Hello friends!


The Black Friday 2021 sale is on! I’m sending you links now which will give you the maximum possible discount during the Black Friday / Cyber Monday season (see below for specific dates).

For those students who are new (welcome!), you may not know that I have a whole catalog of machine learning and AI courses built up and continuously updated over the past 6 years, with separate in-depth courses covering nearly every topic in the field, including:

– Machine Learning (Linear Regression, Logistic Regression, K-Means Clustering, GMMs, Hierarchical Clustering, HMMs, Markov Models, Decision Trees, Random Forests, Naive Bayes, Perceptrons, SVMs, boosting, Bayesian ML, A/B Testing)

– NLP (Attention, seq2seq, BiLSTMs, word2vec, GloVe, article spinning, latent semantic indexing, sentiment analysis, spam detection)

– Reinforcement Learning (the basics, along with modern Deep RL topics like DQN, Policy Gradient Methods, Evolution Strategies, A2C and A3C, DDPG)

– Deep Learning (separate courses for ANNs, CNNs, RNNs, GANs, Variational Autoencoders, Recommender Systems, Computer Vision)

BOOKMARK THIS POST because these links will give you the best discount possible over the Black Friday / Cyber Monday sales later this month.

The “Black Friday” links will work on the following dates:

– Friday November 26, 2021 (Black Friday)

– All week before Black Friday (starting November 19)

– Monday November 29, 2021 (Cyber Monday)

– Maybe a few days after that, but don’t wait and regret it



Outline of the following discounts:

1. Time Series Analysis, Forecasting, and Machine Learning (VIP)

2. Financial Engineering and Artificial Intelligence in Python (VIP)

3. PyTorch: Deep Learning and Artificial Intelligence (VIP)

4. Artificial Intelligence: Reinforcement Learning in Python (VIP)

5. Black Friday Discounts for Other Courses

Note that the “VIP links” will work for the next 30 days, you don’t need to wait until Black Friday for those.

Time Series Analysis, Forecasting, and Machine Learning (VIP PROMOTION)

==The Complete Time Series Analysis Course Has Arrived==


(note: this VIP coupon expires in 30 days!)

We will cover techniques such as:

  • ETS and Exponential Smoothing
  • Holt’s Linear Trend Model
  • Holt-Winters Model
  • ACF and PACF
  • Vector Autoregression and Moving Average Models (VAR, VMA, VARMA)
  • Machine Learning Models (including Logistic Regression, Support Vector Machines, and Random Forests)
  • Deep Learning Models (Artificial Neural Networks, Convolutional Neural Networks, and Recurrent Neural Networks)
  • GRUs and LSTMs for Time Series Forecasting

We will cover applications such as:

  • Time series forecasting of sales data
  • Time series forecasting of stock prices and stock returns
  • Time series classification of smartphone data to predict user behavior

The VIP version (obtained by purchasing the course NOW during the VIP period) of the course covers even more exciting topics, such as:

  • AWS Forecast (Amazon’s state-of-the-art low-code forecasting API)
  • GARCH (financial volatility modeling)
  • FB Prophet (Facebook’s time series library)


Financial Engineering and Artificial Intelligence in Python

VIP Promotion

==The complete Financial Engineering course has arrived==


This is a MASSIVE (20 hours) Financial Engineering course covering the core fundamentals of financial engineering and financial analysis from scratch. We will go in-depth into all the classic topics, such as:

  • Exploratory data analysis, significance testing, correlations, alpha and beta
  • Time series analysis, simple moving average, exponentially-weighted moving average
  • Holt-Winters exponential smoothing model
  • Efficient Market Hypothesis
  • Random Walk Hypothesis
  • Time series forecasting (“stock price prediction”)
  • Modern portfolio theory
  • Efficient frontier / Markowitz bullet
  • Mean-variance optimization
  • Maximizing the Sharpe ratio
  • Convex optimization with Linear Programming and Quadratic Programming
  • Capital Asset Pricing Model (CAPM)
  • Algorithmic trading

In addition, we will look at various non-traditional techniques which stem purely from the field of machine learning and artificial intelligence, such as:

  • Regression models
  • Classification models
  • Unsupervised learning
  • Reinforcement learning and Q-learning

List of VIP content:

  • Classic Algorithmic Trading – Trend Following Strategy
  • Machine Learning-Based Trading Strategy
  • Reinforcement Learning-Based (Q-Learning) Trading Strategy
  • Statistical Factor Models
  • Regime Detection and Volatility Clustering with HMMs and Sequence Modeling


PyTorch: Deep Learning and Artificial Intelligence

VIP Promotion

=====The complete PyTorch course has arrived=====


This is a MASSIVE (over 22 hours) Deep Learning course covering EVERYTHING from scratch. That includes:

  • Machine learning basics (linear neurons)
  • ANNs, CNNs, and RNNs for images and sequence data
  • Time series forecasting and stock predictions (+ why all those fake data scientists are doing it wrong)
  • NLP (natural language processing)
  • Recommender systems
  • Transfer learning for computer vision
  • GANs (generative adversarial networks)
  • Deep reinforcement learning and applying it by building a stock trading bot
  • VIP only: Making predictions with your trained NLP model
  • VIP only: Making predictions with your trained Recommender model
  • VIP only: Modeling prediction uncertainty and heteroskedasticity (i.e. stock returns!)
  • VIP only: Facial recognition with Siamese Networks


Artificial Intelligence: Reinforcement Learning in Python

VIP Promotion

===The complete Reinforcement Learning course has arrived===


Reinforcement Learning is the most general form of AI we know of so far – some speculate it is the way forward to mimic animal intelligence and attain “AGI” (artificial general intelligence).

This course covers:

  • The explore-exploit dilemma and the Bayesian bandit method
  • MDPs (Markov Decision Processes)
  • Dynamic Programming solution for MDPs
  • Monte Carlo Method
  • Temporal Difference Method (including Q-Learning)
  • Approximation Methods using RBF Neural Networks
  • Applying your code to OpenAI Gym with zero effort / code changes
  • Building a stock trading bot (different approach in each course!)


Tensorflow 2: Deep Learning and Artificial Intelligence VIP

Exclusive to deeplearningcourses.com only

===The complete Tensorflow 2 course has arrived===

Get it here: https://deeplearningcourses.com/c/deep-learning-tensorflow-2



Looking for the LOWEST PRICE POSSIBLE Udemy Coupons?


Please enjoy the below Black Friday coupons for the rest of my courses on Udemy.

The best part is, you don’t have to enter any coupon code at all. Simply clicking on the links below will automatically get you the best possible price.

*Note: a few of the courses below, marked with an asterisk (*) are not part of the Black Friday sale. However, if you purchase these courses at the current price, you will receive, upon request, complimentary access to the full VIP version of the course on deeplearningcourses.com. Just email me at [email protected] for free access with proof of purchase.


  • Machine Learning and Neurons
  • ANNs, CNNs, RNNs
  • NLP (Natural Language Processing)
  • GANs
  • Recommender Systems
  • Deep Reinforcement Learning (build a “stock trading bot”)
  • Transfer Learning for Computer Vision



  • Deep Reinforcement Learning algorithms such as A2C, Evolution Strategies, and DDPG

https://www.udemy.com/course/support-vector-machines-in-python/?referralCode=8EDBF7E0BD5AF7C1545D (*)

  • Support Vector Machines (SVMs) in-depth starting from linear classification theory to the maximum margin method, kernel trick, quadratic programming, and the SMO (sequential minimal optimization) algorithm


  • Reddit and Hacker News algorithms
  • PageRank (what Google Search uses)
  • Bayesian / Thompson sampling
  • Collaborative filtering
  • Matrix factorization
  • We use the 20 million ratings dataset, not the puny 100k dataset everyone else uses
  • Implementing matrix factorization with Deep Learning
  • Using Deep Neural Networks for recommenders
  • Autoencoders for recommenders
  • Restricted Boltzmann Machines (RBMs) for recommenders
  • Recommenders with big data (PySpark) on AWS cluster


  • Modern Deep NLP techniques such as Bidirectional LSTMs, CNNs for text classification, seq2seq, attention, and memory networks


  • Deep Learning techniques for computer vision, such as state-of-the-art networks (VGG, ResNet, Inception)
  • Train state-of-the-art models fast with transfer learning
  • Object detection with SSD
  • Neural style transfer

www.udemy.com/course/deep-learning-gans-and-variational-autoencoders/?referralCode=A7980E2D769910C847F9 (*)

  • Generate realistic, high quality images with deep neural networks
  • Apply game theory and Bayesian machine learning to deep learning
  • Learn about the “transpose convolution”

www.udemy.com/course/deep-reinforcement-learning-in-python/?referralCode=1FE6DB1ECC128417A7F1 (*)

  • Learn how we got from classical reinforcement learning to deep reinforcement learning and why it’s nontrivial
  • Play OpenAI Gym environments such as CartPole and Atari
  • Learn the “tricks” of DQN and A3C and how they improve classical RL approaches


  • Learn about the most fundamental of machine learning algorithms: linear regression
  • Believe it or not, this gets you MOST of the way there to understanding deep learning



  • After learning about linear regression, see how a similar model (logistic regression) can be used for classification
  • Importantly, understand how and why this is a model of the “neuron” (and because of that, we can use it to build neural networks)


  • Learn IN-DEPTH the theory behind artificial neural networks (ANNs)
  • This is THE fundamental course for understanding what deep learning is doing, from ANNs to CNNs to RNNs to GANs and beyond


  • Learn how to apply machine learning to NLP tasks, such as: spam detection, sentiment analysis, article spinning, and latent semantic analysis
  • Learn how to preprocess text for use in a ML algorithm
  • Learn about the classic NLTK library


  • Learn how we went from the fundamental ANNs to many of the key technologies we use today, such as:
  • Batch / stochastic gradient descent instead of full gradient descent
  • (Nesterov) momentum, RMSprop, Adam, and other adaptive learning rate techniques
  • Dropout regularization
  • Batch normalization
  • Learn how deep learning is accelerated by GPUs (and how to set one up yourself)
  • Learn how deep learning libraries improve the development process with GPUs (faster training) and automatic differentiation (so you don’t have to write the code or derive the math yourself)



  • Learn the fundamentals of the SQL language and how to apply it to data
  • Practice for job interviews by going through several interview-style questions



  • Go from ANNs to CNNs
  • Learn about the all important “convolution” operation in-depth
  • Implement convolution yourself (no other course does this!)
  • Design principles for CNNs and why they specialize to work with images




  • Learn how Deep Learning handles sequences of data (like DNA, text processing, etc.)
  • Learn the limitations of a naive (simple) RNN
  • How to extend / improve RNNs with GRUs and LSTMs
  • Build GRUs and LSTMs by yourself (not just calling some library function)


www.udemy.com/course/natural-language-processing-with-deep-learning-in-python/?referralCode=78A60E6BD16F3A656EA7 (*)

  • Apply deep learning to natural language processing (NLP)
  • Covers the famous word2vec and GloVe algorithms
  • See how RNNs apply to text problems
  • Learn about a neural network structured like a “tree” which we call recursive neural networks and a more powerful version: recursive neural tensor networks (RNTNs)


www.udemy.com/course/data-science-supervised-machine-learning-in-python/?referralCode=14513C7EEDFDF1EBD49F (*)

  • Covers classic machine learning algorithms which EVERY student of machine learning should know (AND be able to implement)
  • K-Nearest Neighbor (KNN), Naive Bayes and non-Naive Bayes Classifiers, the Perceptron, and Decision Trees
  • Learn how to build a machine learning web service using Python server frameworks



  • Learn how Bayesian machine learning differs from traditional machine learning
  • We focus mostly on “comparing” multiple things (i.e. A/B Testing)
  • Learn why traditional (frequentist) A/B Testing is limited
  • Learn about adaptive approaches to “choosing the best item”


www.udemy.com/course/machine-learning-in-python-random-forest-adaboost/?referralCode=0210246BE75FD01DDF5F (*)

  • Learn how combining multiple machine learning models is better than just one
  • Covers fundamental ensemble approaches such as Random Forest and AdaBoost
  • Learn/derive the famous “bias-variance tradeoff” (most people can only discuss it at a high level, you will learn what it really means)
  • Learn about the difference between the “bagging” and “boosting” approaches



Remember, this is a very rare sale (only once per year!). If there’s anything you want or if you are on the fence and think you might be interested, get it NOW so that you don’t miss out!