recursive neural networks
• SHOULD NOT: Anyone who is not comfortable with the prerequisites.

Description

This Python course will get you up and running with using Python for data analysis and visualization. You will learn how to handle, analyze and visualize data in Python by actually completing two big data analysis projects, one demonstrated through videos and another laid out through six exercises.  

The course assumes you have no prior knowledge of Python, so you also get to learn the basics of Python in the first two sections of the course. However, if you already know Python, the first two sections can serve as a refresher before you jump into the data analysis and visualization part.

In the course you will learn to use Python third-party data analysis libraries such as Pandas, Matplotlib, Seaborn, just to mention a few and tools to boost your productivity such as Spyder and Jupyter.

As you progress through the course, you will be guided step by step on building a program that uses real world data containing hundreds of files and millions of records. You will learn to write Python code that downloads, extracts, cleans, manipulates, aggregates and visualizes these datasets using Python. Apart from following the video screencasts, you will also be required to write your own Python scripts from scratch for completing a data analysis project on income data.

Who is the target audience?

• Those who come from any technology field that deals with any kind of data.
• Those who want to leverage the power of the Python programming language for handling data.
• Those who need to learn Python basics and want to quickly advance their skills by learning how to perform data cleaning, analysis and visualization with Python - all in one single course.
• Those who want to switch from programming languages such as Java, C, R, Matlab, etc. to Python.

Description

Learn Python Programming by doing!

There are lots of Python courses and lectures out there. However, Python has a very steep learning curve and students often get overwhelmed. This course is different!

This course is truly step-by-step. In every new tutorial we build on what had already learned and move one extra step forward.

After every video you learn a new valuable concept that you can apply right away. And the best part is that you learn through live examples.

This training is packed with real-life analytical challenges which you will learn to solve. Some of these we will solve together, some you will have as homework exercises.

In summary, this course has been designed for all skill levels and even if you have no programming or statistical background you will be successful in this course!

I can't wait to see you in class,

Sincerely,

Kirill Eremenko

Who is the target audience?

• This course if for you if you want to learn how to program in Python
• This course is for you if you are tired of Python courses that are too complicated
• This course is for you if you want to learn Python by doing
• This course is for you if you like exciting challenges
• You WILL have homework in this course so you have to be prepared to work on it

Description

========================= TESTIMONIALS ===============================

"If you can take just one Python course, make sure it's this one." - A. Barbosa

"The information is extremely well presented. Best Python training I have found so far, and I have been looking for several weeks!" - Tanara

"I have tried many python courses on Udemy but this one is the best of them all." - Natalie

"I have other python courses on Udemy but this one is the best by far. It explains things in a very hands on and easy to follow method that will have you understanding what you are doing in no time." - Robert Rodono

"High Energy, Interesting and has a sense of humor making the material more comprehensible and clear." - Ronald Cosentino

"It is more like a friend showing how to use a new toy than a professor reminding you of your ignorance." - Sylvester Pierce

“I was highly impressed with the teaching. It was sophisticated and highly professional.  I imagine there are a lot of people who pay a huge amount of money to be taught by teachers greatly inferior to Ziyad. He was fantastic."  - Matthew

“The sessions have always been very engaging and structured in an effective manner. He is very patient, approachable and enthusiastic about the content he teaches.” – Jay

“Ziyad is and will always continue to be an excellent tutor.” - Jessie

======================================================================

It's no secret that Project-based learning is proven to be the single most-effective way to learn any skill, but this is especially true for Programming!

If you are looking to Learn the Python programming language with a hands on approach, then have come to the right place.

Go from a complete beginner to building 11 Projects with the Python Bible, Udemy's premier Project-based Python Programming Course!

This comprehensive, in-depth and meticulously prepared course is going to teach you everything you need to know to program in Python!  A - Z, it's all here!

No more rummaging through YouTube videos, documentation and random stack-overflow posts to find the information you crave. This course gives you instant access to everything you need to know to get programming in Python, and puts it all right at your fingertips!

Hi, I'm Ziyad. I am a prize winning University lecturer of foundation level computer science, and delivering project based learning is what I do best!

In this authoritative and illuminating course, I am going to teach you about:

Variables - Learn to conveniently store data in your programs! 
Numbers - Learn how numbers work behind the scenes in your programs!
Strings -  Master Python Text and automate messages using Strings!
Logic and Datastructures - Teach your program to think and decide!
Loops - Save time and effort, by making computers do the hard work for you!
Functions - Build your very own Python Functions that you can use over and over!
OOP - Learn Object Oriented Programming, the industry programming standard!

So whether you want to get into Data Science, Web Development or make cool robots with the Raspberry Pi, The Python Bible gives you everything you need to get started on your path!

The Course also comes with a Zero Risk, 30 Day 100% Money Back Guarantee!

Test drive the course for a full 30 days, try it out, and if you aren’t happy, I will happily refund 100% of your money. I know you are going to love the course, but yes, you have a rock-solid 30 day money back guarantee!

What do you have to lose? 

 Enrol in the Python Bible Now! You won't be disappointed...

Who is the target audience?

• For People Who want to learn Python Fundamentals and later transition into Data Science or Web Development
• For Complete Programming Beginners
• For People New to Python
• Not Intended for Seasoned Developers

Description

Welcome! This is Deep Learning, Machine Learning, and Data Science Prerequisites: The Numpy Stack in Python.

One question or concern I get a lot is that people want to learn deep learning and data science, so they take these courses, but they get left behind because they don’t know enough about the Numpy stack in order to turn those concepts into code.

Even if I write the code in full, if you don’t know Numpy, then it’s still very hard to read.

This course is designed to remove that obstacle - to show you how to do things in the Numpy stack that are frequently needed in deep learning and data science.

So what are those things?

Numpy. This forms the basis for everything else.  The central object in Numpy is the Numpy array, on which you can do various operations.

The key is that a Numpy array isn’t just a regular array you’d see in a language like Java or C++, but instead is like a mathematical object like a vector or a matrix.

That means you can do vector and matrix operations like addition, subtraction, and multiplication.

The most important aspect of Numpy arrays is that they are optimized for speed. So we’re going to do a demo where I prove to you that using a Numpy vectorized operation is faster than using a Python list.

Then we’ll look at some more complicated matrix operations, like products, inverses, determinants, and solving linear systems.

Pandas. Pandas is great because it does a lot of things under the hood, which makes your life easier because you then don’t need to code those things manually.

Pandas makes working with datasets a lot like R, if you’re familiar with R.

The central object in R and Pandas is the DataFrame.

We’ll look at how much easier it is to load a dataset using Pandas vs. trying to do it manually.

Then we’ll look at some dataframe operations, like filtering by column, filtering by row, the apply function, and joins, which look a lot like SQL joins.

So if you have an SQL background and you like working with tables then Pandas will be a great next thing to learn about.

Since Pandas teaches us how to load data, the next step will be looking at the data. For that we will use Matplotlib.

In this section we’ll go over some common plots, namely the line chart, scatter plot, and histogram.

We’ll also look at how to show images using Matplotlib.

99% of the time, you’ll be using some form of the above plots.

Scipy.

I like to think of Scipy as an addon library to Numpy.

Whereas Numpy provides basic building blocks, like vectors, matrices, and operations on them, Scipy uses those general building blocks to do specific things.

For example, Scipy can do many common statistics calculations, including getting the PDF value, the CDF value, sampling from a distribution, and statistical testing.

It has signal processing tools so it can do things like convolution and the Fourier transform.

In sum:

If you’ve taken a deep learning or machine learning course, and you understand the theory, and you can see the code, but you can’t make the connection between how to turn those algorithms into actual running code, this course is for you.

All the code for this course can be downloaded from my github: /lazyprogrammer/machine_learning_examples

In the directory: numpy_class

Make sure you always "git pull" so you have the latest version!

HARD PREREQUISITES / KNOWLEDGE YOU ARE ASSUMED TO HAVE:

• linear algebra
• probability
• Python coding: if/else, loops, lists, dicts, sets
• you should already know "why" things like a dot product, matrix inversion, and Gaussian probability distributions are useful and what they can be used for

TIPS (for getting through the course):

• Watch it at 2x.
• Take handwritten notes. This will drastically increase your ability to retain the information.
• Ask lots of questions on the discussion board. The more the better!
• Realize that most exercises will take you days or weeks to complete.

USEFUL COURSE ORDERING:

• (The Numpy Stack in Python)
• Linear Regression in Python
• Logistic Regression in Python
• (Supervised Machine Learning in Python)
• Deep Learning in Python
• Practical Deep Learning in Theano and TensorFlow
• Convolutional Neural Networks in Python
• (Easy NLP)
• (Cluster Analysis and Unsupervised Machine Learning)
• Unsupervised Deep Learning
• (Hidden Markov Models)
• Recurrent Neural Networks in Python
• Natural Language Processing with Deep Learning in Python

Who is the target audience?

• Students and professionals with little Numpy experience who plan to learn deep learning and machine learning later
• Students and professionals who have tried machine learning and data science but are having trouble putting the ideas down in code

Description

This course is all about A/B testing.

A/B testing is used everywhere. Marketing, retail, newsfeeds, online advertising, and more.

A/B testing is all about comparing things.

If you’re a data scientist, and you want to tell the rest of the company, “logo A is better than logo B”, well you can’t just say that without proving it using numbers and statistics.

Traditional A/B testing has been around for a long time, and it’s full of approximations and confusing definitions.

In this course, while we will do traditional A/B testing in order to appreciate its complexity, what we will eventually get to is the Bayesian machine learning way of doing things.

First, we’ll see if we can improve on traditional A/B testing with adaptive methods. These all help you solve the explore-exploit dilemma.

You’ll learn about the epsilon-greedy algorithm, which you may have heard about in the context of reinforcement learning.

We’ll improve upon the epsilon-greedy algorithm with a similar algorithm called UCB1.

Finally, we’ll improve on both of those by using a fully Bayesian approach.

Why is the Bayesian method interesting to us in machine learning?

It’s an entirely different way of thinking about probability.

It’s a paradigm shift.

You’ll probably need to come back to this course several times before it fully sinks in.

It’s also powerful, and many machine learning experts often make statements about how they “subscribe to the Bayesian school of thought”.

In sum - it’s going to give us a lot of powerful new tools that we can use in machine learning.

The things you’ll learn in this course are not only applicable to A/B testing, but rather, we’re using A/B testing as a concrete example of how Bayesian techniques can be applied.

You’ll learn these fundamental tools of the Bayesian method - through the example of A/B testing - and then you’ll be able to carry those Bayesian techniques to more advanced machine learning models in the future.

See you in class!

All the code for this course can be downloaded from my github: /lazyprogrammer/machine_learning_examples

In the directory: ab_testing

Make sure you always "git pull" so you have the latest version!

HARD PREREQUISITES / KNOWLEDGE YOU ARE ASSUMED TO HAVE:

• calculus
• probability (continuous and discrete distributions, joint, marginal, conditional, PDF, PMF, CDF, Bayes rule)
• Python coding: if/else, loops, lists, dicts, sets
• Numpy, Scipy, Matplotlib

TIPS (for getting through the course):

• Watch it at 2x.
• Take handwritten notes. This will drastically increase your ability to retain the information.
• Write down the equations. If you don't, I guarantee it will just look like gibberish.
• Ask lots of questions on the discussion board. The more the better!
• Realize that most exercises will take you days or weeks to complete.
• Write code yourself, don't just sit there and look at my code.

USEFUL COURSE ORDERING:

• (The Numpy Stack in Python)
• Linear Regression in Python
• Logistic Regression in Python
• (Supervised Machine Learning in Python)
• (Bayesian Machine Learning in Python: A/B Testing)
• Deep Learning in Python
• Practical Deep Learning in Theano and TensorFlow
• (Supervised Machine Learning in Python 2: Ensemble Methods)
• Convolutional Neural Networks in Python
• (Easy NLP)
• (Cluster Analysis and Unsupervised Machine Learning)
• Unsupervised Deep Learning
• (Hidden Markov Models)
• Recurrent Neural Networks in Python
• Artificial Intelligence: Reinforcement Learning in Python
• Natural Language Processing with Deep Learning in Python
• Advanced AI: Deep Reinforcement Learning in Python

Who is the target audience?

• Students and professionals with a technical background who want to learn Bayesian machine learning techniques to apply to their data science work

Description

This is the 3rd part in my Data Science and Machine Learning series on Deep Learning in Python. At this point, you already know a lot about neural networks and deep learning, including not just the basics like backpropagation, but how to improve it using modern techniques like momentum and adaptive learning rates. You've already written deep neural networks in Theano and TensorFlow, and you know how to run code using the GPU.

This course is all about how to use deep learning for computer vision using convolutional neural networks. These are the state of the art when it comes to image classification and they beat vanilla deep networks at tasks like MNIST.

In this course we are going to up the ante and look at the StreetView House Number (SVHN) dataset - which uses larger color images at various angles - so things are going to get tougher both computationally and in terms of the difficulty of the classification task. But we will show that convolutional neural networks, or CNNs, are capable of handling the challenge!

Because convolution is such a central part of this type of neural network, we are going to go in-depth on this topic. It has more applications than you might imagine, such as modeling artificial organs like the pancreas and the heart. I'm going to show you how to build convolutional filters that can be applied to audio, like the echo effect, and I'm going to show you how to build filters for image effects, like the Gaussian blur and edge detection.

We will also do some biology and talk about how convolutional neural networks have been inspired by the animal visual cortex.

After describing the architecture of a convolutional neural network, we will jump straight into code, and I will show you how to extend the deep neural networks we built last time (in part 2) with just a few new functions to turn them into CNNs. We will then test their performance and show how convolutional neural networks written in both Theano and TensorFlow can outperform the accuracy of a plain neural network on the StreetView House Number dataset.

All the materials for this course are FREE. You can download and install Python, Numpy, Scipy, Theano, and TensorFlow with simple commands shown in previous courses.

This course focuses on "how to build and understand", not just "how to use". Anyone can learn to use an API in 15 minutes after reading some documentation. It's not about "remembering facts", it's about "seeing for yourself" via experimentation. It will teach you how to visualize what's happening in the model internally. If you want more than just a superficial look at machine learning models, this course is for you.

NOTES:

All the code for this course can be downloaded from my github: /lazyprogrammer/machine_learning_examples

In the directory: cnn_class

Make sure you always "git pull" so you have the latest version!

HARD PREREQUISITES / KNOWLEDGE YOU ARE ASSUMED TO HAVE:

• calculus
• linear algebra
• probability
• Python coding: if/else, loops, lists, dicts, sets
• Numpy coding: matrix and vector operations, loading a CSV file
• Can write a feedforward neural network in Theano and TensorFlow

TIPS (for getting through the course):

• Watch it at 2x.
• Take handwritten notes. This will drastically increase your ability to retain the information.
• Write down the equations. If you don't, I guarantee it will just look like gibberish.
• Ask lots of questions on the discussion board. The more the better!
• Realize that most exercises will take you days or weeks to complete.
• Write code yourself, don't just sit there and look at my code.

USEFUL COURSE ORDERING:

• (The Numpy Stack in Python)
• Linear Regression in Python
• Logistic Regression in Python
• (Supervised Machine Learning in Python)
• (Bayesian Machine Learning in Python: A/B Testing)
• Deep Learning in Python
• Practical Deep Learning in Theano and TensorFlow
• (Supervised Machine Learning in Python 2: Ensemble Methods)
• Convolutional Neural Networks in Python
• (Easy NLP)
• (Cluster Analysis and Unsupervised Machine Learning)
• Unsupervised Deep Learning
• (Hidden Markov Models)
• Recurrent Neural Networks in Python
• Artificial Intelligence: Reinforcement Learning in Python
• Natural Language Processing with Deep Learning in Python

Who is the target audience?

• Students and professional computer scientists
• Software engineers
• Data scientists who work on computer vision tasks
• Those who want to apply deep learning to images
• Those who want to expand their knowledge of deep learning past vanilla deep networks
• People who don't know what backpropagation is or how it works should not take this course, but instead, take parts 1 and 2.
• People who are not comfortable with Theano and TensorFlow basics should take part 2 before taking this course.

Description

*** NEW PREVIEW VIDEOS: Take a look at several newly enabled Preview videos. All lectures in Section 3 and Section 4 on Linear Regression are available for preview as well as Section 15 Integration objectives 

Note: AWS Machine Learning is not part of free-tier.  So, you will incur a small charge when creating and running prediction on models. For this course, I spent USD 5-6 total for creating and testing all models. ***

This course is designed to make you an expert in AWS Machine Learning and it teaches you how to convert your cool ideas into highly scalable products in a matter of days.

Biggest challenge for a Data Science professional is how to convert the proof-of-concept models into actual products that your customers can use. There are several courses on machine learning that teach you how to build models in R, Python, Matlab and so forth.  However, converting a model into a scalable solution and integrating with your existing application requires a lot of effort and development.  The real success of your ideas and concepts depends on how soon you can put the capabilities in the hands of your customers.

With AWS Machine Learning service, you can easily conduct experiments and test your concepts. Once you are happy, you can instantly scale to support millions of requests. No separate development work needed.

This course is focused on three aspects:

The Core of the machine learning process is the algorithm itself.  Gaining an intuitive understanding of the algorithm, how does it find the solution, and what are the knobs to tweak is essential for a successful career in this field.  That is where we will focus first.

Once we build the model, how do we know if it is good or bad? Or If we want to compare two different models, how do we decide which one to pick?  We will look at industry standard metrics and powerful visualization tools that AWS provides to assess the goodness of a model.

The third aspect and most exciting part of model development is putting the prediction capability in the hands of the users, validate how they are using it and identify what needs to be refined.  There is a whole section in this course dedicated to integration of machine learning models with your application.  We will walk thru several integration and security options.

This course is completely hands-on with examples using: AWS Web Console, Python Notebook Files, and Web clients built on AngularJS. You will also learn and integrate security into exercises using variety of AWS provided capabilities including Cognito.

There are Quizzes and supporting resources as well.

Who is the target audience?

• This course is designed for anyone who is interested in machine learning and data science
• If you are new to machine learning, this is a perfect course to upskill yourself and fastest way to learn machine learning
• If you are an experienced practitioner, you will gain insight into AWS Machine Learning capability and learn how you can convert your ideas into highly scalable solution in a matter of days
• AWS Certification - If you are preparing for certification, you will learn best practices and gain hands-on experience on securely deploying products using AWS Cloud

Description

Learn the latest Big Data Technology - Spark! And learn to use it with one of the most popular programming languages, Python!

One of the most valuable technology skills is the ability to analyze huge data sets, and this course is specifically designed to bring you up to speed on one of the best technologies for this task, Apache Spark! The top technology companies like Google, Facebook, Netflix, Airbnb, Amazon, NASA, and more are all using Spark to solve their big data problems!

Spark can perform up to 100x faster than Hadoop MapReduce, which has caused an explosion in demand for this skill! Because the Spark 2.0 DataFrame framework is so new, you now have the ability to quickly become one of the most knowledgeable people in the job market!

This course will teach the basics with a crash course in Python, continuing on to learning how to use Spark DataFrames with the latest Spark 2.0 syntax! Once we've done that we'll go through how to use the MLlib Machine Library with the DataFrame syntax and Spark. All along the way you'll have exercises and Mock Consulting Projects that put you right into a real world situation where you need to use your new skills to solve a real problem!

We also cover the latest Spark Technologies, like Spark SQL, Spark Streaming, and advanced models like Gradient Boosted Trees! After you complete this course you will feel comfortable putting Spark and PySpark on your resume! This course also has a full 30 day money back guarantee and comes with a LinkedIn Certificate of Completion!

If you're ready to jump into the world of Python, Spark, and Big Data, this is the course for you!

Who is the target audience?

• Someone who knows Python and would like to learn how to use it for Big Data
• Someone who is very familiar with another programming language and needs to learn Spark

Description

Data Scientists enjoy one of the top-paying jobs, with an average salary of $120,000 according to Glassdoor and Indeed. That's just the average! And it's not just about money - it's interesting work too!

If you've got some programming or scripting experience, this course will teach you the techniques used by real data scientists in the tech industry - and prepare you for a move into this hot career path. This comprehensive course includes 68 lectures spanning almost 9 hours of video, and most topics include hands-on Python code examples you can use for reference and for practice. I’ll draw on my 9 years of experience at Amazon and IMDb to guide you through what matters, and what doesn’t.

The topics in this course come from an analysis of real requirements in data scientist job listings from the biggest tech employers. We'll cover the machine learning and data mining techniques real employers are looking for, including:

• Regression analysis
• K-Means Clustering
• Principal Component Analysis
• Train/Test and cross validation
• Bayesian Methods
• Decision Trees and Random Forests
• Multivariate Regression
• Multi-Level Models
• Support Vector Machines
• Reinforcement Learning
• Collaborative Filtering
• K-Nearest Neighbor
• Bias/Variance Tradeoff
• Ensemble Learning
• Term Frequency / Inverse Document Frequency
• Experimental Design and A/B Tests

...and much more! There's also an entire section on machine learning with Apache Spark, which lets you scale up these techniques to "big data" analyzed on a computing cluster.

If you're new to Python, don't worry - the course starts with a crash course. If you've done some programming before, you should pick it up quickly. This course shows you how to get set up on Microsoft Windows-based PC's; the sample code will also run on MacOS or Linux desktop systems, but I can't provide OS-specific support for them.

Each concept is introduced in plain English, avoiding confusing mathematical notation and jargon. It’s then demonstrated using Python code you can experiment with and build upon, along with notes you can keep for future reference.

If you’re a programmer looking to switch into an exciting new career track, or a data analyst looking to make the transition into the tech industry – this course will teach you the basic techniques used by real-world industry data scientists. I think you'll enjoy it!

Who is the target audience?

• Software developers or programmers who want to transition into the lucrative data science career path will learn a lot from this course.
• Data analysts in the finance or other non-tech industries who want to transition into the tech industry can use this course to learn how to analyze data using code instead of tools. But, you'll need some prior experience in coding or scripting to be successful.
• If you have no prior coding or scripting experience, you should NOT take this course - yet. Go take an introductory Python course first.

Description

Welcome to the most comprehensive and complete bot developer course. Learn concepts, tools and techniques that you will need to build fully functional chatbots for business and enterprise.

Chatbots are computer programs that can interact with humans through a simple conversational  interface. They are designed to simulate an interaction with another human. Couple this with the fact that more than 90% of smartphone users spend most of their time in messaging apps such as Facebook Messenger and you have an excellent opportunity to build highly interactive chatbot based services that can empower business and enterprise like never before.

In this course, we will go from zero to pro as we build multiple chatbots using a variety of techniques and platforms. We will explore chatbot platforms that do not require you to write code, and all the way to code intensive chatbots that can be built for specialized scenarios. 

We will learn about the brain behind a chatbot, as we go from simple pattern recognition to natural language processing and AI.

This course features an ever evolving project based curricula that will see new sections, case studies and examples being added on a regular basis. This is critical because this technology space is growing by leaps & bounds and consequently this course aims to keep up with the pace. All of this comes with personalized help, hand holding and support.

You will need to be familiar with JavaScript and NodeJS to accomplish the coding projects in the course.

And even before reaching the end of the course, you will be able to build and deploy chatbots and offer this brand new way of reaching out to the world, to your customers and business. 

Chatbots can help people shop, order food, entertain, provide help, advice, information, support and more, through a simple chat interface. Imagine chatting with a friend on Facebook Messenger. No learning curve needed, no apps to install. As a matter of fact, when Facebook launched their Messenger platform in early 2016, they ushered in the era of chatbots. As a result, huge investments are being made in this space and it is poised to exponentially grow in the next few years. 

Almost all industries, ranging from entertainment, medicine, hospitality, performing arts, banking, aviation and more are already eyeing the chatbot space to enhance customer engagement for business and marketing. And it is no wonder that Google, Facebook and Microsoft are leading the pack with dedicated divisions and projects being incubated at the moment.

The bottom line is - we're at a stage in the evolution of chatbots where mobile apps were back in 2007 when Apple announced the first iPhone. Grasp the opportunity today.

Join me, as we understand, design and build incredible chatbots!

Who is the target audience?

• Web developers interested in building exciting conversational interfaces and agents
• Mobile developers who're keen on integrating conversational agents in their apps
• Business managers with a flair for coding in JavaScript & NodeJS
• Professionals who want to be at the cutting edge of technology
• You should not take this course if you're not a coder
• You should not take this course if you're not familiar with JavaScript and NodeJS

Description

Do you want to build a chatbot, so a bot that can talk? Yes a bot that can talk to your friends or customers or fans while you sleep or do something else. You can make one for your customer that keep on asking the same questions. Or if you have a community for your fans and followers that want to know your personal details. Use your imagination, any time you have to reply the same thing over and over again, someone else like a bot can do it for you. 

In the first part of the course we’ll make a chatbot without programming skills. We’ll build a ChatBot that can answer frequently asked questions and I'll show you how to teach your bot to have any other dialogs. We'll learn this by teaching our ChatBot to take job interviews. We’ll use API.AI to process natural language, that is understand what users want. Chat bot will communicate to it’s customers via the Facebook Messenger. 

And in the second part we’ll use Node.js to upgrade the bot. So the basic knowledge of javascript and Node.js is needed.

With the new app our bot will be able to remember things, that is store information into a database or connect to other API services. With this bot will gain external knowledge and functionality. 

And remember, I'LL BE THERE FOR YOU. I ANSWER EVERY QUESTION AND HELP EVERY STUDENT. 

At the end of the course you’ll have a fully functional ChatBot. And you’ll also know how to teach the bot to have other dialogs with customers. You’ll know how to make a bot for yourself and for other people. 

My name is Jana Bergant and I’m a developer with over 16 years of experience. I’m an IT instructor teaching people new tech skills.  Over 3660 people are already taking my course.

I help all my students at every step of development. And I'll be here for you!

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Last edit:

24.April: 

- Prebuilt agents

- Follow-up intents

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Do you want to catch the train in the new tech revolution, the bot revolution?

Chatbots represent one of the major trends in 2016. Some have even suggested they’ll eventually supplant our app-based ecosystem. 

App leaders like Facebook, who want to guarantee that people spend most of their time in their apps, are placing big bets on aggregating content and collecting bot integrations to keep users active and engaged.

So if the predictions turn right this will open up a new channel for businesses to reach a large audience. And here is a BIG OPPORTUNITY FOR YOU! Be one of the first people that know how to build chatbots. You can build it for your business or other people.

This course will show you how to create a ChatBot!

------------------------------------------------------------------------------------------------------

Edit history:

16. January:

- save and retrieve information from a chatbot to a database

- add quick replies to parameters

21.November: 

- we learn how to add rich messages to your Chatbot in API.AI. 

- we learn how to connect to 3rd party api, get data from it and use it in a chatbot

- I added extra resources for chatbot prototyping, chatbot analytics, chatbot developer platforms, chatbot stores, chatbot marketing, chatbot translators, chatbot customer service engines, chatbot job boards, chatbot magazines, chatbot newsletters, discussion forums, podcasts and conferences

11.September: added 2 part of the course. We upgrade the chatbot, add new features using Node.JS

------------------------------------------------------------------------------------------------------

So join me and let's build a chat bot together!

Who is the target audience?

• everybody that realizes the potential chatbots bring
• everybody who wan't a deeper knowledge of API.AI
• everybody who wants to build a chatbot for Messanger
• everybody who needs a bot to answer FAQ
• everybody who needs a bot to take job interviews
• everybody who need the bot to talk to customers or friend or fans or ...
• You should not take the course if you don't know what chatbot is. Take a peak at the first FREE videos first!
• for the second part of the course you need to know the basics of JavaScript and Node.js

Description

This course discusses the key technical ideas that form the foundation of modern commercial racing games. The emphasis is on the driving game physics and the artificial intelligence techniques that are used.  It's a fantastic course for video game developers working on racing game titles. It's also a perfect fit for gamers and enthusiasts interested in understanding how racing games and related simulations really work. Although the included sample games were built with Unity, the coverage in this course will not require any particular knowledge of the software. Follow-on training is available for students who wish to take their knowledge even further and actually build a racing game of their own!  

NOTE: The playable build and project source is no longer offered with this version of this course as the file has become too large for download from Udemy. To get the files please email me directly at joe@gameinstitute.com and I can send you a download link.   

Who is the target audience?

• This course is targeted first at both amateur and professional video game developers working on racing game titles. It is also perfect for gamers and enthusiasts interested in understanding how racing games and related simulations really work. Although the included sample game was built with Unity, the coverage will not require any particular knowledge of the software. More technically minded learners will probably find the material easier to understand, but most of it is very accessible to non-technical learners as well.

Description

If you sell your expertise for a living, you will discover significant benefits in transferring your knowledge to the world of Artificial Intelligence (AI). It is now possible to create an online chatbot with near human characteristics to deliver your intellectual property to the world.

From your website your clients will be able to interact with the AI system to receive a personalised experience of the way you deliver your specialist skills. The Chatbot will be able to track the client's progress and adapt the learning experience to suit their individual mood, personality and abilities.

Your unique talents will be instantly made available to a global audience 24 / 7.

Up until August 2016 the software to build a chatbot has been in prototype with major corporations but now it's going mainstream. This course outlines the basic concepts, taking IBM's offering as the template because it is currently the most well developed.

This is a very new field of commerce for the smaller business and there is a lot you need to be aware of before embarking on the journey. Armed with an understanding of what you will need to do personally and what help you will need from Software Developers will help you build the right partnerships. This will make it easier for you to negotiate contract terms at realistic market rates.

You will also learn what sorts of skills any software developer partners ought to have and how to recognise them in their CVs and past experience.

You will be sharing in my learning from beginning the process of transferring my coaching skills onto

an AI chatbot platform and we also consider what a legal practice would need to do to operate in this way.

1.  We look at the conversion of human language into machine language.
2.  Then we convert our expertise into a dynamic model by annotating our key documents.
3.  Consideration is given to the skills, qualifications and investment required to build a chatbot.
4.  Delivery of the system via an animated avatar is examined.
5.  Inspirations from early market entrants illustrates just what can be achieved.

At he product level we give  top level view of IBM's Watson Knowledge Studio and the Alchemy APIs. The Course is not aimed at Software Developers, although they may find it interesting, but at small independent business owners selling their expert skills. This probably covers all Udemy instructors.

My background in IT gives me an advantage in taking complex IT products and making them comprehensible to the business user. A conceptual understanding of the overall process will position you well to start making the transition from standard online delivery with all its limitations into a fully interactive chatbot experience.

                          

Who is the target audience?

• Businesses that sell specialist expertise, typically face to face, which have been transferring their skills to online knowledge sharing platforms
• Innovative business owners with reasonable IT skills but not yet at the Software Developer level
• Client groups would include coaches, teachers, therapists, mentors, language trainers, lawyers, accountants, medical advisers and all providers of online learning products

Description

This course is about the fundamental concepts of artificial intelligence. This topic is getting very hot nowadays because these learning algorithms can be used in several fields from software engineering to investment banking. Learning algorithms can recognize patterns which can help detect cancer for example. We may construct algorithms that can have a very very good guess about stocks movement in the market.

In the first chapter we are going to talk about the basic graph algorithms. Several advanced algorithms can be solved with the help of graphs, so as far as I am concerned these algorithms are the first steps.

Second chapter is about local search: finding minimum and maximum or global optimum in the main. These searches are used frequently when we use regression for example and want to find the parameters for the fit. We will consider basic concepts as well as the more advanced algorithms: heuristics and meta-heuristics.

The last topic will be about minimax algorithm and how to use these technique in games such as chess or tic-tac-toe, how to build and construct a game tree, how to analyze these kinds of tree like structures and so on. We will implement the tic-tac-toe game together in the end.

LAST UPDATE OF THE COURSE: 2016 october

Who is the target audience?

• This course is meant for students or anyone who interested in programming and have some background in basic Java

Description

PLEASE READ BEFORE ENROLLING: 

1.) IF YOU ARE A COMPLETE BEGINNER IN PYTHON-CHECK OUT MY OTHER COURSE "COMPLETE PYTHON BOOTCAMP"!

2.) THERE IS AN UPDATED VERSION OF THIS COURSE: 

"PYTHON FOR DATA SCIENCE AND MACHINE LEARNING BOOTCAMP" 

CLICK ON MY PROFILE TO FIND IT.

This course will give you the resources to learn python and effectively use it analyze and visualize data! Start your career in Data Science!

You'll get a full understanding of how to program with Python and how to use it in conjunction with scientific computing modules and libraries to analyze data.

You will also get lifetime access to over 100 example python code notebooks, new and updated videos, as well as future additions of various data analysis projects that you can use for a portfolio to show future employers!

By the end of this course you will:

- Have an understanding of how to program in Python.

- Know how to create and manipulate arrays using numpy and Python.

- Know how to use pandas to create and analyze data sets.

- Know how to use matplotlib and seaborn libraries to create beautiful data visualization.

- Have an amazing portfolio of example python data analysis projects!

- Have an understanding of Machine Learning and SciKit Learn!

With 100+ lectures and over 20 hours of information and more than 100 example python code notebooks, you will be excellently prepared for a future in data science!

Who is the target audience?

• Anyone interested in learning more about python, data science, or data visualizations.
• Anyone interested about the rapidly expanding world of data science!

Description

Welcome to the Python and Django Full Stack Web Developer Bootcamp! In this course we cover everything you need to know to build a website using Python, Django, and many more web technologies!

Whether you want to change career paths, expand your current skill set, start your own entrepreneurial business, become a consultant, or just want to learn, this is the course for you!

We will teach you the latest technologies for building great web applications with Python 3 and Django 1.11 (the latest released at this time)! But we don't just teach that, we also teach the Front End technologies you need to know, including HTML, CSS, and Javascript. This course can be your one stop shop for everything you need! It will serve as a useful reference for many of your questions as you begin your journey in becoming a web developer!

This course is designed so that anyone can learn how to become a web developer. We teach you how to program by using HD Video Lectures, Walkthrough Code Projects, Exercises, Concept Presentation Slides, Downloadable Code Notes, Reading Assignments, and much more! 

Here is just a small sampling of the topics included in this course:

• HTML5
• CSS3
• Javascript
• jQuery
• Bootstrap 3 and 4
• Document Object Model
• Python
• Django Basics
• Django Templates
• Django Forms
• Django Admin Customization
• ORM
• Class Based Views
• REST APIs
• User Authentication
• and much,much more!

You will also get access to our online community of thousands of students, happy to help you out with any questions you may have! Any questions, feel free to send me a message here on Udemy and connect with me on LinkedIn, check out my profile for other courses.

We also have a 30-day money back guarantee, so you can try out the course for an entire month, risk-free!

Always keep learning!

Jose

Who is the target audience?

• Complete Beginners
• Professionals looking to bridge gaps in their knowledge
• Python Developers looking to get into Web Development

Description

This is not just another Python tutorial that shows how to write Python code. This is a carefully designed course that will train you to develop real life applications with Python.

Through a combination of videos, real world code examples, quizzes, exercises, and a final project, this course makes sure you are able to think Python, and design and build real world applications by the end of it. After you buy the course, you will have lifetime access to it and to the course cheat sheet ebook containing all the code consumed throughout the course. You can use that book for quick look-up of Python commands.

The course is designed for all student levels. The first 5% of the course teaches Python basics for beginners and can serve as a refresher crash course for post-beginner students. After completing the first 5%, you will be guided in building 10 real world applications in a wide range of areas that include:

• Web applications 
• Desktop applications 
• Database applications 
• Web scraping 
• Web mapping 
• Data analysis
• Interactive web visualization
• Computer vision for image and video processing
• Object Oriented Programming

By the end of the course you will have built 10 useful applications in the above areas.

The applications you are going to build are as follows:

• A name generator 
• A website URL timed blocker 
• A web map generator
• A portfolio website with Flask 
• A GUI-based desktop application 
• A webcam motion detector
• A web scraper of property
• An interactive web-based financial chart
• A data collector web application 
• A geocoding web service.

Who is the target audience?

• All student levels - the course starts from zero and progresses to advanced level.

Description

Want to program networks using Python, but not sure where to start? Well, this course will show you how you can start start programming Cisco networks within 20 minutes.

NOTE: You will also get access to my new Python Network Programability course launching in July with your purchase of this course. Learn even more about network automation!

This course is practical. I won't talk about programming in abstract terms and make you wait before you can start automating networks. I will show you how you can quickly and easily start network programming by using GNS3, Cisco IOS and Python.

You will see demonstrations of the configuration of both Cisco routers and switches in GNS3. For example, how to configure multiple VLANs on a multiple switches, or how to configure OSPF on a router and more.

This course shows you practical examples of using Python to programmatically configure Cisco network devices rather then just talking about it.

The days of configuring Cisco networks only with the command line interface (CLI) are drawing to a close. You need to add network programmability using Python and APIs to your skill set.

Learn how to program Cisco networks using:

- Telnet

- SSH

- Paramiko

- Netmiko

- Loops

- Cisco best practices

Start programming Cisco networks today!

Who is the target audience?

• Network Engineers
• Network Architects

Description

Become a Python Programmer!

This is the most comprehensive, yet straight-forward, course for the Python programming language on Udemy! Whether you have never programmed before, already know basic syntax, or want to learn about the advanced features of Python, this course is for you! In this course we will teach you both versions of Python (2 and 3) so you can easily adapt your skill set to either version!

With over 100 lectures and more than 10 hours of video this comprehensive course leaves no stone unturned! This course includes quizzes, tests, and homework assignments as well as 3 major projects to create a Python project portfolio!

This course will teach you Python in a practical manner, with every lecture comes a full coding screencast and a corresponding code notebook! Learn in whatever manner is best for you!

You will get lifetime access to over 100 lectures plus corresponding Notebooks for the lectures!

This course comes with a 30 day money back guarantee! If you are not satisfied in any way, you'll get your money back. Plus you will keep access to the Notebooks as a thank you for trying out the course!

So what are you waiting for? Learn Python in a way that will advance your career and increase your knowledge, all in a fun and practical way!

Who is the target audience?

• Beginners who have never programmed before.
• Programmers switching languages to Python.
• Intermediate Python programmers who want to level up their skills!

Description

Prerequisites: No prerequisites, knowledge of some undergraduate level mathematics would help but is not mandatory. Working knowledge of Python would be helpful if you want to run the source code that is provided.

Taught by a Stanford-educated, ex-Googler and an IIT, IIM - educated ex-Flipkart lead analyst. This team has decades of practical experience in quant trading, analytics and e-commerce.

This course is a down-to-earth, shy but confident take on machine learning techniques that you can put to work today

Let’s parse that.

The course is down-to-earth : it makes everything as simple as possible - but not simpler

The course is shy but confident : It is authoritative, drawn from decades of practical experience -but shies away from needlessly complicating stuff.

You can put ML to work today : If Machine Learning is a car, this car will have you driving today. It won't tell you what the carburetor is.

The course is very visual : most of the techniques are explained with the help of animations to help you understand better.

This course is practical as well : There are hundreds of lines of source code with comments that can be used directly to implement natural language processing and machine learning for text summarization, text classification in Python.

The course is also quirky. The examples are irreverent. Lots of little touches: repetition, zooming out so we remember the big picture, active learning with plenty of quizzes. There’s also a peppy soundtrack, and art - all shown by studies to improve cognition and recall.

What's Covered:

Machine Learning:

Supervised/Unsupervised learning, Classification, Clustering, Association Detection, Anomaly Detection, Dimensionality Reduction, Regression.

Naive Bayes, K-nearest neighbours, Support Vector Machines, Artificial Neural Networks, K-means, Hierarchical clustering, Principal Components Analysis, Linear regression, Logistics regression, Random variables, Bayes theorem, Bias-variance tradeoff

Natural Language Processing with Python:

Corpora, stopwords, sentence and word parsing, auto-summarization, sentiment analysis (as a special case of classification), TF-IDF, Document Distance, Text summarization, Text classification with Naive Bayes and K-Nearest Neighbours and Clustering with K-Means

Sentiment Analysis: 

Why it's useful, Approaches to solving - Rule-Based , ML-Based , Training , Feature Extraction, Sentiment Lexicons, Regular Expressions, Twitter API, Sentiment Analysis of Tweets with Python

Mitigating Overfitting with Ensemble Learning:

Decision trees and decision tree learning, Overfitting in decision trees, Techniques to mitigate overfitting (cross validation, regularization), Ensemble learning and Random forests

Recommendations:  Content based filtering, Collaborative filtering and Association Rules learning

Get started with Deep learning: Apply Multi-layer perceptrons to the MNIST Digit recognition problem

A Note on Python: The code-alongs in this class all use Python 2.7. Source code (with copious amounts of comments) is attached as a resource with all the code-alongs. The source code has been provided for both Python 2 and Python 3 wherever possible.

Using discussion forums

Please use the discussion forums on this course to engage with other students and to help each other out. Unfortunately, much as we would like to, it is not possible for us at Loonycorn to respond to individual questions from students:-(

We're super small and self-funded with only 2-3 people developing technical video content. Our mission is to make high-quality courses available at super low prices.

The only way to keep our prices this low is to *NOT offer additional technical support over email or in-person*. The truth is, direct support is hugely expensive and just does not scale.

We understand that this is not ideal and that a lot of students might benefit from this additional support. Hiring resources for additional support would make our offering much more expensive, thus defeating our original purpose.

It is a hard trade-off.

Thank you for your patience and understanding!

Who is the target audience?

• Yep! Analytics professionals, modelers, big data professionals who haven't had exposure to machine learning
• Yep! Engineers who want to understand or learn machine learning and apply it to problems they are solving
• Yep! Product managers who want to have intelligent conversations with data scientists and engineers about machine learning
• Yep! Tech executives and investors who are interested in big data, machine learning or natural language processing
• Yep! MBA graduates or business professionals who are looking to move to a heavily quantitative role

Description

Taught by a 4 person team including 2 Stanford-educated, ex-Googlers  and 2 ex-Flipkart Lead Analysts. This team has decades of practical experience in working with Java and with billions of rows of data. 

Get your data to fly using Spark for analytics, machine learning and data science 

Let’s parse that.

What's Spark? If you are an analyst or a data scientist, you're used to having multiple systems for working with data. SQL, Python, R, Java, etc. With Spark, you have a single engine where you can explore and play with large amounts of data, run machine learning algorithms and then use the same system to productionize your code.

Analytics: Using Spark and Python you can analyze and explore your data in an interactive environment with fast feedback. The course will show how to leverage the power of RDDs and Dataframes to manipulate data with ease. 

Machine Learning and Data Science : Spark's core functionality and built-in libraries make it easy to implement complex algorithms like Recommendations with very few lines of code. We'll cover a variety of datasets and algorithms including PageRank, MapReduce and Graph datasets. 

What's Covered:

Lot's of cool stuff ..

• Music Recommendations using Alternating Least Squares and the Audioscrobbler dataset
• Dataframes and Spark SQL to work with Twitter data
• Using the PageRank algorithm with Google web graph dataset
• Using Spark Streaming for stream processing 
• Working with graph data using the  Marvel Social network dataset 

.. and of course all the Spark basic and advanced features: 

• Resilient Distributed Datasets, Transformations (map, filter, flatMap), Actions (reduce, aggregate) 
• Pair RDDs , reduceByKey, combineByKey 
• Broadcast and Accumulator variables 
• Spark for MapReduce 
• The Java API for Spark 
• Spark SQL, Spark Streaming, MLlib and GraphFrames (GraphX for Python) 

Using discussion forums

Please use the discussion forums on this course to engage with other students and to help each other out. Unfortunately, much as we would like to, it is not possible for us at Loonycorn to respond to individual questions from students:-(

We're super small and self-funded with only 2-3 people developing technical video content. Our mission is to make high-quality courses available at super low prices.

The only way to keep our prices this low is to *NOT offer additional technical support over email or in-person*. The truth is, direct support is hugely expensive and just does not scale.

We understand that this is not ideal and that a lot of students might benefit from this additional support. Hiring resources for additional support would make our offering much more expensive, thus defeating our original purpose.

It is a hard trade-off.

Thank you for your patience and understanding!

Who is the target audience?

• Yep! Analysts who want to leverage Spark for analyzing interesting datasets
• Yep! Data Scientists who want a single engine for analyzing and modelling data as well as productionizing it.
• Yep! Engineers who want to use a distributed computing engine for batch or stream processing or both

Description

The Hidden Markov Model or HMM is all about learning sequences.

A lot of the data that would be very useful for us to model is in sequences. Stock prices are sequences of prices. Language is a sequence of words. Credit scoring involves sequences of borrowing and repaying money, and we can use those sequences to predict whether or not you’re going to default. In short, sequences are everywhere, and being able to analyze them is an important skill in your data science toolbox.

The easiest way to appreciate the kind of information you get from a sequence is to consider what you are reading right now. If I had written the previous sentence backwards, it wouldn’t make much sense to you, even though it contained all the same words. So order is important.

While the current fad in deep learning is to use recurrent neural networks to model sequences, I want to first introduce you guys to a machine learning algorithm that has been around for several decades now - the Hidden Markov Model.

This course follows directly from my first course in Unsupervised Machine Learning for Cluster Analysis, where you learned how to measure the probability distribution of a random variable. In this course, you’ll learn to measure the probability distribution of a sequence of random variables.

You guys know how much I love deep learning, so there is a little twist in this course. We’ve already covered gradient descent and you know how central it is for solving deep learning problems. I claimed that gradient descent could be used to optimize any objective function. In this course I will show you how you can use gradient descent to solve for the optimal parameters of an HMM, as an alternative to the popular expectation-maximization algorithm.

We’re going to do it in Theano and Tensorflow, which are popular libraries for deep learning. This is also going to teach you how to work with sequences in Theano and Tensorflow, which will be very useful when we cover recurrent neural networks and LSTMs.

This course is also going to go through the many practical applications of Markov models and hidden Markov models. We’re going to look at a model of sickness and health, and calculate how to predict how long you’ll stay sick, if you get sick. We’re going to talk about how Markov models can be used to analyze how people interact with your website, and fix problem areas like high bounce rate, which could be affecting your SEO. We’ll build language models that can be used to identify a writer and even generate text - imagine a machine doing your writing for you. HMMs have been very successful in natural language processing or NLP.

We’ll look at what is possibly the most recent and prolific application of Markov models - Google’s PageRank algorithm. And finally we’ll discuss even more practical applications of Markov models, including generating images, smartphone autosuggestions, and using HMMs to answer one of the most fundamental questions in biology - how is DNA, the code of life, translated into physical or behavioral attributes of an organism?

All of the materials of this course can be downloaded and installed for FREE. We will do most of our work in Numpy and Matplotlib, along with a little bit of Theano. I am always available to answer your questions and help you along your data science journey.

This course focuses on "how to build and understand", not just "how to use". Anyone can learn to use an API in 15 minutes after reading some documentation. It's not about "remembering facts", it's about "seeing for yourself" via experimentation. It will teach you how to visualize what's happening in the model internally. If you want more than just a superficial look at machine learning models, this course is for you.

See you in class!

NOTES:

All the code for this course can be downloaded from my github: /lazyprogrammer/machine_learning_examples

In the directory: hmm_class

Make sure you always "git pull" so you have the latest version!

HARD PREREQUISITES / KNOWLEDGE YOU ARE ASSUMED TO HAVE:

• calculus
• linear algebra
• probability
• Be comfortable with the multivariate Gaussian distribution
• Python coding: if/else, loops, lists, dicts, sets
• Numpy coding: matrix and vector operations, loading a CSV file
• Cluster Analysis and Unsupervised Machine Learning in Python will provide you with sufficient background

TIPS (for getting through the course):

• Watch it at 2x.
• Take handwritten notes. This will drastically increase your ability to retain the information.
• Write down the equations. If you don't, I guarantee it will just look like gibberish.
• Ask lots of questions on the discussion board. The more the better!
• Realize that most exercises will take you days or weeks to complete.
• Write code yourself, don't just sit there and look at my code.

USEFUL COURSE ORDERING:

• (The Numpy Stack in Python)
• Linear Regression in Python
• Logistic Regression in Python
• (Supervised Machine Learning in Python)
• (Bayesian Machine Learning in Python: A/B Testing)
• Deep Learning in Python
• Practical Deep Learning in Theano and TensorFlow
• (Supervised Machine Learning in Python 2: Ensemble Methods)
• Convolutional Neural Networks in Python
• (Easy NLP)
• (Cluster Analysis and Unsupervised Machine Learning)
• Unsupervised Deep Learning
• (Hidden Markov Models)
• Recurrent Neural Networks in Python
• Artificial Intelligence: Reinforcement Learning in Python
• Natural Language Processing with Deep Learning in Python

Who is the target audience?

• Students and professionals who do data analysis, especially on sequence data
• Professionals who want to optimize their website experience
• Students who want to strengthen their machine learning knowledge and practical skillset
• Students and professionals interested in DNA analysis and gene expression
• Students and professionals interested in modeling language and generating text from a model