Machine Learning, Deep Learning & Time Series with Python

About this course

Machine Learning, Deep Learning & Time Series with Python is a comprehensive, practical course designed to take you from Python programming fundamentals to Machine Learning, Deep Learning, Reinforcement Learning, Time Series, Generative AI, Prompt Engineering, and real-world analytics applications.

The course begins with a strong Python foundation, covering variables, data types, operators, strings, lists, tuples, sets, dictionaries, conditionals, loops, functions, recursion, modules, comprehensions, file handling, exception handling, object-oriented programming, iterators, generators, decorators, regular expressions, dates and times, JSON, debugging, logging, testing, concurrency, performance profiling, SQL, web scraping, CLI tools, context managers, and Excel/CSV operations.

You will then work with important Python libraries for Machine Learning and Deep Learning, including NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch, Keras, and Hugging Face.

The Machine Learning section covers Linear Regression, Logistic Regression, KNN, SVM, Decision Trees, Random Forests, Naïve Bayes, Ensemble Learning, K-Means, Hierarchical Clustering, DBSCAN, PCA, Association Rules, NLP, XGBoost, LightGBM, FastAI, JAX, and Q-Learning.

The course also includes a complete Reinforcement Learning project based on an Unmanned Ground Vehicle path-finding problem, covering the Bellman Equation, Markov Decision Process, Q-Learning, implementation, and path visualization.

In Deep Learning, you will learn Neural Networks, MLP, CNN, RNN, and LSTM.

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