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What you’ll learn
Master Machine Learning using Python
Demystify Artificial Intelligence, Machine Learning, Data Science
ML Business Solution Blueprint
Explore Spyder, Pandas and NumPy
Implement Data Engineering and Data Analysis
Introduction to Statistics and Probability Distributions
Understand Supervised and Unsupervised Learning
Implement Simple & Multiple Linear Regression
Regression & Classification Model Evaluation
Cross Validation, Hyperparameter, Ensemble Modeling, Random Forest & XGBoost

Overview of Contents
The Bigger Picture
The Problem Landscape
Defining Data Science
Demystifying AI-ML-Data Science
Exploring the Data Scientist’s Toolbox

“Introduction to Data Scientist’s Toolbox
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Overview of Contents
Quick recap of Python
Python 2.7 vs Python 3.5
Installation & Setup
Datatypes Overview
Spyder tour
Datatypes demo
Datatypes- Numpy
Data Engineering
Data Visualization

“Exploratory Data Analysis, Feature Engineering and Hypothesis Testing
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Overview of Contents
Machine Learning Methodology
Exploratory Data Analysis
Univariate Analysis
Bivariate Analysis
Feature Engineering
Introduction to Statistics
Probability Distributions

“Machine Learning
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Overview of Contents
Introduction to Machine Learning
Supervised Learning
Simple & Multiple Linear Regression
Regression Demo
Classification – Logistic Regression
Classification Logistic Regression Demo
Decision Trees
Decision Trees Demo
Unsupervised Learning – Clustering
Unsupervised Learning Clustering Demo
Unsupervised Learning -Association Rules
Model Evaluation – Regression
Model Evaluation – Regression Demo
Model Evaluation – Classification
Model Evaluation – Classification Demo
Regularization & Hyperparameter tuning
Bias Variance Tradeoff
Cross Validation
Hyperparameter Tuning
Cross Validation , Hyperparameter Demo
Ensemble Modeling
Random Forest [Bagging]
XGBoost [Boosting]
RF & XGB Demo

“Capstone Project
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Overview of Contents
Project Use Case Overview
Defining the Problem Statement
Business Solution Blueprint
Explore & Define a ML use case
EDA and Feature Engineering
Approach for Model Development, Evaluation, Optimization