Data Analytics & Machine Learning with R Programming

Applied Machine Learning with Python & Analytics

About this Course:

Machine Learning is the most used technology these days due to its ability to automate tasks, detect patterns and learn from Data. Python is the language that is extensively used in Machine Learning & Data Analytics applications. Therefore, this course is designed in such a way that the participants can learn Python Basics, Data Analytics & Machine Learning together in a specified timeframe. The course begins with Python, covering its fundamentals to certain advanced concepts, followed by an in-depth discussion on using various Python libraries for Data Analytics applications. Next, the course explores Machine Learning in detail, covering both Supervised and Unsupervised Machine Learning algorithms. The hands-on approach adopted here will enable participants to gain proficiency in using Python libraries and Machine Learning models for real-world applications. The skills and certification acquired through this course will enhance employability and open doors to exciting career prospects.

What you will Learn:

Python Programming from the fundamentals.
Data Analytics with Python Programming (with Hands-on projects)
Machine Learning, covering both Supervised & Unsupervised Learning algorithms (with Hands-on projects)

Course Objectives:
●   To enable participants to understand the fundamentals of Python programming and its wide range of applications.
●   To make the participants efficient in using Python Libraries in different Data Analytics applications.
●   To familiarize participants with a range of Machine Learning Algorithms/Models along with their strengths and weaknesses.
●   To help participants become comfortable with using predictive models through Projects and Assignments.
●  To equip participants with the ability to select and apply suitable algorithms and techniques to solve real-world problems using Machine Learning.

Batch Details:
Class Timings: 4:00 pm – 6:00 pm (Saturday & Sunday) Start Date: 21st March 2026
Duration: 78 Hours End Date: 09th Aug 2026
Mode: Online Last Date to Register: 20th March 2026

Course Fee: : Rs. 11,000/- (Amounts inclusive of GST)

Course Highlights:
•   Industry-Relevant and high-in-demand Skills.
• Hands-on based learning experience through practical projects.
•   Full-time access to recorded lectures/PPTs/PDFs/Study Materials.
•   Session on Resume Preparation/Interview Preparation.

Course Overview:

Module 1: Basics of Python Programming

Introduction to Python
Python objects with details of Numbers/Variables/Strings/Lists/Dictionaries/Tuples etc.
Variable Assignment, Indexing & Slicing
Comparison & Logical operators
Range, List Comprehension
Functions, Lambda expressions.
Object Oriented Programming with Python, -class, Attributes, Inheritance, Polymorphism

Module 2: Data Analytics with Python Libraries

Introduction to NumPy
Random functions, Reshape, & Arithmetic operations.
Introduction and working with Pandas
Selecting a single column, important series methods
Inspecting DataFrames, filtering with conditional operators
Adding & removing columns; updating values, working with date & time

Module 3: Data Visualization

Working with Matplotlib Library; Working with different plots
Working with Text
Concatenating Series & DataFrames
Working with Seaborn Library
Seaborn categorical plots
Capstone projects on Data Analytics.

Module 4: ML Basics & Supervised Learning

Machine Learning basics, introduction to Supervised & Unsupervised learning.
Linear Regression for One and Multiple Variables, Cost Function & Gradient Function
Ordinary Least Square, Dummy Variables, One Hot Encoding
Polynomial Regression and its applications
Anscombe’s quartet, Performance Metrics like Mean Absolute Error, Root Mean Squared Error, - Regularization (Ridge & Lasso)
Logistic Regression;Confusion Matrix, interpreting parameters like F-1 score, Accuracy, Precision, Recall etc.

Module 5: Supervised Learning & Natural Language Processing (NLP)

Decision Trees using rpart, C50
k-Nearest Neighbors with class and caret
Random Forest with randomForest package
Naive Bayes Classifier
Basics of Natural Language Processing (NLP) in R
Text Mining with tm, tidytext, quanteda
Creating Document Term Matrices and using TF-IDF

Module 6: Unsupervised Learning with R

Introduction to Clustering in R
K-Means Clustering
Hierarchical Clustering with Dendrograms
DBSCAN using dbscan package
Dimensionality Reduction with PCA
Hands-on Unsupervised Learning Project with R

Prerequisites and eligibility:

No coding experience in any programming language required. We’ll start from scratch.
This course can be taken up by any undergraduate/postgraduate student of Basic & Applied Sciences, Engineering, Management and Computer Applications and also by Research Scholars/Faculties/Working Professionals who want to upskill themselves.
Participants need to have a laptop/PC (with a minimum of 4 GB RAM, 100 GB HDD, Intel i3 processor) and proper internet/Wi-Fi connection.

Contact Person: Dr. Subrat Kotoky

Email: [email protected] / [email protected]

Phone: 9085317465 / 8473874389

Expert Profile: Mr. Shreyas Shukla

Professional Corporate Trainer & Microsoft Azure Certified Data Engineer

M.Tech-IIT Kharagpur & BE- The Aeronautical Society of India, New Delhi

Has successfully conducted 25+ courses and trained 1500+ learners in the fields of Python Programming, Data Analytics, Machine Learning, Deep Learning, Computer Vision etc. till now.

(Total Experience in conducting Professional Courses: 4+ Years)

Certifications: