Machine Learning & Data Analytics- Python, Pandas, Maths, Statistics, Probability, Regression, Classification,Clustering

What you'll learn

Part 1 is a Beginner’s course that covers Machine Learning and Data Analytics
Objective is to teach students how to do an End-2-End data science project
From problem definition, data sourcing, wrangling, modelling, analyzing and visualizing to deploying and maintaining
Part 1 will cover all the basics required for building machine learning models - programming, analytics, maths, process, algorithms and deployment
It will provide full maths and logic details for all algorithms
Programming (python) and Data analytics (pandas)
Maths, Statistics and Probability basics required for understanding the different algorithms
Data Science Process – Problem, Wrangling, Algorithm Selection, Model Building , Visualization, Deployment
Data Wrangling
Build Machine Learning models - Supervised & Unsupervised algorithms using Regression, Classification & Clustering
How to Visualize and Evaluate models
Model Persistence and Deployment using joblib and flask, Deploying on AWS Cloud using S3 and Elastic Beanstalk, Using AWS Sagemaker
End 2 End Project – Building a RoboAdvisor - multi-asset portfolio using global assets and macroeconomic data
Detailed python code and data is provided to explain all concepts and algorithms
Use popular libraries like scikit-learn, xgboost, numpy, matplotlib, seaborn, joblib, flask, etc