1. Week 1
1. Numpy ✅
2. Pandas ✅
3. Visualization ✅
2. Week 2
1. Probability and statistics: theory and concepts ✅
2. A/B Testing ✅
3. ANOVA ✅
3. Week 3
1. Summary and final exercise probability and statistics ✅
2. Supervised learning ✅
3. Supervised learning ✅
4. Week 4
1. Supervised learning ✅
2. Time series analysis part I ✅
3. Time series analytis part II ✅
5. Week 5
1. Decision trees: regression trees, classification trees 📝
2. Exercise decision trees 📝
3. Intro to unsupervised learning 📝
6. Week 6
1. K - means ✅
2. Hierarchical clustering ✅
3. Clustering exercise ✅
7. Week 7
1. Supervised learning II 📝
2. Supervised learning II 📝
3. Exercise Supervised learning II
8. Week 8
1. Data: Engineer vs. Data Scientist 📝
2. Web scraping 📝
3. API connections 📝
9. Week 9
1. Exercise web scraping and API 🤔
2. SQL vs. No SQL: Import and export data 🤔
3. ETL Process 🤔
10. Week 10
1. Final exercise ETL Process 🤔
2. Intro to artificial neural networks: logistic regression is also a neural network 🤔
3. Multilayer perceptron 🤔
11. Week 11
1. Deep learning 😰
2. Feed forward neural networks 😰
3. Final exercise neural networks 😰
12. Week 12
1. Recurrent neural networks 😰
2. Final exercise deep neural networks 😰
3. API Development 😰
13. Week 13
1. Exercise local deployment 😰
2. Convolutional neural networks 😰
3. Transfer learning and pretraining 😰
14. Week 14
1. Cloud solutions for Machine Learning (GPU training) 😰
2. Containers: Docker 😰
3. Deploy machine learning models in cloud 😰
15. Week 15
1. Final project.
2. Final project.
3. Final project.
16. Week 16
1. Final project.
2. Final project.
3. Final project.