KIT Workshop on

Faculty Training on ‘Data Science & Analytics’

30 March 2020 - 08 Aptil 2020


The objective of this course is to impart knowledge about basic data science and application in Industries.

Machine Learning ( Duration: 60 Hours)

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
Basics of Python Basic programming syntax of Python, File Handling 3 2
Fundamental of Machine Learning Categories Supervised Learning 1 1
Unsupervised Learning
Reinforcement Learning
Working with Data Reading Files, Scraping the Web, Cleaning, Munging and Manipulating 2 2
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 2
Supervised Machine Learning Regression 1 1
Generalization, Overfitting and Underfitting
UnSupervised Machine Learning Challenges in unsupervised learning 1 1
Supervised Machine Learning Algorithms Linear models 2 2
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 Understand how continuous supervised learning is different from discrete learning 2 1
Code a Linear Regression in Python with scikit-learn
UnSupervise ML - Clustering k-Means clustering 2 1
Introduction Neural Networks Genearl introduction of Neural Networks-Learning And Generalization, Overview of Deep Learning 1
Prepare Your Data For Machine Learning Need For Data Pre-processing 2 2
Data Transforms
Rescale Data
Standardize Data
Normalize Data
Binarize Data
Evaluate the Performance of Machine Learning Algorithms with Resampling Evaluate Machine Learning Algorithms 2 2
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.
Which Techniques to Use When
Examples of Real-time application of ML Weather forecasting, Stock market prediction, Object recognition, Real Time Sentiment Analysis, etc. 2 4
Industrial use case walk through Industrial use case walk through for supervised learning 2 4
Industrial use case walk through for unsupervised learning


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