Master of Science in Machine Learning & Artificial Intelligence

Course material for Master of Science in Machine Learning & Artificial Intelligence by UpGrad in collaboration with Indian Institute of Technology, Madras & Liverpool John Moores University.

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Learn to deploy Machine Learning models using Cloud computing with an M.Sc. Degree from Liverpool John Moores University, and an Advanced Certification from IIT Madras.

Content Formatting

Content accross the course in different courses and modules are mostly .md and .ipynb formats with a few .pdf documents and .sql files for database related modules. Highly recomend to download the entire repo and view content in respective IDE or browser.

PS: This respository is not meant to promote plagarism or to be used as an alternative to actually taking up the course. Purpose of this repository is only to be used as a reference for the course. The content of this repository is intended to be used as an additive not a replacement for actual studying or practicing course content. Please use this as content reference only and if interested do enroll through the course. A basic honor system is expected to be maintained everyone.

Contents

• Course 1 - PreLaunch Preparatory Content
  • Module 1 - Introduction to Python Programming
    • Basics of Python
    • Data Structures in Python
    • Control Structures and Functions
    • Object-Oriented Programming in Python
  • Module 2 - Python Libraries
    • NumPy
    • Pandas
  • Module 3 - Data Visualisation in Python
    • Introduction to Data Visualization with Matplotlib
    • Introduction to Data Visualization
    • Data Visualisation with Seaborn
  • Module 4 - Math for Machine Learning
    • Introduction to Probability
    • Introduction to Linear Algebra
    • Linear Transformations and Matrices
    • Differential Calculus
  • Uber Case Study
• Course 2 - Introduction to Machine Learning and Cloud
  • Module 2 - Introduction to Machine Learning
    • Understanding Machine Learning
    • The Machine Learning Pipeline
    • The Apriori Algorithm : Theory
  • Module 3 - Exploratory Data Analysis
    • Data Sourcing
    • Data Cleaning
    • Univariate Analysis
    • Bivariate and Multivariate Analysis
  • Module 4 - Basics of RDBMS and SQL Querying
    • RDBMS and its Features
    • Basic SQL Querying
    • Advanced SQL Querying
    • Practice Workbench
  • Module 5 - Inferential Statistics and Hypothesis Testing
    • Introduction to Probability
    • Probability and Probability Distribution
    • Central Limit Theorem and Confidence Interval
    • Hypothesis Testing with a Single Population
    • Hypothesi Testing with Two Populations
    • A/B Testing Case Study
    • Applications of Sampling Methods
  • Case Study - Bank Loan Default Risk EDA
• Course 3 - Big Data and Cloud
  • Module 1 - Introduction to Cloud
    • Why Cloud?
    • Deployment and Service Models
    • Amazon Web Services
  • Module 2 - Working with AWS
    • AWS CLI
    • AWS Services
    • Applications in AWS
  • Module 3 - Hadoop Framework
    • Hadoop Distributed File System
    • MapReduce and YARN
    • EMR CLuster
  • Module 4 - Hive and Querying
    • Introduction to Hive
    • Basic Hive Queries
    • Advanced Hive Queries
  • Case Study - Data Warehousing and Data Management
    • Case Study Part I
    • Case Study Part II
    • E-Commerce Assignment
• Course 4 - Big Data Processing using Apache Spark
  • Module 1 - Introduction to Spark
    • Basics of Spark
    • RDD Programming I
    • RDD Programming II
  • Module 2 - Spark Structured APIs
    • Introduction to Structured APIs
    • Optimisation in Structured APIs
    • Structured APIs Usage
    • Basic EDA using Spark ML library
  • Module 3 - PySpark Implementation Streaming and ALS
    • Spark Streaming
    • Twitter Hashtags Analysis
    • Recommendation System
    • ALS Implementation
  • Market Basket Analysis Assignment
• Course 5 - Machine Learning - I
  • Module 1 - Linear Regression
    • Introduction to Linear Regression
    • Linear Regression Model Building
    • Linear Regression using PySpark
    • Linear Regression Case Study
    • Industry Relevance of Linear Regression
  • Module 2 - Logistic Regression
    • Introduction to Logistic Regression
    • Logistic Regression - Model Building with Python
    • Logistic Regression - Model Evaluation with Python
    • Logistic Regression - Model Building with PySpark
    • Bias-Variance Tradeoff and Regularization
    • CTR Improvement Assignment Solution
  • Credit Card Application Approval Model
• Course 6 - Machine_Learning - II
  • Module 1 - Decision Trees
    • Introduction to Decision Trees
    • Implementing Decision Trees
  • Module 2 - Ensembles and Random Forest
    • Ensemble Learning With Random Forest
    • Implementation of Random Forest
    • Random Forest in Spark
  • Module 3 - E-Commerce Churn Case Study
  • Module 4 - Principal Component Analysis
    • Introduction to PCA
    • Basis and Python Demonstration of Matrices
    • PCA Algorithm
    • Movie Recommendation using PCA
  • Module 5 - Clustering
    • Introduction to Clustering and K-Means Algorithm
    • Executing K-Means in Python
    • Hierarchical Clustering and Case Study
    • Clustering Using PySpark
    • DBSCAN and Other Forms of Clustering
• Course 7 - Deep Learning
  • Module 1 - Introduction to Neural Nets with TensorFlow
    • Structure of Neural Networks
    • Feedforward Neural Networks
    • Backpropagation in Neural Networks
    • Fundamentals of TensorFlow
    • Modifications in Neural Networks
• Docs
  • Amazon Web Services
  • Course Overview
  • Instalation Guides

Programing Languages and Tools covered

• python
• TensorFlow
• MySQL
• Apache HIVE
• Apache Spark
• Amazon Web Services
• Apache Hadoop

Partner Platforms & Universities

UpGrad

Indian Institute of Technology Madras

Liverpool John Moores University