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- AI Fundamentals: Practical ML Essentials
Curriculum
- 10 Sections
- 58 Lessons
- 10 Weeks
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- Python & Data Tools for Machine Learning8
- Machine Learning Workflows & Data Prep10
- 2.1Machine Learning Difficulties
- 2.2Model Selection
- 2.3Comparing Different Estimators
- 2.4Machine Learning Workflow and Pipelines
- 2.5Feature Engineering
- 2.6Overfitting and Validation
- 2.7Regularization and Feature Selection
- 2.8Module Summary: Model Selection and ML Workflows
- 2.9Quiz-1: Foundation of Machine Learning5 Questions
- 2.10FML-Assignment-1: Model Selection, Pipeline, and Regularization5 Days
- Supervised Learning – Classification10
- 3.1Introduction to Supervised Learning Algorithms
- 3.2Classification
- 3.3SVM (Support Vector Machines)
- 3.4Decision Tree and Tree-Based Models
- 3.5Probabilistic Classification Models
- 3.6Ensemble Models
- 3.7Dimensionality Reduction using PCA
- 3.8Module Summary: Supervised Learning
- 3.9Quiz-2: Supervised Learning – Classification10 Questions
- 3.10FML-Assignment-2: Supervised Learning in Practice5 Days
- Supervised Learning – Regression11
- 4.1Linear and Logistic Regressions
- 4.2Gradient Descent
- 4.3Multivariate Regression
- 4.4Robust Regression
- 4.5Logistic Regression
- 4.6Radial Basis Function (RBF)
- 4.7Predictions and Model Goodness
- 4.8Predicting with Linear and Logistic Regression Models
- 4.9Summary Module: Supervised Learning – Regression
- 4.10Quiz-3: Supervised Learning – Regression15 Questions
- 4.11FML-Assignment-3: Apply Supervised Learning – Regression5 Days
- Unsupervised Learning – Clustering & Dimensionality Reduction15
- 5.1Clustering
- 5.2K-Nearest Neighbors for Unsupervised Tasks
- 5.3Elongated Clusters
- 5.4Gaussian Mixture Models (GMMs)
- 5.5Density-Based Spatial Clustering of Applications with Noise (DBSCAN)
- 5.6Hierarchical Clustering
- 5.7Autoencoders (Neural Network-Based)
- 5.8Summary: Unsupervised Learning Algorithms
- 5.9t-Distributed Stochastic Neighbor Embedding (t-SNE)
- 5.10UMAP (Uniform Manifold Approximation and Projection)
- 5.11Apriori Algorithm
- 5.12Eclat Algorithm
- 5.13Spectral Clustering
- 5.14Quiz-4: Unsupervised Learning0 Questions
- 5.15FML-Assignment-4: Applying Unsupervised Learning3 Days
- Model Evaluation & Tuning8
- 6.1Metrics for Supervised Learning – Classification
- 6.2Metrics for Regression
- 6.3Metrics for Clustering
- 6.4Hyper-Parameter Tuning for Supervised Learning
- 6.5Hyper-Parameter Tuning for Unsupervised Learning
- 6.6SHAP with XGBoost
- 6.7Quiz-5: Evaluation Metrics, Optimization, and Interpretability in ML10 Minutes0 Questions
- 6.8FML-Assignment-5: Model Evaluation Metrics, Tuning, and Explainability5 Days
- Modern Topics – Transfer Learning, GANs, XAI8
- 7.1Transfer Learning
- 7.2Generative Adversarial Networks (GANs)
- 7.3Explainable Machine Learning (XAI)
- 7.4Bayesian Machine Learning
- 7.5Reinforcement Learning (RL)
- 7.6Time Series Analysis and Forecasting
- 7.7Quiz-5: Transfer Learning, GANs, XAI10 Minutes0 Questions
- 7.8FML-Assignment-6: Transfer Learning, GANs, RL, and XAI3 Days
- ML Applications – NLP, Recommenders0
- Fairness, Responsible AI, Ethics, and ML in Society0
- Capstone Project – Project Presentations & ML Futures0
Quiz-5: Transfer Learning, GANs, XAI
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