Day to Day

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.