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1. Introduction to Python Programming
1.1. Variable names, Expressions and statements
1.2. Fundamental Concepts and Operations in Python
1.3. Functions
2. Control Flow: Conditionals, Recursion, and Iteration
2.1. Conditionals and recursion
2.2. Iteration
3. Data Structures and File Handling in Python
3.1. Strings in Python
3.2. Python Lists
3.3. Python Dictionaries: Key-Value Pairs in Python
3.4. Python Tuples: Immutable Sequences in Python
3.5. Python Sets
3.6. Files in Python (Optional Section)
4. Classes and Objects
4.1. Introduction to Classes and Objects
4.2. Inheritance and Polymorphism
4.3. Encapsulation and Abstraction
4.4. Copying Objects (Optional Section)
4.5. Magic Methods (Dunder Methods) in Python (Optional Section)
4.6. Examples of Classes (Optional Section)
5. Introduction to NumPy
5.1. Basics of Numpy
5.2. NumPy Function Reference and Usage Examples
5.3. Advanced Numpy Concepts (Optional Section)
6. Working with Data using Pandas
6.1. An Introduction to Pandas
6.2. DataFrame and Series Indices
6.3. Pandas Data Selection
6.4. Handling Missing Data in Pandas
6.5. Combining Datasets using Pandas
6.6. Aggregation and Grouping in Pandas
7. Data Visualization using Python
7.1. Getting started with Matplotlib
7.2. Matplotlib Styles
7.3. Matplotlib interfaces
7.4. Adjusting the Plot
7.5. Seaborn plots
7.6. Python Plotting Guide
8. An Introduction to Computer Vision
8.1. Getting Started with OpenCV
8.2. Geometric Transformations
8.3. Image Thresholding
8.4. Image Filtering
8.5. Drawing Functions (Optional Section)
9. An Introduction to Machine Learning
9.1. Prologue: Statistical Metrics and Evaluation
9.2. An Introduction to Linear Regression
9.3. Multiple Linear Regression
9.4. Logistic Regression
9.5. K-Nearest Neighbors (K-NN)
9.6. Resampling Methods
9.7. Support Vector Machines
10. Tree-Based Methods
10.1. Fundamental Structure of Decision Trees
10.2. Regression Trees
10.3. Classification Trees
10.4. Regression Trees and Linear Models (Optional Section)
10.5. Enhancing Decision Trees with Bagging: An Introduction (Optional Section)
10.6. Random Forests
10.7. Gradient Boosting
11. Dimensionality Reduction and Feature Selection
11.1. Introduction to Dimensionality Reduction
11.2. Principal Components Analysis (PCA)
11.3. t-Distributed Stochastic Neighbor Embedding (t-SNE)
11.4. Linear and Quadratic Discriminant Analyses
11.5. Recursive Feature Elimination (RFE)
11.6. Practical Considerations in Dimensionality Reduction
12. Introduction to Deep Learning
12.1. Understanding Deep Learning
12.2. Fundamentals of Neural Networks
12.3. TensorFlow Basics
12.4. Introduction to Variables
12.5. Tensors in Various Operations (Ops)
12.6. Building a linear Regression Model
12.7. Building a Logistic Regression Model
12.8. Multilayer Perceptron (MLP)
12.9. Deep Learning Architectures
12.10. Image classification with TensorFlow
12.11. Image Augmentations with TensorFlow
12.12. Enhancing Image Classification Precision Through TensorFlow and Data Augmentation Strategies
12.13. Brief Overview of Additional Topics
13. References
Index
