What is the Course?
A comprehensive program designed to master machine learning algorithms and techniques.
From fundamental concepts to advanced model deployment, learn to build intelligent systems
that solve real-world problems across industries.
Course Curriculum
Total Sessions: 16
Session Duration: 3 Hours
Sessions per Week: 2
Week 1
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Session 1: Introduction to Machine Learning
- Overview of Machine Learning (ML) and Key Applications
- Types of ML (Supervised, Unsupervised, Reinforcement)
- Course Goals & Structure
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Session 2: Python & Scikit-learn Basics
- Python Environment Setup & Review
- Introduction to Scikit-learn
- Basic Statistics & Probability Concepts
Week 2
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Session 3: Data Preprocessing & Feature Engineering
- Data Cleaning (Handling Missing Values, Outliers)
- Feature Engineering (Scaling, Encoding, Feature Selection)
- Best Practices in Preparing Data for ML
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Session 4: Supervised Learning Fundamentals
- Linear Regression & Logistic Regression
- Evaluation Metrics (MSE, Accuracy, Precision/Recall)
- Practical Exercises in Scikit-learn
Week 3
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Session 5: Advanced Supervised Learning
- Decision Trees, Random Forests, and Ensemble Methods
- Hyperparameter Tuning (Grid Search, Random Search)
- Hands-On Exercises & Model Comparison
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Session 6: Model Evaluation & Validation
- Train/Test Splits, Cross-Validation
- Overfitting vs. Underfitting
- Performance Metrics & ROC Curves
Week 4
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Session 7: Unsupervised Learning Fundamentals
- Clustering (K-Means, Hierarchical)
- Dimensionality Reduction (PCA)
- Practical Applications & Hands-On Exercises
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Session 8: Advanced Unsupervised Techniques
- DBSCAN, t-SNE, and Other Methods
- Interpreting Clusters & Visualizing High-Dimensional Data
- Project Brainstorming Session
Week 5
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Session 9: Introduction to Neural Networks & TensorFlow
- Basics of Neural Networks (Perceptron, Activation Functions)
- Setting Up TensorFlow
- Building a Simple Neural Network
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Session 10: Deep Learning with TensorFlow
- Dense (Fully Connected) Networks
- Forward Propagation & Backpropagation
- Practical Exercises with Neural Network Training
Week 6
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Session 11: Model Optimization & Regularization
- Dropout, L1/L2 Regularization
- Learning Rate Schedules & Optimizers (SGD, Adam)
- Tips for Faster Training & Better Generalization
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Session 12: Specialized Architectures
- Overview of Convolutional Neural Networks (CNNs)
- Intro to Recurrent Neural Networks (RNNs) & LSTMs
- Potential Project Ideas & Real-World Applications
Week 7
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Session 13: Real-World Applications & Best Practices
- Handling Large Datasets & Big Data Pipelines
- Model Deployment & Serving (Cloud/On-Premise)
- Ethical Considerations & Bias in ML
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Session 14: Project Setup & Collaborative Tools
- Version Control with Git/GitHub
- Final Project Requirements & Planning
- Team or Individual Work Kickoff
Week 8
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Session 15: Final Project Development
- Project Work & Iterations
- Instructor/Peer Feedback
- Troubleshooting & Performance Tuning
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Session 16: Final Project Presentation & Wrap-Up
- Final Project Demonstrations
- Course Recap & Q&A
- Next Steps: Competitions, Further Learning, Career Opportunities
