Free ebookMachine Learning and Deep Learning with Python

Introduction to Machine Learning

Python Fundamentals for Data Science

Configuring the Development Environment

Data Manipulation with Pandas

Exploratory Data Analysis with Matplotlib and Seaborn

Exploratory Data Analysis with Matplotlib and Seaborn: Importing libraries (Matplotlib and Seaborn)

Exploratory Data Analysis with Matplotlib and Seaborn: Initial data loading and inspection

Exploratory Data Analysis with Matplotlib and Seaborn: Data cleaning and preparation

Exploratory Data Analysis with Matplotlib and Seaborn: Univariate analysis (distribution of a single variable)

Exploratory Data Analysis with Matplotlib and Seaborn: Bivariate analysis (relationships between two variables)

Exploratory Data Analysis with Matplotlib and Seaborn: Visualizing categorical data

Exploratory Data Analysis with Matplotlib and Seaborn: Visualizing Continuous Data

Exploratory Data Analysis with Matplotlib and Seaborn: Use of histograms, boxplots and scatter plots

Exploratory Data Analysis with Matplotlib and Seaborn: Creating Line Plots for Time Series

Exploratory Data Analysis with Matplotlib and Seaborn: Customizing graphs (colors, titles, labels)

Exploratory Data Analysis with Matplotlib and Seaborn: Correlation and heatmap analysis

Exploratory Data Analysis with Matplotlib and Seaborn: Using pairplots to visualize relationships in multiple dimensions

Exploratory Data Analysis with Matplotlib and Seaborn: Save visualizations to files (PNG, JPG, etc.)

Exploratory Data Analysis with Matplotlib and Seaborn: Interpretation and conclusions from visualizations

Basic Statistical Concepts for Machine Learning

Principles of Supervised Learning

Supervised Learning Principles: Definition of Supervised Learning

Supervised Learning Principles: Datasets: Training and Testing

Supervised Learning Principles: Classification Algorithms

Supervised Learning Principles: Regression Algorithms

Supervised Learning Principles: Performance Assessment Metrics

Supervised Learning Principles: Cross Validation

Supervised Learning Principles: Overfitting and Underfitting

Supervised Learning Principles: Regularization

Supervised Learning Principles: Model Selection

Supervised Learning Principles: Hyperparameter Optimization

Supervised Learning Principles: Feature Engineering

Supervised Learning Principles: Class Balancing

Supervised Learning Principles: Model Interpretability

Supervised Learning Principles: Practical Applications

SIMPLE and Multiple Linear Regression Models

Cross Validation and Assessment Metrics

Classification Models: Decision Trees and K-NN

Evaluation of Classification Models

Ensemble Learning: Bagging, Boosting and Random Forest

Hyperparameter Optimization

Dimensionality Reduction and Principal Component Analysis (PCA)

Clustering Algorithms: K-Means and Hierarchical Clustering

Introduction to Deep Learning and Artificial Neural Networks

Neuron Concepts and Activation Functions

Backpropagation and Training of Neural Networks

Backpropagation and Neural Network Training: What is Backpropagation

Backpropagation and Neural Network Training: Gradient Calculation

Backpropagation and Training of Neural Networks: Derivation Rule Chain

Backpropagation and Neural Network Training: Weights Update with Gradient Descent

Backpropagation and Training of Neural Networks: Activation Functions

Backpropagation and Training of Neural Networks: Learning Rate

Backpropagation and Training of Neural Networks: Momentum and Other Optimization Methods

Backpropagation and Training of Neural Networks: Vanishing and Exploding Gradient Problems

Backpropagation and Neural Network Training: Weights Initialization

Backpropagation and Training of Neural Networks: Regularization (L1, L2, Dropout)

Backpropagation and Neural Network Training: Batch Normalization

Backpropagation and Neural Network Training: Deep Neural Network Training

Backpropagation and Training of Neural Networks: Overfitting and Underfitting

Backpropagation and Training of Neural Networks: Cross Validation

Backpropagation and Training of Neural Networks: Data Augmentation Techniques

Backpropagation and Training of Neural Networks: Transfer Learning

Backpropagation and Training of Neural Networks: Recurrent Neural Networks (RNN) and Backpropagation Through Time (BPTT)

Backpropagation and Training of Neural Networks: Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU)

Backpropagation and Training of Neural Networks: Deep Learning Frameworks (TensorFlow, PyTorch, Keras)

Optimizers and Regularization Strategies

Building Neural Networks with Keras and TensorFlow

Building Neural Networks with Keras and TensorFlow: Introduction to TensorFlow and Keras

Building Neural Networks with Keras and TensorFlow: Installation and environment configuration

Building Neural Networks with Keras and TensorFlow: Fundamentals of Artificial Neural Networks

Building Neural Networks with Keras and TensorFlow: Building sequential models in Keras

Building Neural Networks with Keras and TensorFlow: Working with dense, convolutional and recurrent layers

Building Neural Networks with Keras and TensorFlow: Applying regularization and normalization techniques

Building Neural Networks with Keras and TensorFlow: Using activation functions and weight initializers

Building Neural Networks with Keras and TensorFlow: Compiling and training deep learning models

Building Neural Networks with Keras and TensorFlow: Model Performance Assessment and Optimization

Building Neural Networks with Keras and TensorFlow: Using callbacks and saving models

Building Neural Networks with Keras and TensorFlow: Fine-tuning and transfer learning

Building Neural Networks with Keras and TensorFlow: Implementing neural networks for classification and regression tasks

Building Neural Networks with Keras and TensorFlow: Applications in natural language processing and computer vision

Building Neural Networks with Keras and TensorFlow: Integrating TensorFlow with the Python Data Ecosystem

Deep Learning Models for Computer Vision

Convolutional Neural Networks (CNNs)

Transfer Learning and Fine-tuning

Transfer Learning and Fine-tuning: Definition of Transfer Learning

Transfer Learning and Fine-tuning: Benefits of Transfer Learning

Transfer Learning and Fine-tuning: Application Scenarios

Transfer Learning and Fine-tuning: Pre-trained Neural Networks

Transfer Learning and Fine-tuning: Feature Extraction

Transfer Learning and Fine-tuning: Fine-tuning of layers

Transfer Learning and Fine-tuning: Layer Freezing

Transfer Learning and Fine-tuning: Adapting models to new domains

Transfer Learning and Fine-tuning: Datasets and Data Augmentation

Transfer Learning and Fine-tuning: Optimizers and Learning Rates

Transfer Learning and Fine-tuning: Regularization and Avoiding Overfitting

Transfer Learning and Fine-tuning: Deep Learning Frameworks (TensorFlow, Keras, PyTorch)

Transfer Learning and Fine-tuning: Model Evaluation and Cross Validation

Transfer Learning and Fine-tuning: Transfer Learning in Computer Vision

Transfer Learning and Fine-tuning: Transfer Learning in Natural Language Processing (NLP)

Transfer Learning and Fine-tuning: Challenges and Limitations of Transfer Learning