Module #1 Introduction to Machine Learning Overview of machine learning, types of machine learning, and importance of machine learning in real-world applications
Module #2 Mathematical Foundations of Machine Learning Review of linear algebra, calculus, probability, and statistics required for machine learning
Module #3 Types of Machine Learning Supervised, unsupervised, and reinforcement learning, including examples and applications
Module #4 Supervised Learning Regression, classification, logistic regression, and support vector machines
Module #5 Unsupervised Learning Clustering, dimensionality reduction, and density estimation
Module #6 Introduction to Neural Networks Basic concepts of neural networks, including perceptrons, multilayer perceptrons, and backpropagation
Module #7 Deep Learning Fundamentals Convolutional neural networks, recurrent neural networks, and long short-term memory networks
Module #8 Gradient Descent and Optimization Gradient descent, stochastic gradient descent, and other optimization techniques
Module #9 Overfitting and Underfitting Causes, consequences, and prevention of overfitting and underfitting in machine learning models
Module #10 Model Evaluation Metrics Metrics for evaluating machine learning models, including accuracy, precision, recall, and F1 score
Module #11 Data Preprocessing Handling missing values, data normalization, and feature scaling
Module #12 Feature Selection and Engineering Techniques for selecting and engineering features for machine learning models
Module #13 Decision Trees and Random Forests Decision trees, random forests, and gradient boosting machines
Module #14 K-Nearest Neighbors and Support Vector Machines K-nearest neighbors and support vector machines for classification and regression
Module #16 Dimensionality Reduction Techniques Principal component analysis, t-SNE, and autoencoders for dimensionality reduction
Module #17 Recommendation Systems Content-based, collaborative, and hybrid recommendation systems
Module #18 Natural Language Processing Tokenization, sentiment analysis, and named entity recognition using machine learning
Module #19 Time Series Analysis Time series forecasting, seasonal decomposition, and anomaly detection
Module #20 Unsupervised Deep Learning Autoencoders, generative adversarial networks, and variational autoencoders
Module #21 Reinforcement Learning Markov decision processes, Q-learning, and deep reinforcement learning
Module #22 Transfer Learning and Fine-Tuning Using pre-trained models and fine-tuning for specific tasks
Module #23 Model Interpretability and Explainability Techniques for interpreting and explaining machine learning models
Module #24 Machine Learning in Real-World Applications Case studies of machine learning in computer vision, natural language processing, and healthcare
Module #25 Course Wrap-Up & Conclusion Planning next steps in Machine Learning Algorithms and Techniques career
