Machine Learning in TensorFlow/Keras Fundamentals

Course description:

The Tensorflow/Keras course for beginners is designed for users who want to dive into the realms of Artificial Intelligence. It naturally comes as a follower of the Basic Data Science in Python course.

Prerequisites:

Data Science in Python Fundamentals

Nice to have:

• Basic Linear Algebra
– Vectors
– Vectorial spaces
– Matrix operations
– Factorizations
• Basic Probabilities and Statistics
– Histograms, Heatmaps
– Variance, Covariance, Correlation
– Distributions: Gaussian, Poisson
– Bayes Theory

Course module structure

Module 1: Introduction
– What is ML?
– Where can I find it in real life?
– Why now?
– What are the three main categories of ML?
• Supervised learning
• Unsupervised learning
• Reinforcement learning (demo)
– ML pipeline

Module 2: Machine Learning with sci-kit
– ML pipeline review
– Scikit Python Library
– Data representation
• Feature matrix
• Target array
• Iris dataset example
– Estimator API
– Linear Regression
• Simple Linear Regression
• Model Evaluation
• Polynomial Regression
– Selecting the best model
– The bias-variance trade-off
– Logistic Regression
• Who survives the Titanic?
– Naive Bayes
• Gaussian Naive Bayes
• Multinomial Naive Bayes
• Categorical Naive Bayes
– k Nearest Neighbours
– k-Means Clustering
– Dimensionality reduction
• Principal Components Analysis (PCA)
• Singular Value Decomposition (SVD)
– Decision Trees
– Random Forests
Hands-on Lab:
– Playing around with different values affecting the bias and the variance, calculating precision, recall, F1 and F2-scores, comparing different models on the training and testing accuracies
– Doing a little bit of data preprocessing, analyzing the difference between categorical and numerical data, plotting some relevant statistical values and visually inspecting the correlation between features

Module 3: Neural Networks in Tensorflow/Keras
– Artificial Neural Networks (ANNs)
• Neurons
• Layers
• Activation Functions
• More vocabulary
– Popular Frameworks
– Keras
– Linear Regression
• Defining Models in Keras
• Training and predicting
– Fashion MNIST example
Hands-on Lab:
– Creating our first custom neural network model
– Choosing the number of layers and the number of neurons per layer
– Tweaking the learning rate
– Training the neural network on real world data

Module 4: Convolutional Neural Networks (peek)
– Motivation behind CNNs
– CNN Building blocks
• Convolution Layers
• Pooling Layers
– CNNs in Keras
– Data Augmentation
– Architectures

Module 5: NLP using Deep Learning
– Spam detector
– Sentiment analyzer
– Autocomplete

Module 6: Reinforcement Learning
– Frozen Lake demo
– Flappy Bird demo

Module 7: Recommender Systems
– Data preparation
– Cosine distance
– SVD for recommender systems
– Autoencoder demo