Machine Learning With Python
IIIT-Bhagalpur offers Certified project based learning Program in “Machine Learning with Python”

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Machine Learning is field of computer science that uses statistical techniques to give computer systems the ability to learn with Data without explicitly being programmed. This course exposes you to different classes of machine learning algorithms like supervised, unsupervised and reinforcement algorithms. This course imparts you the necessary skills and practical exposure on skillsets like data pre-processing, dimensional reduction, model evaluation and also exposes you to different machine learning algorithms like regression, clustering, decision trees, random forest, Naive Bayes and Q-Learning.
Especially developed in collaboration with academia & AI Industry experts to reskill and retool working professionals towards Artificial Intelligence space, this program offers the benefits of Experienced Faculty Led Sessions: Live-Interactive Online classes and practical exposure. Program content & structure designed in collaboration with faculty and Industry experts.

Session: 2 hours/day - 2 days a week (weekend) Batch Size: 50 Students

6
Interactive Modules

50
Hours of Learning

2
Industry Projects

6
Assignments

This course is focused on building industry-ready professionals who can work on Machine Learning & it will provide in-depth understanding of Artificial Neural Networks, Data Analytics for Machine Learning and its mechanism. Furthermore, you will be provided with project based Learning which is an important to gain expertise in Artificial Intelligence. With this program, one will be able to automate real life scenarios using Machine Learning Algorithms. As part of the course, practical use cases of Machine Learning with Python programming language will be discussed for better & improved learning experience.

Syllabus Structure

What you will learn from this course


Module 1:

Module 1 : Introduction to Machine Learning
  • Machine Learning Categories
  • Supervised Learning
  • Unsupervised Learning
  • Reinforcement Learning
  • Frameworks for Building Machine Learning Systems
  • Machine Learning Python Packages

Module 2:

Module 2 : Data Analysis Packages
  • NumPy
  • Pandas
  • Matplotlib
  • Machine Learning Core Libraries

Module 3:

Module 3 : Machine Learning Perspective of Data
  • Dealing with Missing Data
  • Handling Categorical Data
  • Normalizing Data
  • Feature Construction or Generation
  • Exploratory Data Analysis
  • Univariate Analysis
  • Multivariate Analysis

Module 4:

Module 4 : Supervised Machine Learning
  • Classification
  • Collecting, preparing and exploring the data
  • Regression
  • Multivariate regression
  • KNN algorithm
  • Classification using Decision Trees and rules
  • The naïve Bayes classification
  • Preparing and exploring data
  • Training a model on the data
  • Evaluating the model performance
  • Logistic Regression
  • Support Vector Machine
  • Artificial Neural Network

Module 5:

Module 5 : Unsupervised Machine Learning
  • Clustering as a machine learning task
  • Hierarchical Clustering
  • K-means
  • Finding Value of k
  • Principal Component Analysis (PCA)
  • Hidden Markov Mode

Module 6:

Module 6 : Convolutional Neural Network
  • Architecture of CNN
  • Types of layers in CNN
  • Building an image classifier using CNN
  • Deep Learning with CNN

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Our Faculty

A.K Sinha
Lead Instructor
DR. DEEPTI YADAV
Instructor
KUNAL GERA
Instructor
DR. R. BHATTACHARYYA
Instructor
DR. SANDEEP RAJ
Instructor
DR. DILIP K. CHOUBEY
Instructor

Testimonials

The course was highly informative and very well presented. It was very easier to follow. Many complicated concepts were clearly explained. It improved my confidence with respect to programming skills.

- Nitesh Singh

I'm extremely excited with what I have learnt so far. As a newbie in Machine Learning, the exposure gained will serve as the much needed foundation to delve into its application to real life problems.

- Mohit Singh

This is a very good start for Machine leaning with Python. I didnt have much idea about ML concepts but this course gave me great understanding on each topic and lot of learning. Awesome Course !!

- Rajesh Chatterji

Course FAQ

Why Enroll for the Machine Learning With Python Program?

Everyone wants to succeed in business, but this fast-moving digital world requires new skills. Whether you are a manager, a product engineer, a business analyst, a consultant, or a student, you will benefit from the skills to gain insights from your data through analytics. As the top-ranked programming language, Python allows you to analyze very large data sets and create visualizations to move you and your organization forward. Whether you are a first-time programmer or someone with experience in other languages, the Python for Data Analytics certificate program will give you the foundation to move ahead with confidence.

Who Is This Program For?

Engineers, Marketing & Sales Professionals, Freshers, Domain Experts, Software & IT Professionals

What skills do I need to become a Data Scientist or Machine lEarning Specialist?

The core skills for data analysis work include: SQL, Programming in Python (or R), Data Cleaning, Data Analysis, Data Visualization, Statistical Analysis etc.

How Python is used in data science and machine learning?

That makes Python a must-have tool not only for data analysis but for all data science. You can make the data more accessible and easier-to-use by means of creating various charts and graphics, as well as web-ready interactive plots. Yes, Python provides you with the capability to get a good sense of data.

What are the Eligibility criteria?

The minimum eligibility criteria for a Data Analytics course is Bachelor's degree with at least 50% marks in aggregate or equivalent preferably in Science or Computer Science from a recognised university, No coding experience required.
Required Skill-Set for Data Analytics - To be good at data analysis, one needs to have strong analytical and numerical skills and must have a thorough understanding of computer software(s) like Querying Language (SQL, Hive, Pig), scripting Language (Python, Matlab), Statistical Language (R, SAS, SPSS), and Excel.

What will be learning outcome of this course?

  • Participants will be able to understand and use python data science libraries as a tool for data analytics
  • Participants will be able to create Python codes for the above techniques
  • Participants will be create visualizations using python
  • Job opportunities: Data Analyst, Data Scientist, Data Engineer, Product Analyst, Machine Learning Engineer, Decision Scientist

What if I miss the class?

Hey, No need to worry! Our in-house Learning Management System will provide you with recordings of every lecture.
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