Duration

• Total Duration: 4 weeks
• Weekly Commitment: 5 – 6 hours per week

Course Format

• Lectures: In-depth video lectures and demonstrations
• Labs: Hands-on coding labs using Python and machine learning libraries
• Projects: Individual and group projects for practical experience
• Weekly Assignments: To reinforce learning and apply concepts
• Interactive Sessions: Live Q&A and discussion sessions with instructors

Syllabus

1. Introduction to Machine Learning

• Overview of machine learning and its applications
• Types of machine learning: supervised, unsupervised, and reinforcement learning
• Essential Python for machine learning
• Data preprocessing and exploration

2. Supervised Learning

• Linear Regression and Logistic Regression
• Decision Trees and Random Forests
• Support Vector Machines (SVM)
• Evaluation metrics for classification and regression

3. Unsupervised Learning

• Clustering algorithms: K-means, Hierarchical, DBSCAN
• Dimensionality Reduction: PCA and t-SNE
• Association Rule Mining

4. Neural Networks and Deep Learning

• Basics of Neural Networks
• Introduction to Deep Learning Frameworks
• Convolutional Neural Networks (CNNs) for image analysis
• Recurrent Neural Networks (RNNs) for time series analysis

5. Advanced Machine Learning Techniques

• Ensemble methods and boosting
• Feature engineering and selection
• Hyperparameter tuning and optimization
• Model deployment and scalability

6. Special Topics in Machine Learning

• Natural Language Processing (NLP) basics
• Introduction to Reinforcement Learning
• Ethics and bias in machine learning
• Current trends and research topics

7. Project and Course Conclusion

• Comprehensive project incorporating various machine learning techniques
• Presentations and peer reviews of projects
• Course wrap-up and guidance for further learning

Certification

• Certificate of Completion to be awarded to participants who complete the course, including the project.

Enrollment

• Next Cohort Start Date: January 2024
• Enrollment Deadline: December 28, 2023

Additional Support

• Access to a dedicated course forum for ongoing support
• Regular office hours with instructors for personalized guidance
• Access to a curated list of resources for extended learning