What Is Machine Learning?
Why Use Machine Learning?
Types of Machine Learning Systems
Supervised/Unsupervised Learning
Batch and Online Learning
Instance-Based Versus Model-Based Learning
Main Challenges of Machine Learning
Insufficient Quantity of Training Data
Nonrepresentative Training Data
Poor-Quality Data
Irrelevant Features
Overfitting the Training Data
Underfitting the Training Data
Stepping Back
Testing and Validating
Hyperparameter Tuning and Model Selection
Data Mismatch

Working with Real Data
Look at the Big Picture
Frame the Problem
Select a Performance Measure
Check the Assumptions
Get the Data
Create the Workspace
Download the Data
Take a Quick Look at the Data Structure
Create a Test Set
Discover and Visualize the Data to Gain Insights
Visualizing Geographical Data
Looking for Correlations
Experimenting with Attribute Combinations
Prepare the Data for Machine Learning Algorithms
Data Cleaning
Handling Text and Categorical Attributes
Custom Transformers
Feature Scaling
Transformation Pipelines
Select and Train a Model
Training and Evaluating on the Training Set
Better Evaluation Using Cross-Validation
Fine-Tune Your Model
Grid Search
Randomized Search
Ensemble Methods
Analyze the Best Models and Their Errors
Evaluate Your System on the Test Set
Launch, Monitor, and Maintain Your System

MNIST
Training a Binary Classifier
Performance Measures
Measuring Accuracy Using Cross-Validation
Confusion Matrix
Precision and Recall
Precision/Recall Tradeoff
The ROC Curve
Multiclass Classification
Error Analysis
Multilabel Classification
Multioutput Classification

Linear Regression
The Normal Equation
Computational Complexity
Gradient Descent
Batch Gradient Descent
Stochastic Gradient Descent
Mini-batch Gradient Descent
Polynomial Regression
Learning Curves
Regularized Linear Models
Ridge Regression
Lasso Regression
Elastic Net
Early Stopping
Logistic Regression
Estimating Probabilities
Training and Cost Function
Decision Boundaries
Softmax Regression

Linear SVM Classification
Soft Margin Classification
Nonlinear SVM Classification
Polynomial Kernel
Adding Similarity Features
Gaussian RBF Kernel
Computational Complexity
SVM Regression
Under the Hood
Decision Function and Predictions
Training Objective
Quadratic Programming
The Dual Problem
Kernelized SVM
Online SVMs

Training and Visualizing a Decision Tree
Making Predictions
Estimating Class Probabilities
The CART Training Algorithm
Computational Complexity
Gini Impurity or Entropy?
Regularization Hyperparameters
Regression
Instability

Voting Classifiers
Bagging and Pasting
Bagging and Pasting in Scikit-Learn
Out-of-Bag Evaluation
Random Patches and Random Subspaces
Random Forests
Extra-Trees
Feature Importance
Boosting
AdaBoost
Gradient Boosting
Stacking

The Curse of Dimensionality
Main Approaches for Dimensionality Reduction
Projection
Manifold Learning
PCA
Preserving the Variance
Principal Components
Projecting Down to d Dimensions
Using Scikit-Learn
Explained Variance Ratio
Choosing the Right Number of Dimensions
PCA for Compression
Randomized PCA
Incremental PCA
Kernel PCA
Selecting a Kernel and Tuning Hyperparameters
LLE
Other Dimensionality Reduction Techniques

Clustering
K-Means
Limits of K-Means
Using clustering for image segmentation
Using Clustering for Preprocessing
Using Clustering for Semi-Supervised Learning
DBSCAN
Other Clustering Algorithms
Gaussian Mixtures
Anomaly Detection using Gaussian Mixtures
Selecting the Number of Clusters
Bayesian Gaussian Mixture Models
Other Anomaly Detection and Novelty Detection Algorithms

From Biological to Artificial Neurons
Biological Neurons
Logical Computations with Neurons
The Perceptron
Multi-Layer Perceptron and Backpropagation
Regression MLPs
Classification MLPs
Implementing MLPs with Keras
Installing TensorFlow 2
Building an Image Classifier Using the Sequential API
Building a Regression MLP Using the Sequential API
Building Complex Models Using the Functional API
Building Dynamic Models Using the Subclassing API
Saving and Restoring a Model
Using Callbacks
Visualization Using TensorBoard
Fine-Tuning Neural Network Hyperparameters
Number of Hidden Layers
Number of Neurons per Hidden Layer
Learning Rate, Batch Size and Other Hyperparameters

Vanishing/Exploding Gradients Problems
Glorot and He Initialization
Nonsaturating Activation Functions
Batch Normalization
Gradient Clipping
Reusing Pretrained Layers
Transfer Learning With Keras
Unsupervised Pretraining
Pretraining on an Auxiliary Task
Faster Optimizers
Momentum Optimization
Nesterov Accelerated Gradient
AdaGrad
RMSProp
Adam and Nadam Optimization
Learning Rate Scheduling
Avoiding Overfitting Through Regularization
ℓ1 and ℓ2 Regularization
Dropout
Monte-Carlo (MC) Dropout
Max-Norm Regularization
Summary and Practical Guidelines

A Quick Tour of TensorFlow
Using TensorFlow like NumPy
Tensors and Operations
Tensors and NumPy
Type Conversions
Variables
Other Data Structures
Customizing Models and Training Algorithms
Custom Loss Functions
Saving and Loading Models That Contain Custom Components
Custom Activation Functions, Initializers, Regularizers, and Constraints
Custom Metrics
Custom Layers
Custom Models
Losses and Metrics Based on Model Internals
Computing Gradients Using Autodiff
Custom Training Loops
TensorFlow Functions and Graphs
Autograph and Tracing
TF Function Rules

The Data API
Chaining Transformations
Shuffling the Data
Preprocessing the Data
Putting Everything Together
Prefetching
Using the Dataset With tf.keras
The TFRecord Format
Compressed TFRecord Files
A Brief Introduction to Protocol Buffers
TensorFlow Protobufs
Loading and Parsing Examples
Handling Lists of Lists Using the SequenceExample Protobuf
The Features API
Categorical Features
Crossed Categorical Features
Encoding Categorical Features Using One-Hot Vectors
Encoding Categorical Features Using Embeddings
Using Feature Columns for Parsing
Using Feature Columns in Your Models
TF Transform
The TensorFlow Datasets (TFDS) Project

The Architecture of the Visual Cortex
Convolutional Layer
Filters
Stacking Multiple Feature Maps
TensorFlow Implementation
Memory Requirements
Pooling Layer
TensorFlow Implementation
CNN Architectures
LeNet-5
AlexNet
GoogLeNet
VGGNet
ResNet
Xception
SENet
Implementing a ResNet-34 CNN Using Keras
Using Pretrained Models From Keras
Pretrained Models for Transfer Learning
Classification and Localization
Object Detection
Fully Convolutional Networks (FCNs)
You Only Look Once (YOLO)
Semantic Segmentation

Predict Housing Price
Stock Price Prediction
Online Assignment Plagiarism Checker
Personality Prediction System via CV Analysis
Breast Cancer Detection
Undersea Sonar Bomb Detection
Handwriting Recognition
Face recognition
Chatbot
YouTube Comment Spam Detection
11. Face Filter

Secrets of Success
SUpercharged CVs
Technical & HR Interview Preperation
Speaking & giving Presentations on AI
Freelancing / Entrepreneurship