95% off Data Science: Deep Learning in Python (Coupon)

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Coupon & course info

Course Name: Data Science: Deep Learning in Python

Subtitle: A guide for writing your own neural network in Python and Numpy, and how to do it in Google’s TensorFlow.

Instructor: Taught by Justin C, Data scientist and big data engineer

Category: Business

Subcategory: Data & Analytics

Provided by: Udemy

Price: $49 (before discount)

Free coupon code: See above (no charge for coupon)

Review info & popularity

As of February 19, 2016…

Students: 462 students enrolled

Ratings: 19 reviews

Rank: ranked #71 in Udemy Business Courses

Brief course description

This course will get you started in building your FIRST artificial neural network. Following my previous course on logistic regression, we take this basic building block, and build full-on non-linear neural networks right out of the gate using Python and Numpy. All the materials for this course are FREE.

We extend the previous binary classification model to K classes using the softmax function, and we derive the very important training method called “backpropagation” using first principles. I show you how to code backpropagation in Numpy, first “the slow way”, and then “the fast way” using Numpy features.

Next, we implement a neural network using Google’s new TensorFlow library.

(Read more about this course on the official course page.)

Justin C bio

I am a data scientist, big data engineer, and full stack software engineer.

(Learn more about this instructor on the official course page.)

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Final details for this Udemy course

Languages: English

Skill level: Intermediate Level

Lectures: 13 lessons

Duration: 1.5 Hours of video

What you get: Code a neural network from scratch in Python and numpy

Target audience: Students interested in machine learning – you’ll get all the tidbits you need to do well in a neural networks course

Requirements: How to take partial derivatives and log-likelihoods (ex. finding the maximum likelihood estimations for a die)

Access: Lifetime access

Peace of mind: 30 day money back guarantee

Availability: available online, as well as on iOS and Android

Download options: check course to see if you can download lessons

95% off Easy Natural Language Processing in Python (Coupon)

Easy Natural Language Processing in Python - Udemy Coupon

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Coupon & course info

Course Name: Easy Natural Language Processing in Python

Subtitle: A-Z guide to practical NLP: spam detection, sentiment analysis, article spinners, and latent semantic analysis.

Instructor: Taught by Justin C, Data scientist and big data engineer

Category: Business

Subcategory: Data & Analytics

Provided by: Udemy

Price: $49 (before discount)

Free coupon code: See above (no charge for coupon)

Review info & popularity

As of March 9, 2016…

Students: 22077 students enrolled

Ratings: 12 reviews

Rank: ranked #167 in Udemy Business Courses

Brief course description

In this course you will build MULTIPLE practical systems using natural language processing, or NLP. This course is not part of my deep learning series, so there are no mathematical prerequisites – just straight up coding in Python. All the materials for this course are FREE.

After a brief discussion about what NLP is and what it can do, we will begin building very useful stuff. The first thing we’ll build is a spam detector. You likely get very little spam these days, compared to say, the early 2000s, because of systems like these.

Next we’ll build a model for sentiment analysis in Python. This is something that allows us to assign a score to a block of text that tells us how positive or negative it is. People have used sentiment analysis on Twitter to predict the stock market.

(Read more about this course on the official course page.)

Justin C bio

I am a data scientist, big data engineer, and full stack software engineer.

(Learn more about this instructor on the official course page.)

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Final details for this Udemy course

Languages: English

Skill level: All Levels

Lectures: 18 lessons

Duration: 1.5 Hours of video

What you get: Write your own spam detection code in Python

Target audience: Students who are comfortable writing Python code, using loops, lists, dictionaries, etc.

Requirements: Install Python, it’s free!

Access: Lifetime access

Peace of mind: 30 day money back guarantee

Availability: available online, as well as on iOS and Android

Download options: check course to see if you can download lessons

95% off Deep Learning Prerequisites: Logistic Regression in Python (Coupon)

Deep Learning Prerequisites: Logistic Regression in Python - Udemy Coupon

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Coupon & course info

Course Name: Deep Learning Prerequisites: Logistic Regression in Python

Subtitle: Data science techniques for professionals and students – learn the theory behind logistic regression and code in Python

Instructor: Taught by Justin C, Data scientist and big data engineer

Category: Academics

Subcategory: Math & Science

Provided by: Udemy

Price: $30 (before discount)

Free coupon code: See above (no charge for coupon)

Review info & popularity

As of July 20, 2016…

Students: 2133 students enrolled

Ratings: 109 reviews

Rank: ranked #9 in Udemy Academics Courses

Brief course description

This course is a lead-in to deep learning and neural networks – it covers a popular and fundamental technique used in machine learning, data science and statistics: logistic regression. We cover the theory from the ground up: derivation of the solution, and applications to real-world problems. We show you how one might code their own logistic regression module in Python.

This course does not require any external materials. Everything needed (Python, and some Python libraries) can be obtained for free.

This course provides you with many practical examples so that you can really see how deep learning can be used on anything. Throughout the course, we’ll do a course project, which will show you how to predict user actions on a website given user data like whether or not that user is on a mobile device, the number of products they viewed, how long they stayed on your site, whether or not they are a returning visitor, and what time of day they visited.

(Read more about this course on the official course page.)

Justin C bio

I am a data scientist, big data engineer, and full stack software engineer.

(Learn more about this instructor on the official course page.)

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Final details for this Udemy course

Languages: English

Skill level: All Levels

Lectures: 31 lessons

Duration: 3 hours of video

What you get: program logistic regression from scratch in Python

Target audience: Adult learners who want to get into the field of data science and big data

Requirements: You should know how to take a derivative

Access: Lifetime access

Peace of mind: 30 day money back guarantee

Availability: available online, as well as on iOS and Android

Download options: check course to see if you can download lessons

95% off Deep Learning Prerequisites: Linear Regression in Python (Coupon)

Deep Learning Prerequisites: Linear Regression in Python - Udemy Coupon

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Coupon & course info

Course Name: Deep Learning Prerequisites: Linear Regression in Python

Subtitle: Data science: Learn linear regression from scratch and build your own working program in Python for data analysis.

Instructor: Taught by Justin C, Data scientist and big data engineer

Category: Academics

Subcategory: Math & Science

Provided by: Udemy

Price: $30 (before discount)

Free coupon code: See above (no charge for coupon)

Review info & popularity

As of July 20, 2016…

Students: 3009 students enrolled

Ratings: 115 reviews

Rank: ranked #10 in Udemy Academics Courses

Brief course description

This course teaches you about one popular technique used in machine learning, data science and statistics: linear regression. We cover the theory from the ground up: derivation of the solution, and applications to real-world problems. We show you how one might code their own linear regression module in Python.

This course does not require any external materials. Everything needed (Python, and some Python libraries) can be obtained for free.

If you are a programmer and you want to enhance your coding abilities by learning about data science, then this course is for you. If you have a technical or mathematical background, and you want to know how to apply your skills as a software engineer or “hacker”, this course may be useful.

(Read more about this course on the official course page.)

Justin C bio

I am a data scientist, big data engineer, and full stack software engineer.

(Learn more about this instructor on the official course page.)

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Work through this comprehensive calculus 1 course, from Precalculus to Applications of Derivatives.

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Final details for this Udemy course

Languages: English

Skill level: All Levels

Lectures: 19 lessons

Duration: 2 hours of video

What you get: Derive and solve a linear regression model, and apply it appropriately to data science problems

Target audience: People who are interested in data science, machine learning, statistics and artificial intelligence

Requirements: How to take a derivative using calculus

Access: Lifetime access

Peace of mind: 30 day money back guarantee

Availability: available online, as well as on iOS and Android

Download options: check course to see if you can download lessons

95% off Natural Language Processing with Deep Learning in Python (Coupon)

Natural Language Processing with Deep Learning in Python - Udemy Coupon

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Coupon & course info

Course Name: Natural Language Processing with Deep Learning in Python

Subtitle: Complete guide on deriving and implementing word2vec, GLoVe, word embeddings, and sentiment analysis with recursive nets

Instructor: Taught by Justin C, Data scientist and big data engineer

Category: Business

Subcategory: Data & Analytics

Provided by: Udemy

Price: $120 (before discount)

Free coupon code: See above (no charge for coupon)

Review info & popularity

As of September 8, 2016…

Students: 528 students enrolled

Ratings: 13 reviews

Rank: ranked #41d in Udemy Business Courses

Brief course 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.

(Read more about this course on the official course page.)

Justin C bio

I am a data scientist, big data engineer, and full stack software engineer.

(Learn more about this instructor on the official course page.)

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Final details for this Udemy course

Languages: English

Skill level: Expert Level

Lectures: 40 lessons

Duration: 4.5 hours of video

What you get: Understand and implement word2vec

Target audience: Students and professionals who want to create word vector representations for various NLP tasks

Requirements: Install Numpy, Matplotlib, Sci-Kit Learn, Theano, and TensorFlow (should be extremely easy by now)

Access: Lifetime access

Peace of mind: 30 day money back guarantee

Availability: available online, as well as on iOS and Android

Download options: check course to see if you can download lessons

95% off Deep Learning: Recurrent Neural Networks in Python (Coupon)

Deep Learning: Recurrent Neural Networks in Python - Udemy Coupon

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Coupon & course info

Course Name: Deep Learning: Recurrent Neural Networks in Python

Subtitle: GRU, LSTM, + more modern deep learning, machine learning, and data science for sequences

Instructor: Taught by Justin C, Data scientist and big data engineer

Category: Business

Subcategory: Data & Analytics

Provided by: Udemy

Price: $120 (before discount)

Free coupon code: See above (no charge for coupon)

Review info & popularity

As of September 8, 2016…

Students: 563 students enrolled

Ratings: 20 reviews

Rank: ranked #65d in Udemy Business Courses

Brief course description

Like the course I just released on Hidden Markov Models, Recurrent Neural Networks are all about learning sequences – but whereas Markov Models are limited by the Markov assumption, Recurrent Neural Networks are not – and as a result, they are more expressive, and more powerful than anything we’ve seen on tasks that we haven’t made progress on in decades.

So what’s going to be in this course and how will it build on the previous neural network courses and Hidden Markov Models?

In the first section of the course we are going to add the concept of time to our neural networks.

(Read more about this course on the official course page.)

Justin C bio

I am a data scientist, big data engineer, and full stack software engineer.

(Learn more about this instructor on the official course page.)

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Final details for this Udemy course

Languages: English

Skill level: All Levels

Lectures: 32 lessons

Duration: 4 hours of video

What you get: Understand the simple recurrent unit (Elman unit)

Target audience: If you want to level up with deep learning, take this course.

Requirements: Calculus

Access: Lifetime access

Peace of mind: 30 day money back guarantee

Availability: available online, as well as on iOS and Android

Download options: check course to see if you can download lessons

95% off Unsupervised Machine Learning Hidden Markov Models in Python (Coupon)

Unsupervised Machine Learning Hidden Markov Models in Python - Udemy Coupon

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Bonus: download a free guide that reveals 11 tricks for getting the biggest discounts on Udemy courses, including this course.

Coupon & course info

Course Name: Unsupervised Machine Learning Hidden Markov Models in Python

Subtitle: HMMs for stock price analysis, language modeling, web analytics, biology, and PageRank.

Instructor: Taught by Justin C, Data scientist and big data engineer

Category: Business

Subcategory: Data & Analytics

Provided by: Udemy

Price: $120 (before discount)

Free coupon code: See above (no charge for coupon)

Review info & popularity

As of September 8, 2016…

Students: 614 students enrolled

Ratings: 51 reviews

Rank: ranked #73d in Udemy Business Courses

Brief course 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.

(Read more about this course on the official course page.)

Justin C bio

I am a data scientist, big data engineer, and full stack software engineer.

(Learn more about this instructor on the official course page.)

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5. Skillshare Success # 1: Skillshare Newbies – Your Magic ID

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Final details for this Udemy course

Languages: English

Skill level: All Levels

Lectures: 40 lessons

Duration: 4.5 hours of video

What you get: Understand and enumerate the various applications of Markov Models and Hidden Markov Models

Target audience: Students and professionals who do data analysis, especially on sequence data

Requirements: Familiarity with probability and statistics

Access: Lifetime access

Peace of mind: 30 day money back guarantee

Availability: available online, as well as on iOS and Android

Download options: check course to see if you can download lessons

95% off Deep Learning: Convolutional Neural Networks in Python (Coupon)

Deep Learning: Convolutional Neural Networks in Python - Udemy Coupon

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Coupon & course info

Course Name: Deep Learning: Convolutional Neural Networks in Python

Subtitle: Computer Vision and Data Science and Machine Learning combined! In Theano and TensorFlow

Instructor: Taught by Justin C, Data scientist and big data engineer

Category: Business

Subcategory: Data & Analytics

Provided by: Udemy

Price: $120 (before discount)

Free coupon code: See above (no charge for coupon)

Review info & popularity

As of September 8, 2016…

Students: 701 students enrolled

Ratings: 52 reviews

Rank: ranked #160d in Udemy Business Courses

Brief course 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!

(Read more about this course on the official course page.)

Justin C bio

I am a data scientist, big data engineer, and full stack software engineer.

(Learn more about this instructor on the official course page.)

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Final details for this Udemy course

Languages: English

Skill level: Expert Level

Lectures: 24 lessons

Duration: 3 hours of video

What you get: Understand convolution

Target audience: Students and professional computer scientists

Requirements: Install Python, Numpy, Scipy, Matplotlib, Scikit Learn, Theano, and TensorFlow

Access: Lifetime access

Peace of mind: 30 day money back guarantee

Availability: available online, as well as on iOS and Android

Download options: check course to see if you can download lessons

95% off Unsupervised Deep Learning in Python (Coupon)

Unsupervised Deep Learning in Python - Udemy Coupon

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Bonus: download a free guide that reveals 11 tricks for getting the biggest discounts on Udemy courses, including this course.

Coupon & course info

Course Name: Unsupervised Deep Learning in Python

Subtitle: Autoencoders + Restricted Boltzmann Machines for Deep Neural Networks in Theano, + t-SNE and PCA

Instructor: Taught by Justin C, Data scientist and big data engineer

Category: Business

Subcategory: Data & Analytics

Provided by: Udemy

Price: $120 (before discount)

Free coupon code: See above (no charge for coupon)

Review info & popularity

As of September 8, 2016…

Students: 537 students enrolled

Ratings: 20 reviews

Rank: ranked #167d in Udemy Business Courses

Brief course description

This course is the next logical step in my deep learning, data science, and machine learning series. I’ve done a lot of courses about deep learning, and I just released a course about unsupervised learning, where I talked about clustering and density estimation. So what do you get when you put these 2 together? Unsupervised deep learning!

In these course we’ll start with some very basic stuff – principal components analysis (PCA), and a popular nonlinear dimensionality reduction technique known as t-SNE (t-distributed stochastic neighbor embedding).

Next, we’ll look at a special type of unsupervised neural network called the autoencoder. After describing how an autoencoder works, I’ll show you how you can link a bunch of them together to form a deep stack of autoencoders, that leads to better performance of a supervised deep neural network. Autoencoders are like a non-linear form of PCA.

(Read more about this course on the official course page.)

Justin C bio

I am a data scientist, big data engineer, and full stack software engineer.

(Learn more about this instructor on the official course page.)

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Final details for this Udemy course

Languages: English

Skill level: Intermediate Level

Lectures: 30 lessons

Duration: 3 hours of video

What you get: Understand the theory behind principal components analysis (PCA)

Target audience: Students and professionals looking to enhance their deep learning repertoire

Requirements: Knowledge of calculus and linear algebra

Access: Lifetime access

Peace of mind: 30 day money back guarantee

Availability: available online, as well as on iOS and Android

Download options: check course to see if you can download lessons

95% off Data Science: Practical Deep Learning in Theano + TensorFlow (Coupon)

Data Science: Practical Deep Learning in Theano + TensorFlow - Udemy Coupon

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Bonus: download a free guide that reveals 11 tricks for getting the biggest discounts on Udemy courses, including this course.

Coupon & course info

Course Name: Data Science: Practical Deep Learning in Theano + TensorFlow

Subtitle: Take deep learning to the next level with SGD, Nesterov momentum, RMSprop, Theano, TensorFlow, and using the GPU on AWS.

Instructor: Taught by Justin C, Data scientist and big data engineer

Category: Business

Subcategory: Data & Analytics

Provided by: Udemy

Price: $120 (before discount)

Free coupon code: See above (no charge for coupon)

Review info & popularity

As of September 8, 2016…

Students: 853 students enrolled

Ratings: 46 reviews

Rank: ranked #173d in Udemy Business Courses

Brief course description

This course continues where my first course, Deep Learning in Python, left off. You already know how to build an artificial neural network in Python, and you have a plug-and-play script that you can use for TensorFlow. Neural networks are one of the staples of machine learning, and they are always a top contender in Kaggle contests. If you want to improve your skills with neural networks and deep learning, this is the course for you.

You already learned about backpropagation (and because of that, this course contains basically NO MATH), but there were a lot of unanswered questions. How can you modify it to improve training speed? In this course you will learn about batch and stochastic gradient descent, two commonly used techniques that allow you to train on just a small sample of the data at each iteration, greatly speeding up training time.

You will also learn about momentum, which can be helpful for carrying you through local minima and prevent you from having to be too conservative with your learning rate. You will also learn about adaptive learning rate techniques like AdaGrad and RMSprop which can also help speed up your training.

(Read more about this course on the official course page.)

Justin C bio

I am a data scientist, big data engineer, and full stack software engineer.

(Learn more about this instructor on the official course page.)

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Final details for this Udemy course

Languages: English

Skill level: All Levels

Lectures: 23 lessons

Duration: 3 hours of video

What you get: Apply momentum to backpropagation to train neural networks

Target audience: Students and professionals who want to deepen their machine learning knowledge

Requirements: Be comfortable with Python, Numpy, and Matplotlib. Install Theano and TensorFlow.

Access: Lifetime access

Peace of mind: 30 day money back guarantee

Availability: available online, as well as on iOS and Android

Download options: check course to see if you can download lessons