Learn AI, machine learning, and deep learning with Python from the ground up, then build and tune models with XGBoost, TensorFlow, PyTorch, CNNs, RNNs, transformers, and transfer learning through practical Python projects.
Key Features
- Build Python, data science, mathematics, and statistics foundations for machine learning
- Train, evaluate, and tune regression, classification, ensemble, XGBoost, LightGBM, and CatBoost models
- Create CNN, RNN, transformer, and transfer learning projects with TensorFlow, Keras, and PyTorch
Book Description
Learning modern AI often feels fragmented: Python in one resource, mathematics in another, and deep learning or transformers somewhere else. This book brings those foundations into one practical progression, helping you connect the concepts behind machine learning with the code used to build models. You will start with Python programming, NumPy, Pandas, data visualization, exploratory data analysis, linear algebra, calculus, probability, and statistics. From there, you will build supervised learning skills across regression, classification, k-NN, feature engineering, model evaluation, cross-validation, and hyperparameter tuning. You will then work with ensemble methods including random forests, gradient boosting, XGBoost, LightGBM, and CatBoost, while learning to handle imbalanced data and justify modeling decisions. The final part moves into neural networks and deep learning with TensorFlow, Keras, and PyTorch. You will build CNNs for image classification, explore RNNs, LSTMs, GRUs, word embeddings, and sequence-to-sequence models, and understand attention and transformer architectures through BERT and GPT examples. You will finish by applying transfer learning and fine-tuning to computer vision and NLP tasks, supported by projects that turn theory into practical AI and machine learning skills for real-world problem solving.
What you will learn
- Use Python, NumPy, Pandas, Matplotlib, and Seaborn for data analysis
- Apply linear algebra, calculus, probability, and statistics to ML
- Build regression, classification, and k-NN models
- Engineer features and evaluate models with cross-validation and hyperparameter tuning
- Train ensembles with random forests, XGBoost, LightGBM, and CatBoost
- Build neural networks and CNNs with TensorFlow, Keras, and PyTorch
- Develop RNNs, LSTMs, GRUs, attention, BERT, and GPT workflows for NLP
- Apply transfer learning and fine-tuning to computer vision and NLP
Who this book is for
This book is for aspiring data scientists, machine learning engineers, AI developers, students, and career changers who want one structured path from Python fundamentals to machine learning and deep learning. It suits readers seeking practical skills in data analysis, predictive modeling, computer vision, NLP, transformers, and transfer learning. No prior machine learning or deep learning experience is required. Python is introduced from the fundamentals, although basic programming familiarity and foundational mathematics will help.
