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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 PG Certification in AI and Machine Learning online course is a study on the different aspects and techniques involved in the principles of artificial intelligence and machine learning. The course is provided by Intellipaat with the faculty and certification offered by the E&ICT academy and Malaviya National Institute of Technology, Jaipur. This online program is provided in collaboration with the leading innovators of the industry, IBM and Microsoft. The program is scheduled for the students to be completed by nine months with independent learning videos, live sessions, and projects.

The course curriculum is robustly designed to gain industrial exposure and knowledge to overcome technical and technological challenges with AI and ML. The students of this program will acquire various skills and will get familiarised with the software tools to improve AI and ML techniques. The students after completing the ‘PG Certification in AI and Machine Learning program are provided with benefits and certification for skill development.

The Highlights

Online mode
Twelve months classes
Live sessions
MNIT Faculty
Mentorship
Alumni Support
Certifications

Programme Offerings

122 Hours Of Videos
More Than 80 Live Sessions
assignments
Projects
professional support
Interviews
Profile Building
Counselling Session
Career Development
Course Certification

Courses and Certificate Fees

Certificate Availability	Certificate Providing Authority
yes	MNIT JaipurIBMMicrosoft Corporation

The students applying for the ‘PG Certification in AI and Machine Learning’ course should pay the admission fee to secure the seat.
No Cost EMI Starts at Rs 4,999

PG Certification in AI and Machine Learning fee structure

Head	Amount in INR
Course fee	Rs 85,044

Eligibility Criteria

Certificate qualifying details

The students of this program are given the ‘PG Certification in AI and Machine Learning’ certificate by E&ICT and MNIT Jaipur after completion of the course and they can also receive the SQL Certification from Microsoft.

What you will learn

Knowledge of Artificial IntelligenceMachine learningKnowledge of deep learningSQL knowledgeData science knowledge

The PG Certification in AI and Machine Learning syllabus is developed for the students to develop skills such as machine learning models, regressing modeling, classification modeling, Random Forest, Decision Tree Models, K-means clustering, Time-Series Prediction Model, Deep Learning, TensorFlow, Neural Networking, CuPy, Keras, PyTorch, Natural Language Processing, computer vision, and SQL.

By the end of this course, students are ensured with the expertise of artificial intelligence and machine learning through the project experience and understanding of the software functionalities.

Who it is for

Data AnalystSoftware Engineer

The PG Certification in AI and Machine Learning online certification is designed for individuals who wish to develop a career in artificial intelligence and machine learning while it also enables industry professionals to enhance their skills. The course aims to strengthen AI developers to acquire expertise in the state of the art technologies and it benefits software engineers and data analysts. The online program is structured for artificial intelligence engineers and machine learning engineers to develop their careers. This certification will equip the freshers with the knowledge required to pursue a master’s program in this domain.

Admission Details

The students who wish to join the ‘PG Certification in AI and Machine Learning’ online course are required to register online.

Step 1: Find the course page using the link below, https://intellipaat.com/pg-certification-ai-ml-course-mnit/

Step 2: Click on the ‘Apply Now’ link

Step 3: Admission is given to the candidates based on the decision made by the admission panel.

Application Details

The applicants of the ‘PG Certification in AI and Machine Learning’ online training should fill in the application form with their name, phone number, email address, and with stating the objective for taking the certification course.

The Syllabus

Python

Introduction to Python and IDEs
Python Basics
Object Oriented Programming
Hands-on Sessions And Assignments for Practice

Linux

Introduction to Linux
Linux Basics
Hands-on Sessions And Assignments for Practice

PySpark
Python
NumPy
SciPy
Matplotlib
Pandas
Python script
Python variable

Regression Modeling: Logical and Linear
Classification Modeling: K-nearest neighbor, Naïve Bayes Theorem, and Support Vector Machines (SVM)
Random forest and decision tree models
Use of PCA, k-means clustering, and isolated forests for anomaly detection
Time-series prediction model and recommendation system
Selection, evaluation, and interpretation of models

Introduction to neural networking
Backpropagation
Stochastic gradient descent
Deep neural networking and its principles
CNNs, RNNs, LSTMs, and MLPs
GANs and Generative deep learning
Calculus and linear algebra
TensorFlow, CuPy, Keras, and PyTorch

Natural Language Processing (NLP) and text mining

Introduction NLP & text mining
Applications of text mining
How NPL works with text mining
Natural Language Toolkit (NLTK) environment

Data cleaning and Preprocessing

Various tokenizers and Tokenization
Frequency distribution
Stemming, POS tagging, and lemmatization
Bigrams, Trigrams, and Ngrams
Entity recognition

Classification of text

Overview of Machine Learning
Words, term frequency, and count vectorize
Inverse document frequency, text conversion, and confusion matrix
Naive Bayes Classifier

Sentence structuring, language modeling, sequence tagging, and sequence tasks

Language modeling, sequence tagging, and sequence tasks
Predicting the sequence of tags
Syntax trees
Context-free grammar
Chunking
Automatic paraphrasing of texts

Vector space models and semantics

Distributional semantics
Traditional and topic models
Tools for sentence and word embeddings

Dialog systems

Introduction to task-oriented dialog systems
Natural language understanding
Dialog Manager

Basics of computer vision and OpenCV
Use of neural networking for image processing
Classification and clustering of an image using GANs, multitask classifiers, and k-means
Detection of object
Image segmentation
Computer vision trends

Development of large-scale AI apps using various tools and techniques
Build and deploy APIs with FastAPI, Swagger, Paperspace, and Postman
CI/CD pipeline for model production
Use of TensorFlow Lite, TensorFlow.js, and Streamlit to package model
Model production using PyTorch, Spark, and PySpark

Data collection from RSSs, web scraping, and APIs
Data cleaning and transformation for ML systems
Automatic transformation tools
SQL and NoSQL databases to deal with large sets of data
Spark
Pandas
SQL and Spark SQL
ScrappingHub

Introduction to R
R packages
Sorting DataFrame
Matrices and vectors
Reading data from external files
Generating plots
Analysis of Variance (ANOVA)
K-means clustering
Association rule mining
Regression in R
Analyzing the relationship with regression
Advanced regression
Logistic Regression
Advanced Logistic Regression
Receiver Operating Characteristic (ROC)
Kolmogorov-Smirnov chart
Database connectivity with R
Integrating R with Hadoop

Introduction to Git
Architecture of Git
Working with remote repositories
Branching and merging
Git methodology
Git plugin with IDE (Eclipse)

Job Search Strategy
Resume Building
LinkedIn Profile Creation
Interview Preparation Sessions by Industry Experts
Mock Interviews
Placement opportunities with 400+ hiring partners upon clearing the Placement Readiness Test.