Build and optimize ML Models from a range of Supervised, Unsupervised, Regression and Classification Algorithms

What you'll learn:
Machine Learning Core Concepts in Detail
Understand use-case scenarios for applying Machine Learning
Detailed coverage of Python for Data Science and Machine Learning
Regression Algorithm - Linear Regression
Classification Problems and Classification Algorithms
Unsupervised Learning using K-Means Clustering
Exploratory Data Analysis Techniques
Dimensionality Reduction Techniques (PCA)
Feature Engineering Techniques
Model Optimization using Hyperparameter Tuning
Model Optimization using Grid-Search Cross Validation
Introduction to Deep Neural Networks

Requirements:
Some exposure to Programming Languages will be useful

Homepage