Module #1 Introduction to Machine Learning Overview of machine learning, types of learning, and applications
Module #2 Supervised vs Unsupervised Learning Key differences between supervised and unsupervised learning
Module #3 Mathematical Preliminaries Review of linear algebra, calculus, and probability theory
Module #4 Data Preprocessing Importance of data preprocessing, techniques for feature scaling and normalization
Module #5 Model Evaluation Metrics Overview of common evaluation metrics for supervised and unsupervised learning
Module #6 Linear Regression Introduction to linear regression, ordinary least squares, and gradient descent
Module #7 Logistic Regression Introduction to logistic regression, binary classification, and decision boundaries
Module #8 Decision Trees Introduction to decision trees, entropy, and information gain
Module #9 Random Forests Introduction to random forests, ensemble learning, and bootstrap sampling
Module #10 Support Vector Machines Introduction to SVMs, kernel methods, and soft margin classification
Module #11 Neural Networks for Supervised Learning Introduction to neural networks for supervised learning, backpropagation, and activation functions
Module #12 Model Selection and Hyperparameter Tuning Importance of model selection and hyperparameter tuning, techniques for cross-validation and grid search
Module #13 K-Means Clustering Introduction to k-means clustering, centroid initialization, and clustering evaluation metrics
Module #14 Hierarchical Clustering Introduction to hierarchical clustering, dendrograms, and clustering evaluation metrics
Module #15 Principal Component Analysis Introduction to PCA, dimensionality reduction, and feature extraction
Module #16 t-SNE and Dimensionality Reduction Introduction to t-SNE, dimensionality reduction, and manifold learning
Module #17 Density-Based Clustering Introduction to density-based clustering, DBSCAN, and noise detection
Module #18 Anomaly Detection Introduction to anomaly detection, techniques for identifying outliers and novelties
Module #19 Deep Learning for Computer Vision Introduction to deep learning for computer vision, convolutional neural networks, and transfer learning
Module #20 Autoencoders and Generative Models Introduction to autoencoders, generative models, and unsupervised representation learning
Module #21 Word Embeddings and Natural Language Processing Introduction to word embeddings, NLP, and text analysis
Module #22 Real-World Applications and Case Studies Real-world applications and case studies of supervised and unsupervised learning
Module #23 Practical Implementation of Supervised Learning Hands-on implementation of supervised learning algorithms using Python and scikit-learn
Module #24 Practical Implementation of Unsupervised Learning Hands-on implementation of unsupervised learning algorithms using Python and scikit-learn
Module #25 Project Development and Deployment Guided project development and deployment using supervised and unsupervised learning algorithms
Module #26 Practical Implementation of Deep Learning Hands-on implementation of deep learning algorithms using Python and TensorFlow/Keras
Module #27 Practical Implementation of Natural Language Processing Hands-on implementation of NLP algorithms using Python and spaCy
Module #28 Practical Implementation of Computer Vision Hands-on implementation of computer vision algorithms using Python and OpenCV
Module #29 Big Data and Scalability Practical implementation of supervised and unsupervised learning on big data using Spark and Hadoop
Module #30 Course Wrap-Up & Conclusion Planning next steps in Supervised and Unsupervised Learning career
