Unit 1

Python Basics, Running Python Scripts, Types of Data, Central Tendency: Mean, Median, and Mode, Variation and Standard Deviation, Probability Density Function, Probability Mass Function, Linear Regression, Polynomial Regression, Multi-Level Models.

Unit 2

Introduction to ML, Supervised and Unsupervised Learning, Train/Test, Classification and Regression, Bayesian Methods, Implementing a Spam Classifier with Naive Bayes, K-Means Clustering, Decision Trees, Ensemble Learning, Support Vector Machines (SVM)

Unit 3

Collaborative Filtering, Making Movie Recommendations to People, K-Nearest Neighbours, Using KNN to predict a rating for a movie, Dimensionality Reduction and Principal Component Analysis, Data Warehousing ETL and ELT, Dealing With Real-World Data, Bias/Variance Trade-off, K-Fold Cross Validation to Avoid Overfitting, Cleaning Web Log Data

Unit 4

Installing Spark, Spark Introduction, Spark and the Resilient Distributed Dataset (RDD), Introducing MLLib, Decision Trees in Spark, K-Means Clustering in Spark, TF/IDF, Searching Wikipedia with Spark, Using the Spark 2.0 Data Frame API for MLLib.

Unit 5

Deep Learning Overview, The History of Artificial Neural Networks, Deep Learning in the TensorFlow Playground, Deep Learning Details, Keras Framework, Using Keras to Predict Political Affiliations, Convolutional Neural Networks (CNN’s), Using CNN’s for handwriting recognition, Recurrent Neural Networks (RNN’s), Using an RNN for sentiment analysis

Pre-Requisites

23CSE106 – Computer Programming and Algorithmic Problem Solving, 23MAT213 Foundations of Data Science 

Course Objectives

1. Explore topics in Statistical Methods useful for Machine learning Algorithms.
2. Understand the Predictive Modelling Techniques
3. Demonstrate various Machine Learning Algorithms with Various Python Supported Libraries
4. Implement Recommender Systems with Collaborative Filtering Techniques.
5. Explore Neural Networks and Deep Learning Methods.

Course Outcomes

• CO1 :  Demonstrate understanding of Statistical Methods and Predictive Modelling techniques.
• CO2:   Demonstrate understanding of various Machine Learning Algorithms using case studies.
• CO3:   Demonstrate ability to construct the Recommender Systems in the context of Collaborative Filtering Techniques.
• CO4:   Demonstrate ability to evaluate ML models and apply various best practices to get optimized results CO5: Demonstrate ability to use Deep Learning models and neural networks with TensorFlow.

CO-PO-PSO Mapping

(Affinity: 3- High, 2- Moderate, 1- Slightly)

1. Hands-On Data Science and Python Machine Learning, Kane, F. (2017), Packt Publishing.
2. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems ,3rd ed., Géron. A, O’Reilly Media.
3. ” Spark: The Definitive Guide,” by Bill Chambers, Matei Zaharia, O’Reilly Media, Inc.
4. ” Practical Recommender Systems ” by Kim Falk.
5. “Deep Learning from Scratch: Building with Python from First Principles” by Seth Weidman, published by O’Reilly

E-learning content on L&T Edu Tech Platform

*CA – Can be Quizzes, Assignment, Projects, and Reports