The Objective Of This Course is To

• Make a learner ready for solving real life problems using ML
• Understand all concepts via coding for ML implementation
• Participate in Kaggle competitions
• Be ready to learn “Advanced Machine Learning” and “deep learning” in future
• Code in Python and complete understanding of all ML algorithm’s

Module 1
Module 2
Module 3
Module 4
Module 5
Module 6
Module 1

Terminology & Concepts

• Why Machine learning and what is it?
• What is an Error function or loss function like Gradient Descent mean?
• Some simple real life examples on :
• Linear regression – Predicting the Price of a House.
• Logistic regression – Classifying diabetic people from healthy ones.
• Decision Tree – like Google Play store Recommending Apps to us.
• Naive Bayes – like detecting Spam mails on Gmail / yahoo.
• KNN – help Domino’s to open 2 new outlets in your locality.
• 2 Types of Machine Learning : Supervised & Unsupervised
• Machine Learning work flow steps.

Module 2

Python For Data Science

• NumPy basics for Data Science
• Pandas for Data Analysis
• Matplotlib for Data Visualization
• Scikit-Learn for Data Science

Module 3

Processing, Wrangling, And Visualizing Data

• Handling Missing Values
• Handling Duplicates
• Encode Categorical
• Normalizing Numeric Values
• Data Summarization

Module 4

Feature Engineering And Selection

• Feature Engineering Numeric Data
• Feature Engineering Categorical Data
• Feature Engineering Text Data
• Feature Scaling
• Feature Selection

Module 5

Machine Learning Algorithms For Supervised And Unsupervised Learning

— Supervised Algorithms – Maths part + Python Coding

• Naive Bayes Classification
• Linear Regression
• Support Vector Machines
• Decision Trees
• Random Forests
• KNN (K-Nearest Neighbors)

— Unsupervised Algorithms –Maths part + Python Coding

• k-Means Clustering
• Principal Component Analysis

Module 6

Applying Knowledge To Solve Real World Problems

Project 1: Consumer complaint classification for a Fin-Tech Company

Dataset source: Kaggle

Project 2: Doing Sentiment Analysis of live Twitter feeds for any current hot topic

Dataset source: extracted live through Twitter API

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