Interested in this College?
Get updates on Eligibility, Admission, Placements Fees Structure

Quick Facts

Medium Of Instructions	Mode Of Learning	Mode Of Delivery	Frequency Of Classes
English	Self Study, Virtual Classroom	Video and Text Based	Weekends

Course Overview

The Certificate Program in Machine Learning and AI with Python online course is a short-term accreditation programme that will last for six months. This course is curated by the prestigious Indian Institute of Technology, Bombay, to help students learn about the different aspects of Machine Learning and AI.

Moreover, the Certificate Program in Machine Learning and AI with Python accreditation trains the working professionals and students in neural networks, decision trees, algorithms, classification, regression, etc. to build and enhance their skills in the most popular programming language Python.

Furthermore, in the Certificate Program in Machine Learning and AI with Python online accreditation, you will have live online lessons during the weekend to use your time efficiently during the weekend and manage your job.

You need to be a graduate or a diploma holder with a minimum of one year of experience to pursue this programme. Also, you need to have a working knowledge of C++, or Java, or MATLAB. Besides, you need to be well acquainted with the concepts of linear algebra, statistics, and calculus to make the most out of this course.

The Highlights

Six-months course
Live online lessons
Certification available
Offered by IIT Bombay
Practical course
Skilled faculty

Programme Offerings

Live online learning
Short-term course
Self-paced learning
Certification
Skilled faculty

Courses and Certificate Fees

Certificate Availability	Certificate Providing Authority
yes	IIT Bombay

Interested applicants need to pay a sum of Rs. 1,25,000 plus GST to enroll in the Certificate Programme in Machine Learning and AI with Python.
You can pay online. You have the option to pay in installments too.

Certificate Program in Machine Learning and AI with Python fee structure

Course Name	Fee in INR
Certificate Programme in Machine Learning and AI with Python	Rs. 1,25,000 + GST

Eligibility Criteria

To be eligible for the Certificate Program in Machine Learning and AI with Python certification, you need to be a graduate or a diploma holder with at least one year of experience. Also, you need to know C++ or Java, or MATLAB. Besides, you should be thorough with the concepts of linear algebra, calculus, and statistics.

What you will learn

Knowledge of AlgorithmsKnowledge of PythonKnowledge of Artificial IntelligenceKnowledge of NLP ModellingKnowledge of Numpy

In the Machine Learning and AI with Python online accreditation programme, you will learn the following:

Python, NumPy, and Pandas
Scikit and SciPy
ML Algorithms
Natural Language Processing
Reinforcement Learning
To form predictive models using neural networks and decision trees.
To build DataFrames from scratch.
To create text classification systems with NLP with linear classifiers and deep learning methods.
To visualise data with Matplotlib.
Various Optimisation techniques
To build AI models.

Who it is for

Software Developer

The online Certificate Program in Machine Learning and AI with Python is a specialised course in machine learning and AI with python that is suitable for the following individuals:

Working professionals
Managers who are leading a team of software developers
Software developers
People who are leading a machine learning-related project
People who know programming languages like C++ or Java or MATLAB
People who want to upskill

Admission Details

Step I: You can go through all the information on the Certificate Program in Machine Learning and AI with Python online certification here: https://www.eruditus.com/india/programmes/certificate-program-in-machine-learning-and-ai-with-python/#collapse_sly0.

Step II: After reading all the details, click on the ‘Apply Now’ button to proceed further.

Step III: Provide the necessary details, pay the fee, and start learning according to your batch commencement date.

The Syllabus

Statistics
Probability
Linear Algebra

Numerical Computing with Python (NumPy, Matplotlib)
Introduction to Pandas for data import and export (Excel, CSV etc.)
Basic Introduction to Scikit learn

Introduction to Machine Learning with applications to different domains
Supervised Learning
Unsupervised Learning
Semi-Supervised Learning
Reinforcement Learning
Idea of hypothesis space
Machine learning as hypothesis selection problem
Finite and Infinite hypothesis space
Complexity of the hypothesis space
The Idea of training, testing, and validation
Cross-Validation

Introduction to the Linear Regression Analysis problem with examples
Solving Linear Regression using Matrix Inversion and Gradient Descent Based Approaches
The Idea of Regularisation
Lasso, ridge, and elastic net regularization
Bias-Variance trade-off
Underfitting and Overfitting of models

Idea of empirical risk minimization principle
Idea of generalization
Lazy and active learners
K nearest neighbor classification
Linear discriminant analysis

Bayesian approaches for classication
Naïve Bayes’ algorithm
Gaussian Discriminant Analysis
Parameter estimation using MLE, MAP, Idea of EM algorithm for GMM

Tree based classification,
Decision Tree,
ID3 algorithm for designing decision trees,
Decision Tree for regression, Regularization in Decision Tree, Random Forest

Support Vector machines
Margin Based classification
SVM and linearly and nonlinearly separable cases
Idea of Kernels
Multi-class SVM
Examples using LIBSVM

Introduction to Neural Networks,
Biological neuron model and extension to artificial neuron models
McCulloch-Pitts model
Multi-layer perceptron
Back-propagation based training of neural networks
Introduction to convolution networks
Idea of different layers in CNN
Discussions on different CNN models for image recognition (Alexnet, VGG, Resnet, Inception Net etc.)
Examples in Tensorflow / Keras

Linear and nonlinear programming,
Gradient based optimization,
Convex optimization

Idea of data clustering and density estimation
K-means
Fuzzy C Means
Mean-Shift
DBSCAN clustering techniques
Implementation of K-means in Python

Machine Learning techniques in NLP
Language modelling

Supervised and Unsupervised feature selection
PCA
ICA
Implementation of PCA in Python

Introduction to RL,
Example of RL models,
Markov Decision Process,
Policy and Value Iterations,
Bellman Equation,
Temporal Difference Learning,
Q Learning,
Introduction to deep RL

ML in Finance: Prof. Piyush Pandey (SOM)
ML in computer vision: Vinay Namboodiri (IITK)
ML in speech and text processing: Preethi Jyoti (CSE)

Instructors

IIT Bombay Frequently Asked Questions (FAQ's)

1: When will the classes be conducted?

The classes will be held during the weekend to save you time and provide you flexibility.

2: How long will the course last?

The Machine Learning and AI with Python by Eruditus are spread across six months.

3: Who has developed the course?

Indian Institute of Technology, Bombay, has created the course with the help of Eruditus.

4: Do I need to be a graduate to pursue it?

To enrol in the Machine Learning and AI with Python accreditation programme, you need to be a graduate or a diploma holder with a minimum of one year of experience.