KIT Online

FDP on Machine Learning

Batch A
09 - 20 November 2020
   Batch B
23 November - 04 December 2020


This course aims to provide a concise introduction to the fundamental concepts in machine learning including mathematical foundations, programming tools and packages and popular machine learning algorithms. The participants will gain knowledge in Machine Learning principles through a lot of practical applications covering industrial case walk-throughs and real-time applications.

Topics to be discussed

Sr. No. Topic Sub Topic Theory (Hours) LAB (Hours)
Welcome to Machine Learning Introduction To Machine Learning 2 1
Introduction of Data Science, ML and AI
Concept of Data Science - What/When/Why
Artificial Intelligence Evolution
Find out where Machine Learning is applied in Technology and Science. Applications in real scenarios
GUI Based Machine Learning Development
Basics of Python Basic programming syntax of Python, File Handling 2 3
Fundamental of Machine Learning Categories Supervised Learning 1
Unsupervised Learning
Reinforcement Learning
Working with Data Reading Files, Scraping the Web, Cleaning, Munging and Manipulating 2 3
Visualizing Data Bar Charts, Line Charts, Scatterplots 1 1
Statistics:Describing a Single Set of Data Correlation and Causation 1
Mathematical Foundations Dependence and Independence, Conditional Probability, Bayes’s Theorem, Random Variables, Continuous Distributions, The Normal Distribution 3
Machine Learning Python Packages Data Analysis Packages 3 3
Supervised Machine Learning Regression 1
Generalization, Overfitting and Underfitting
UnSupervised Machine Learning Challenges in unsupervised learning 1
Supervised Machine Learning Algorithms Linear models 2 3
k-Nearest Neighbor
Naive Bayes Classifiers
Decision trees
Supervise ML - Classification Classification - LogisticRegression, Sigmoid Function 1 1
Bagging and Boosting Random Forest 1 1
Supervised ML - Regression Code a Linear Regression in Python with scikit-learn 1 2
UnSupervise ML - Clustering k-Means clustering 1 2
Introduction Neural Networks Genearl introduction of Neural Networks-Learning And Generalization, Overview of Deep Learning 1 2
Prepare Your Data For Machine Learning Need For Data Pre-processing 1 3
Data Transforms
Rescale Data
Standardize Data
Normalize Data
Binarize Data
Feature Engineering
Feature Selection
Evaluate the Performance of Machine Learning Algorithms with Resampling Evaluate Machine Learning Algorithms 1 3
Split into Train and Test Sets
K-fold Cross Validation
Leave One Out Cross Validation
Repeated Random Test-Train Splits
Understand different error metrics such as MSE and MAE in the context of Machine Learning.
Practice Advice on Which Techniques to Use and When for specific scenarios
Examples of Real-time application of ML Weather forecasting, Stock market prediction, Object recognition, Real Time Sentiment Analysis, etc. 1 1
Industrial use case walk through Industrial use case walk through for supervised learning 3 1
Industrial use case walk through for unsupervised learning


Dr. Amey Karkare
(Associate Professor, Dept. of Computer Science and Engineering)


Registeration will be done by nomination. The nominations should be sent by the TEQIP coordinator/Director/HOD of the institute/department. There is no registration fees for participants from TEQIP institutes.

The course will be held in two batches.
Batch A: November 09 – November 20
Batch B: November 23 – December 04
Please mention which batch the nominated faculty will attend.

We look forward to enthusiastic participation from your esteemed institute. TEQIP Coordinator/Head of the departments of the institute are requested to send the nomination for this course in the following format:

S.No Applicant’s Name Batch (A/B) Designation Department Email Mobile

Please email these details to, with subject “Machine learning 2020”. There are limited seats, please send your registration as soon as possible.

Selection will be made on first-come-first-serve basis. If selected, you will be sent a confirmation email. Your expression of interest in participation is confirmation of your participation unless communicated otherwise.


Sr. No. Name
1. Dr. Amey Karkare, CSE, IIT Kanpur
2. Dr. Arnab Bhattacharya, CSE, IIT Kanpur
3. Industry Talks , (Algo8, RCPL, Google Research)

selected participants

Sr. No. Name Institute Name Sr. No. Name Institute Name
Prof. Annappa NIT Karnataka, Surathkal