Name		Name	Last commit message	Last commit date
parent directory ..
Statistics_for_Machine_Learning		Statistics_for_Machine_Learning
Supervised_Machine_Learning		Supervised_Machine_Learning
Unsupervised_Machine_Learning		Unsupervised_Machine_Learning
README.md		README.md

README.md

⚜️ MACHINE LEARNING ⚜️

Roadmap for GWoC '21

🟡 Week 1 [Statistics for Machine Learning] :

1.1. Variables, Range, Population Distribution, Sample Distribution
1.2. PDFs, CDFs
1.3. Central Limit Theorem
1.4. Variance, Standard Deviation, Expectation
1.5. Probability Distributions (Gaussian, Standard, Poisson)
1.6. Maximum Likelyhood Estimation
1.7. Parzen Windows

🟡 Week 2 [Supervised Machine Learning (Classic Algorithms)] :

2.1. What is Learning? Why Machine Learning works?
2.2. Linear Regression
2.3. Logistic Regression
2.4. Sessions on Numpy and Pandas
2.5. Implementing Linear Regression with Logistic Regression
2.6. K-Nearest Neighbour Algorithms
2.7. Decision Trees

N.B.: Upcoming roadmap will be published as the program goes!

Supervisors of Machine Learning

_{Abhishek S (B1)}

_{Akshay Raina (B2)}

_{Isha Shaw (B3)}

_{Madipally Sai Krishna Sashank (B4)}

Happy Contributions!