This module will help you master dimensionality reduction techniques to handle high-dimensional data effectively. You'll learn to apply Principal Component Analysis (PCA) to reduce dimensionality while retaining key features, use t-distributed Stochastic Neighbor Embedding (t-SNE) to visualize high-dimensional data in 2D/3D space for clustering and pattern recognition, and implement Uniform Manifold Approximation and Projection (UMAP) for efficient dimensionality reduction, leveraging its speed and structure-preserving properties.

In this module, you'll focus on natural language processing techniques from basic text preprocessing to advanced sentiment analysis. You'll learn how to preprocess text data using tokenization, stopword removal, and stemming/lemmatization with Natural Language Toolkit (NLTK) and spaCy. Through implementation of text classification using various techniques like Bag-of-Words, TF-IDF, and word embeddings, you'll gain practical experience in NLP tasks. You'll also train sentiment analysis models using Hugging Face Transformers and Scikit-learn.

Reinforcement Learning Description: In this module, you'll explore the fundamentals of reinforcement learning (RL), including Markov Decision Processes (MDPs) and reward-based learning. You'll understand the key components of RL systems and implement both policy-based and value-based learning techniques. Through practical examples and hands-on implementation, you'll discover how RL is applied in real-world scenarios like robotics, gaming, and finance.

This module focuses on automated machine learning techniques and model optimization. You'll learn to automate model selection and hyperparameter tuning using Auto-sklearn and GridSearchCV, and optimize models using MLflow for experiment tracking and reproducibility. You'll also explore Bayesian optimization techniques to improve model accuracy. The module concludes with a comprehensive capstone project that combines multiple techniques from throughout the course.