Intermediate
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Machine Learning
Comprehensive machine learning course covering supervised, unsupervised, and reinforcement learning with hands-on projects.
10 weeks
12 Modules
Certificate Included
Learning Outcomes
What You'll Learn
By the end of this course, you'll be able to:
Implement supervised learning algorithms
Build unsupervised learning models
Evaluate and optimize model performance
Handle feature engineering and selection
Deploy ML models to production
Understand ML best practices
Curriculum
Course Modules
A comprehensive curriculum designed for practical application.
1
ML Fundamentals
- What is ML?
- Types of learning
- ML workflow
- Python ecosystem
2
Linear Regression
- Simple linear regression
- Multiple regression
- Regularization
- Gradient descent
3
Classification
- Logistic regression
- Decision trees
- Random forests
- Evaluation metrics
4
Support Vector Machines
- SVM theory
- Kernel methods
- Hyperparameter tuning
- Practical applications
5
Ensemble Methods
- Bagging and boosting
- XGBoost
- Stacking
- Model selection
6
Dimensionality Reduction
- PCA
- t-SNE
- Feature selection
- LDA
7
Clustering
- K-means
- Hierarchical clustering
- DBSCAN
- Evaluation
8
Recommendation Systems
- Collaborative filtering
- Content-based
- Hybrid approaches
- Deep learning recommenders
9
Model Evaluation
- Cross-validation
- Metrics selection
- Bias-variance tradeoff
- Error analysis
10
Feature Engineering
- Feature transformation
- Encoding
- Scaling
- Automated feature engineering
11
ML Pipelines
- Pipeline design
- Automation
- Model selection
- Hyperparameter optimization
12
Production Deployment
- Model serialization
- API development
- Monitoring
- A/B testing
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