Hands-on Machine Learning with Scikit-learn and TensorFlow 2

Course Curriculum

Chapter 1: Installing Scikit-Learn and TensorFlow 2.0

Lecture 1: Course Overview

Lecture 2: Overview of the Anaconda Distribution

Lecture 3: Installing the Anaconda Distribution for Scikit-Learn

Lecture 4: Installing TensorFlow 2.0 from the Anaconda Distribution

Lecture 5: Install Scikit-Learn and Tensorflow 2.0 Manually Through pip

Chapter 2: ML Fundamentals: Scikit-Learn Introduction

Lecture 1: What Is Machine Learning?

Lecture 2: First Scikit-Learn Model

Lecture 3: Overfitting and Regularization

Lecture 4: Probability and Statistics Review

Lecture 5: Probability Distribution and Metrics

Chapter 3: Applied Scikit-Learn: Supervised Learning Models

Lecture 1: Supervised Learning and KNN

Lecture 2: Logistic Regression

Lecture 3: Naïve Bayes

Lecture 4: Support Vector Machines

Lecture 5: Decision Trees

Lecture 6: Ensemble Methods

Chapter 4: Unsupervised Learning

Lecture 1: K-means and Hierarchical Clustering

Lecture 2: Connectivity and Density Clustering

Lecture 3: Gaussian Mixture Models

Lecture 4: Variational Bayesian Gaussian Mixture Models

Lecture 5: Decomposing Signals into Components

Lecture 6: Signal Decomposition with Factor and Independent Component Analysis

Lecture 7: Novelty Detection

Lecture 8: Outlier Detection

Lecture 9: Locally Linear Embedded Manifolds

Lecture 10: Multi-Dimensional Scaling and t-SNE Manifolds

Lecture 11: Density Estimation

Lecture 12: Restricted Boltzmann Machine

Chapter 5: TensorFlow 2.0 Essentials for ML

Lecture 1: TensorFlow 2.0 Overview

Lecture 2: TensorFlow 2.0’s Gradient Tape

Lecture 3: Working with Neural Networks and Keras

Lecture 4: Keras Customization

Lecture 5: Custom Networks in Keras

Lecture 6: Core Neural Network Concepts

Lecture 7: Regression and Transfer Learning

Lecture 8: TensorFlow Estimators and TensorBoard

Chapter 6: Applied Deep Learning for Computer Vision Tasks

Lecture 1: Introduction to ConvNets

Lecture 2: ConvNets In Keras

Lecture 3: Image Classification with Data Augmentation

Lecture 4: Convolutional Autoencoders

Lecture 5: Denoising and Variational Autoencoders

Lecture 6: Custom Generative Adversarial Networks

Lecture 7: Semantic Segmentation

Lecture 8: Neural Style Transfer

Chapter 7: Natural Language Processing and Sequential Data

Lecture 1: Using Word Embeddings

Lecture 2: Text Pipeline with Tokenization for Classification

Lecture 3: Sequential Data with Recurrent Neural Networks

Lecture 4: Best Practices with Recurrent Neural Networks

Lecture 5: Time Series Forecasting

Lecture 6: Forecasting with CNNs and RNNs

Chapter 8: Applied Sequence to Sequence and Transformer Models

Lecture 1: NLP Language Models

Lecture 2: Generating Text from an LSTM

Lecture 3: Sequence to Sequence Models

Lecture 4: MT Seq2Seq with Attention

Lecture 5: NLP Transformers

Lecture 6: Training Transformers and NLP In Practice

Chapter 9: Working with Reinforcement Learning

Lecture 1: Basics of Reinforcement Learning

Lecture 2: Training a Deep Q-Network with TF-Agents

Lecture 3: TF-agents In Depth

Lecture 4: Value and Policy Based Methods

Lecture 5: Exploration Techniques and Uncertainty In RL

Lecture 6: Imitation Learning and AlphaZero