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BLACK FRIDAY / CYBER MONDAY 2019 — Deep Learning and Artificial Intelligence in Python

November 28, 2019

Yearly Black Friday sale is HERE! As I always tell my students – you never know when Udemy’s next “sale drought” is going to be – so if you are on the fence about getting a course, NOW is the time.

NOTE: If you are looking for the Tensorflow 2.0 VIP materials, as of now they can only be purchased here: https://deeplearningcourses.com/c/deep-learning-tensorflow-2 (coupon code automatically applied). The site contains only the VIP materials, and the main part of the course can be purchased on Udemy as per the link below. Therefore, if you want the “full” version of the course, each part now must be purchased separately.

 

tf2

https://www.udemy.com/course/deep-learning-tensorflow-2/

  • What you’ll learn:
    • Neurons and Machine Learning
    • ANNs
    • CNNs
    • RNNs
    • GANs
    • NLP
    • Recommender Systems
    • Reinforcement Learning
    • build a stock trading bot with Deep RL
    • Low-level and advanced Tensorflow 2.0 features
    • Exporting models for Tensorflow Lite
    • Tensorflow Serving

rl3

https://www.udemy.com/course/cutting-edge-artificial-intelligence/

  • What you’ll learn: A2C, Evolution Strategies, and DDPG

svm

https://www.udemy.com/course/support-vector-machines-in-python/

  • What you’ll learn: 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

rec

https://www.udemy.com/course/recommender-systems/

  • What you’ll learn:
    • 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

nlp3

https://www.udemy.com/course/deep-learning-advanced-nlp/

  • What you’ll learn:
    • modern Deep NLP techniques such as Bidirectional LSTMs
    • CNNs for text classification
    • seq2seq
    • attention
    • memory networks

cv

https://www.udemy.com/course/advanced-computer-vision/

  • What you’ll learn:
    • 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

gan

https://www.udemy.com/course/deep-learning-gans-and-variational-autoencoders/

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

deeprl

https://www.udemy.com/course/deep-reinforcement-learning-in-python/

  • What you’ll learn:
    • 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

rl

https://www.udemy.com/course/artificial-intelligence-reinforcement-learning-in-python/

  • What you’ll learn:
    • Learn what makes Reinforcement Learning special compared to basic supervised/unsupervised learning (hint: it’s very complicated!)
    • Learn how epsilon-greedy and Bayesian machine learning can optimize click-through rates
    • Implement a tic-tac-toe agent
    • MDPs (Markov Decision Processes) and the Bellman equation
    • Learn the 3 approaches to RL: Dynamic Programming, Monte Carlo, and Temporal Difference (which includes the famous Q-Learning algorithm)

lin

https://www.udemy.com/course/data-science-linear-regression-in-python/

  • What you’ll learn:
    • 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

log

https://www.udemy.com/course/data-science-logistic-regression-in-python/

  • What you’ll learn:
    • 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)

deep1

https://www.udemy.com/course/data-science-deep-learning-in-python/

  • What you’ll learn:
    • 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

nlp

https://www.udemy.com/course/data-science-natural-language-processing-in-python/

  • What you’ll learn:
    • 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

deep2

https://www.udemy.com/course/data-science-deep-learning-in-theano-tensorflow/

  • What you’ll learn:
    • 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)

sql

https://www.udemy.com/course/sql-for-marketers-data-analytics-data-science-big-data/

  • What you’ll learn:
    • 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

cnn

https://www.udemy.com/course/deep-learning-convolutional-neural-networks-theano-tensorflow/

  • What you’ll learn:
    • 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

cluster

https://www.udemy.com/course/cluster-analysis-unsupervised-machine-learning-python/

  • What you’ll learn:
    • Learn about classic clustering methods such as K-Means, Hierarchical Clustering, and Gaussian Mixture Models (a probabilistic approach to Cluster Analysis)
    • Apply clustering to real-world datasets such as organizing books, clustering Hillary Clinton and Donald Trump tweets, and DNA

udeep

https://www.udemy.com/course/unsupervised-deep-learning-in-python/

  • What you’ll learn:
    • Learn about how Deep Learning an be applied to data without labels/targets using Autoencoders and RBMs (Restricted Boltzmann Machines)
    • Learn how Autoencoders are like a “nonlinear” version of PCA
    • Visualize / transform data with PCA and t-SNE
    • Apply RBMs to recommender systems

hmm

https://www.udemy.com/course/unsupervised-machine-learning-hidden-markov-models-in-python/

  • What you’ll learn:
    • Learn how unsupervised learning extends to cover sequences of data (like DNA, text processing, etc.)
    • The HMM is a probabilistic graphical model and uses the same learning approach (expectation-maximization) as k-means clustering and GMMs
    • We also review Markov models and you’ll see how they (surprisingly) apply to a famous modern algorithm: Google’s PageRank

rnn

https://www.udemy.com/course/deep-learning-recurrent-neural-networks-in-python/

  • What you’ll learn:
    • 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)

deepnlp

https://www.udemy.com/course/natural-language-processing-with-deep-learning-in-python/

  • What you’ll learn:
    • 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)

super

https://www.udemy.com/course/data-science-supervised-machine-learning-in-python/

  • What you’ll learn:
    • 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

bayes

https://www.udemy.com/course/bayesian-machine-learning-in-python-ab-testing/

  • What you’ll learn:
    • 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”

ensemble

https://www.udemy.com/course/machine-learning-in-python-random-forest-adaboost/

  • What you’ll learn:
    • 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