100 Days Challenge - Days 1-10 Summary and Upcoming Goals

100 Days Challenge

Days 1-10 Summary 

Completed revising Andrew Ng's Machine Learning course on Coursera, did 2 projects and started learning about Decision Trees and Random Forests from Statquest. Details below:


Covered the following topics:

• Linear Regression
• Logistic Regression
• Regularization
• Neural Networks
• Bias, Variance, Handling Skewed Data
• Support Vector Machines
• K-Means Clustering
• Dimensionality Reduction and Principal Component Analysis
• Anomaly Detection
• Recommender Systems
• Machine Learning in Large Scale
• Photo OCR
• Decision Trees and Random Forests

Did 2 projects:

Clustering football players into 4 classes using K-Means Algorithm on the FIFA 19 ratings dataset.
More about the project

Implemented Anomaly Detection to identify fraud credit card transactions from credit card transaction data.
More about the project

Goals for upcoming days

1. Learn prerequisites of a Kaggle competition (House Prices : Advanced Regression Techniques) like feature engineering, gradient boosting and random forests.
2. Participate in the competition above.
3. Build a recommender system.