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Book Overview & Buying
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Table Of Contents
AI & Python Development Megaclass
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AI & Python Development Megaclass
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Overview of this book
This course begins with Python programming essentials, including control flow, functions, data structures, and file handling. You’ll then explore core data science tools such as NumPy for numerical computing, Pandas for data manipulation, and Matplotlib/Seaborn for data visualization. Mathematics foundations—linear algebra, calculus, probability, and statistics—are introduced to support AI learning. Each week ends with mini-projects to apply concepts.
As you progress, you’ll dive into machine learning techniques, covering regression, classification, ensemble methods, feature engineering, and hyperparameter tuning. Advanced algorithms such as Random Forests, XGBoost, LightGBM, and CatBoost are explored. Deep learning modules guide you through building neural networks, CNNs for image recognition, RNNs and LSTMs for sequence tasks, and Transformers for NLP applications.
The final stages emphasize hands-on projects and real-world deployment. You’ll apply skills in computer vision, NLP, reinforcement learning, and time series forecasting. GANs expand your understanding of generative modeling, while AI in production introduces Docker, CI/CD, and cloud scaling. The course concludes with modules on AI ethics, safety, and governance, ensuring responsible and practical AI expertise.
Table of Contents (24 chapters)
Week 1: Python Programming Basics for Artificial Intelligence
Week 2: Data Science Essentials for Artificial Intelligence
Week 3: Mathematics for Machine Learning and Artificial Intelligence
Week 4: Probability and Statistics for Machine Learning and AI
Week 5: Introduction to Machine Learning
Week 6: Feature Engineering and Model Evaluation
Week 7: Advanced Machine Learning Algorithms
Week 8: Model Tuning and Optimization
Week 9: Neural Networks and Deep Learning Fundamentals
Week 10: Convolutional Neural Networks (CNNs)
Week 11: Recurrent Neural Networks (RNNs) and Sequence Modeling
Week 12: Transformers and Attention Mechanisms
Week 13: Transfer Learning and Fine-Tuning
Week 1: Python Basics
Week 2: Intermediate Python
Week 3: Working with Data
Week 4: Object-Oriented Programming
Week 5: GUI Programming
Week 6: Web Development with Python
Week 7: Data Science Basics
Days 50–60: Intermediate Projects
Days 61–70: Advanced Intermediate Projects
Days 71–80: AI & Machine Learning Projects
Machine Learning Algorithms and Implementation in Python
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