Machine Learning Mastery Course
– Module #1 – Introduction to Machine Learning
Lecture 1: What is Machine Learning?
Lecture 2: History and Evolution of ML
Lecture 3: Types of Machine Learning: Supervised, Unsupervised, Reinforcement
Lecture 4: Real-World Applications of ML
Lecture 5: ML vs Traditional Programming
Lecture 6: The Machine Learning Workflow
Lecture 7: Ethical Considerations in ML
Lecture 8: Career Opportunities in Machine Learning
– Module #2 – Python for Machine Learning
Lecture 9: Python Basics for ML
Lecture 10: Jupyter Notebooks and Google Colab
Lecture 11: NumPy for Numerical Computing
Lecture 12: Pandas for Data Manipulation
Lecture 13: Matplotlib and Seaborn for Visualization
Lecture 14: Setting Up Your ML Environment
Lecture 15: Essential Python Libraries for ML
Lecture 16: Writing Efficient Python Code
– Module #3 – Mathematics for Machine Learning
Lecture 17: Linear Algebra Fundamentals
Lecture 18: Matrix Operations and Vector Spaces
Lecture 19: Calculus for Optimization
Lecture 20: Probability Theory Basics
Lecture 21: Statistics for Data Analysis
Lecture 22: Probability Distributions
Lecture 23: Bayes' Theorem and Applications
Lecture 24: Information Theory Concepts
– Module #4 – Data Preprocessing
Lecture 25: Data Collection and Sourcing
Lecture 26: Handling Missing Data
Lecture 27: Outlier Detection and Treatment
Lecture 28: Data Transformation and Scaling
Lecture 29: Encoding Categorical Variables
Lecture 30: Feature Engineering Techniques
Lecture 31: Data Normalization and Standardization
Lecture 32: Working with Imbalanced Datasets
– Module #5 – Supervised Learning - Regression
Lecture 33: Understanding Regression Problems
Lecture 34: Linear Regression Theory
Lecture 35: Multiple Linear Regression
Lecture 36: Polynomial Regression
Lecture 37: Regularization: Ridge and Lasso
Lecture 38: Support Vector Regression
Lecture 39: Decision Tree Regression
Lecture 40: Random Forest Regression
– Module #6 – Supervised Learning - Classification
Lecture 41: Understanding Classification Problems
Lecture 42: Logistic Regression
Lecture 43: K-Nearest Neighbors (KNN)
Lecture 44: Support Vector Machines (SVM)
Lecture 45: Naive Bayes Classifier
Lecture 46: Decision Trees for Classification
Lecture 47: Random Forest Classifier
Lecture 48: Gradient Boosting Machines
– Module #7 – Model Evaluation and Validation
Lecture 49: Train-Test Split and Cross-Validation
Lecture 50: Performance Metrics for Regression
Lecture 51: Confusion Matrix and Classification Metrics
Lecture 52: ROC Curve and AUC
Lecture 53: Precision-Recall Trade-off
Lecture 54: Bias-Variance Trade-off
Lecture 55: Overfitting and Underfitting
Lecture 56: Learning Curves Analysis
– Module #8 – Unsupervised Learning - Clustering
Lecture 57: Introduction to Clustering
Lecture 58: K-Means Clustering
Lecture 59: Hierarchical Clustering
Lecture 60: DBSCAN Clustering
Lecture 61: Gaussian Mixture Models
Lecture 62: Clustering Evaluation Metrics
Lecture 63: Choosing the Right Number of Clusters
Lecture 64: Applications of Clustering
– Module #9 – Unsupervised Learning - Dimensionality Reduction
Lecture 65: The Curse of Dimensionality
Lecture 66: Principal Component Analysis (PCA)
Lecture 67: Singular Value Decomposition (SVD)
Lecture 68: t-SNE for Visualization
Lecture 69: Linear Discriminant Analysis (LDA)
Lecture 70: Autoencoders for Dimensionality Reduction
Lecture 71: Feature Selection Methods
Lecture 72: Applications of Dimensionality Reduction
– Module #10 – Feature Engineering and Selection
Lecture 73: Feature Engineering Strategies
Lecture 74: Polynomial Features and Interactions
Lecture 75: Binning and Discretization
Lecture 76: Time-Based Feature Engineering
Lecture 77: Text Feature Engineering
Lecture 78: Feature Scaling and Normalization
Lecture 79: Wrapper Methods for Feature Selection
Lecture 80: Embedded Methods for Feature Selection
– Module #11 – Ensemble Methods
Lecture 81: Bagging and Bootstrap Aggregation
Lecture 82: Random Forests in Depth
Lecture 83: Boosting Algorithms
Lecture 84: AdaBoost and Gradient Boosting
Lecture 85: XGBoost, LightGBM, and CatBoost
Lecture 86: Stacking and Blending Models
Lecture 87: Voting Classifiers
Lecture 88: Ensemble Model Evaluation
– Module #12 – Hyperparameter Tuning
Lecture 89: Understanding Hyperparameters
Lecture 90: Grid Search and Random Search
Lecture 91: Bayesian Optimization
Lecture 92: Genetic Algorithms for Optimization
Lecture 93: Hyperparameter Tuning with Scikit-learn
Lecture 94: Automated Machine Learning (AutoML)
Lecture 95: Optuna for Hyperparameter Optimization
Lecture 96: Best Practices for Hyperparameter Tuning
– Module #13 – Deep Learning Fundamentals
Lecture 97: Introduction to Neural Networks
Lecture 98: Perceptrons and Activation Functions
Lecture 99: Feedforward Neural Networks
Lecture 100: Backpropagation Algorithm
Lecture 101: Gradient Descent Variants
Lecture 102: Weight Initialization Techniques
Lecture 103: Batch Normalization
Lecture 104: Dropout Regularization
– Module #14 – Deep Learning with TensorFlow/Keras
Lecture 105: Setting Up TensorFlow Environment
Lecture 106: Keras API for Deep Learning
Lecture 107: Building Your First Neural Network
Lecture 108: Model Compilation and Training
Lecture 109: Callbacks and Early Stopping
Lecture 110: Model Checkpointing
Lecture 111: TensorBoard for Visualization
Lecture 112: Saving and Loading Models
– Module #15 – Convolutional Neural Networks
Lecture 113: Introduction to Computer Vision
Lecture 114: Convolutional Layers and Filters
Lecture 115: Pooling Layers and Stride
Lecture 116: CNN Architectures: LeNet, AlexNet
Lecture 117: VGG, ResNet, and Inception
Lecture 118: Transfer Learning with Pre-trained Models
Lecture 119: Data Augmentation for Images
Lecture 120: Object Detection Basics
– Module #16 – Natural Language Processing
Lecture 121: Text Preprocessing Techniques
Lecture 122: Bag of Words and TF-IDF
Lecture 123: Word Embeddings: Word2Vec, GloVe
Lecture 124: Recurrent Neural Networks (RNNs)
Lecture 125: LSTM and GRU Networks
Lecture 126: Sequence-to-Sequence Models
Lecture 127: Attention Mechanisms
Lecture 128: Transformers Architecture
– Module #17 – Reinforcement Learning
Lecture 129: Introduction to Reinforcement Learning
Lecture 130: Markov Decision Processes
Lecture 131: Q-Learning Algorithm
Lecture 132: Deep Q-Networks (DQN)
Lecture 133: Policy Gradient Methods
Lecture 134: Actor-Critic Algorithms
Lecture 135: Proximal Policy Optimization (PPO)
Lecture 136: Applications of RL in Games and Robotics
– Module #18 – Time Series Analysis
Lecture 137: Time Series Data Characteristics
Lecture 138: Stationarity and Differencing
Lecture 139: ARIMA Models
Lecture 140: Seasonal Decomposition
Lecture 141: Exponential Smoothing
Lecture 142: Prophet for Time Series Forecasting
Lecture 143: LSTM for Time Series
Lecture 144: Applications in Finance and Sales
– Module #19 – Model Deployment and MLOps
Lecture 145: Model Serialization and Formats
Lecture 146: REST APIs with Flask and FastAPI
Lecture 147: Docker for ML Deployment
Lecture 148: Cloud Deployment on AWS, GCP, Azure
Lecture 149: Model Monitoring and Logging
Lecture 150: CI/CD for Machine Learning
Lecture 151: Model Versioning with MLflow
Lecture 152: A/B Testing for ML Models
– Module #20 – Advanced Deep Learning
Lecture 153: Generative Adversarial Networks (GANs)
Lecture 154: Variational Autoencoders (VAEs)
Lecture 155: Self-Supervised Learning
Lecture 156: Contrastive Learning
Lecture 157: Few-Shot and Zero-Shot Learning
Lecture 158: Meta-Learning
Lecture 159: Federated Learning
Lecture 160: Explainable AI (XAI)
– Module #21 – Real-World ML Projects
Lecture 161: End-to-End Project: House Price Prediction
Lecture 162: End-to-End Project: Customer Churn Prediction
Lecture 163: End-to-End Project: Image Classification
Lecture 164: End-to-End Project: Sentiment Analysis
Lecture 165: End-to-End Project: Recommendation System
Lecture 166: End-to-End Project: Fraud Detection
Lecture 167: Kaggle Competition Strategies
Lecture 168: Building a Machine Learning Portfolio
Lecture 169: Preparing for ML Job Interviews
Lecture 170: Future Trends in Machine Learning