what you will learn
hands-on machine learning & deep learning
Course Name : AI Foundation
The beginner's course
In this course you’ll learn about Data Visualization Techniques, Data Handling along with its cleaning, containing large datasets, and Mathematical as well as Statistical concepts used in Machine Learning and Deep Learning.
It will prepare you to start your AI journey and polish your python programming skills.
Mode : Classroom
Course Fees : INR 15,000 + GST
Level : Beginner
Language : English
Software/Hardware required : At least I5 laptop with 4 GB RAM
How to Enroll : Score at least 33% in the Enrollment Test
Total course duration : 24 hours | 16 hours of Training Sessions | 8 hours of Practical Sessions
Session 1:
Overview of the course & Setting up the environment, Python Tools, Data types and Variables
Session 2:
Python Function , Loop ,Class and file Handling
Session 3:
Class Inheritance, Exception Handling and Multithreading
Session 4:
Numerical Analysis using Numpy
Session 5:
Data visualisation using Matplotlib
Session 6:
Knowledge extraction using Pandas
Session 7:
Linear Algebra, Matrices and few concepts of Calculus
Session 8:
Probability & Distribution Models
● Movie Database Analysis
● CricInfo style cricket stats
● Playing with files and classes
● Mathematical Problems
● Healthcare Data Search
● Basic knowledge of any Programming Language
● Basic Knowledge of Statistics, Probability, Matrices and Calculus
● Pass Enrollment test - 1 ( JGET - 1)
COURSEWORK -
● Each course is like an interactive textbook, featuring classroom sessions, quizzes and projects.
PRACTICALS -
● There will be a lab session of 2 hours once in a week on a certain week day apart from the classes where you will be solving the assignments.
TESTS -
There are 2 tests throughout the Foundation course:
● Enrollment Test
● Assessment Test after the completion of Foundation Course
HELP FROM PEERS & TRAINERS -
● Connect with our trainers and other learners with their debate ideas, discuss course material, and get help to master the concepts.
CERTIFICATE -
● You will earn an official recognition of your work by getting certified by Jazari Research Institute of Artificial Intelligence after successful completion of the training.
PLACEMENT ASSISTANCE -
● After the completion of the Foundation Course you will be prepared for interviews and also for project communication. We will arrange some call for interview in ongoing recruitments.
MR. ARNAV ARORA
Lead Trainer at Jazari
Arnav Arora is a graduate in Computer Science and Engineering from SRM Institute of
Science and Technology in Chennai, India. Arnav has over 4 years of machine and deep
learning experience.
Arnav’s area of expertise is natural language processing. He has over 2 years of
research experience and has worked with some of the best academic and industry
research labs in the country.
Arnav is one of the core members of PyData Delhi, one of the biggest data science and
machine learning communities in the world. He’s also an international speaker having
given talks at PyCon and PyData conferences around the world.
Arnav is a contributing author at Skynet Today, a publication aiming to provide informed
coverage of news about Artificial Intelligence in order to fight the overhyped articles
about the capabilities of these techniques.
Course Name : AI Advanced
The advanced foundation with machine learning course
In this course you’ll learn about Python for Data Science and Machine Learning. Python includes Data Visualization, mathematical and statistical concepts used in Machine Learning and Deep Learning, along with Data handling and cleaning, consisting large datasets.
In the Machine Learning program you will be trained to develop new ML models by combining existing models. Applications and common algorithms used in Machine Learning and implementation of advanced Machine Learning models, including prediction and classification. You will also learn a visual approach to understand ML and analysis. Machine Learning is extensively used in financial services, retail, and healthcare.
By the end of this course you will be equivalent to a Machine Learning Engineer.
Mode : Classroom
Course Fees : INR 50,000 + GST
Level : Intermediate
Language : English
Software/Hardware required : At least I5 laptop with 4 GB RAM
How to Enroll : Score at least 50% in the Enrollment Test
How to Pass : Must have aggregate at least 3.3 GP out of 10 throughout the course
Total course duration : 72 hours | 48 hours of Training Sessions | 24 hours of Practical Sessions
Session 1:
Overview of the course & Setting up the environment, Python Tools, Data types and Variables
Session 2:
Python Function , Loop ,Class and file Handling
Session 3:
Class Inheritance, Exception Handling and Multithreading
Session 4:
Numerical Analysis using Numpy
Session 5:
Data visualisation using Matplotlib
Session 6:
Knowledge extraction using Pandas
Session 7:
Linear Algebra, Matrices and few concepts of Calculus
Session 8:
Probability & Distribution Models
Session 9:
Intro to Machine Learning and Partition clustering
Session 10:
Clustering Analysis and its implementations
Session 11:
Hierarchical clustering and model
Session 12:
Linear Regression, the fundamentals of parametric algorithms
Session 13:
Logistic Regression model in Python
Session 14:
Naive Bayes algorithm using the Sklearn python Library
Session 15:
SVM Classifiers
Session 16:
Decision Tree classifier : Learning from Data and its working
Session 17:
Algorithm of Random Forest Classifier
Session 18:
Principal Components Analysis
Session 19:
SVD and LDA
Session 20:
Project 1
Session 21:
Project 2
Session 22:
Project 3
Session 23:
Project 4
Session 24:
Final Test
● Recommendation System
● Stock Price Prediction
● Healthcare: Test based Disease Prediction
● Twitter Feed Analysis
● Search Engine Optimizer
● Customer Segmentation
● Target Marketing
● Basic knowledge of any Programming Language
● Decent Knowledge of Statistics, Probability, and Data Analysis
● Pass Enrollment test - 1 ( JGET - 1)
COURSEWORK -
● Each course is like an interactive textbook, featuring classroom sessions, quizzes and projects.
PRACTICALS -
● There will be a lab session of 2 hours once in a week on a certain week day apart from the classes where you will be solving the assignments.
TESTS -
There are 3 tests throughout the AI Advanced course:
● Enrollment Test
● Assessment Test after the completion of AI Foundation Course
● Assessment Test after the completion of AI Advanced Course
HELP FROM PEERS & TRAINERS -
● Connect with our trainers and other learners with their debate ideas, discuss course material, and get help to master the concepts.
CERTIFICATE -
● You will earn an official recognition of your work by getting certified by Jazari Research Institute of Artificial Intelligence after successful completion of the training.
JOB INTERVIEWS -
● After completion of the AI Advanced Course, JAZARI guarantees at least 3 interviews for you.
PLACEMENT ASSISTANCE -
● After the completion of the Foundation Course you will be prepared for interviews and also for project communication.
MR. ARNAV ARORA
Lead Trainer at Jazari
Arnav Arora is a graduate in Computer Science and Engineering from SRM Institute of
Science and Technology in Chennai, India. Arnav has over 4 years of machine and deep
learning experience.
Arnav’s area of expertise is natural language processing. He has over 2 years of
research experience and has worked with some of the best academic and industry
research labs in the country.
Arnav is one of the core members of PyData Delhi, one of the biggest data science and
machine learning communities in the world. He’s also an international speaker having
given talks at PyCon and PyData conferences around the world.
Arnav is a contributing author at Skynet Today, a publication aiming to provide informed
coverage of news about Artificial Intelligence in order to fight the overhyped articles
about the capabilities of these techniques.
Course Name : AI Expert
The complete course including machine learning, deep learning and invention zone
In this course you’ll learn about Python for Data Science, Machine Learning, and Deep Learning. Python section includes Data Visualization, mathematical and statistical concepts used in Machine Learning and Deep Learning, along with Data handling and cleaning, consisting large datasets.
In the Machine Learning program you will be trained to develop new ML models by combining existing models. Applications and common algorithms used in Machine Learning and implementation of advanced Machine Learning models, including prediction and classification. You will also learn a visual approach to understand ML and analysis. Machine Learning is extensively used in financial services, retail, and healthcare.
The Deep learning section involves understanding the idea behind neural networks and their working along with neural networks in Natural Language Processing (NLP), Image Processing, Business Problems, Video Processing and Object Recognition. You will learn about various types of neural networks including Convolutional and Recurrent Neural Networks along with their uses. Adversarial Learning and Reinforcement Learning are the examples of Neural Networks.
Last part of the course will be given as an invention platform to students for bringing their own ideas to life using deep learning techniques.
Mode : Classroom
Course Fees : INR 1,25,000 + GST
Level : Expert
Language : English
Software/Hardware required : At least I5 laptop with 4 GB RAM
How to Enroll : Score at least 50% in the Enrollment Test
How to Pass : Must have aggregate at least 3.3 GP out of 10 throughout the course
Total course duration : 144 hours | 96 hours of Training Sessions | 48 hours of Practical Sessions
Session 1:
Overview of the course & Setting up the environment, Python Tools, Data types and Variables
Session 2:
Python Function , Loop ,Class and file Handling
Session 3:
Class Inheritance, Exception Handling and Multithreading
Session 4:
Numerical Analysis using Numpy
Session 5:
Data visualisation using Matplotlib
Session 6:
Knowledge extraction using Pandas
Session 7:
Linear Algebra, Matrices and few concepts of Calculus
Session 8:
Probability & Distribution Models
Session 9:
Intro to Machine Learning and Partition clustering
Session 10:
Clustering Analysis and its implementations
Session 11:
Hierarchical clustering and model
Session 12:
Linear Regression, the fundamentals of parametric algorithms
Session 13:
Logistic Regression model in Python
Session 14:
Naive Bayes algorithm using the Sklearn python Library
Session 15:
SVM Classifiers
Session 16:
Decision Tree classifier : Learning from Data and its working
Session 17:
Algorithm of Random Forest Classifier
Session 18:
Principal Components Analysis
Session 19:
SVD and LDA
Session 20:
Project 1
Session 21:
Project 2
Session 22:
Project 3
Session 23:
Project 4
Session 24:
Midterm Assessment
Session 25:
Intro to AI, Bayesian statistics and Turing Test
Session 26:
Different types of Neural Networks and Activation Functions
Session 27:
Classification using Neural networks
Session 28:
Project 5
Session 29:
Intro to Tensorflow with Linear regression
Session 30:
Project 6 - Part 1
Session 31:
Project 6 - Part 2
Session 32:
Project 7 - Part 1
Session 33:
Project 7 - Part 2
Session 34:
Project 7 - Part 3
Session 35:
Project 8 - Part 1
Session 36:
Project 8 - Part 2
Session 37:
Project 9 - Part 1
Session 38:
Project 9 - Part 2
Session 39:
Project 9 - Part 3
Session 40:
Project 10 - Part 1
Session 41:
Project 10 - Part 2
Session 42:
Project 10 - Part 3
Session 43:
Intro to other DL libraries like Theano, keras and Pytorch
Session 44:
Transfer learning using keras
Session 45:
Approaches to Transfer learning
Session 46:
Custom Project - Part 1
Session 47:
Custom Project - Part 2
Session 48:
Final Assessment
● Text classification using ANN
● Finding objects using Tensorflow
● Facial Recognition Model
● Sentiment analysis
● Chatbot design using Neural Nets
● Content generation for Youtube videos
● Facial Expression Analysis
● Image Colorization
● Human Age Estimation
● Style Transfer projects
● Transfer Learning Application
● Custom project
● Basic knowledge of any Programming Language
● Decent Knowledge of Statistics, Probability, and Data Analysis
● Pass Enrollment test - 1 ( JGET - 1)
COURSEWORK -
● Each course is like an interactive textbook, featuring classroom sessions, quizzes and projects.
PRACTICALS -
● There will be a lab session of 2 hours once in a week on a certain week day apart from the classes where you will be solving the assignments.
TESTS -
There are 4 tests throughout the AI Expert course:
● Enrollment Test
● Assessment Test after the completion of AI Foundation Course
● Assessment Test after the completion of AI Advanced Course
● Assessment Test after the completion of AI Expert Course
HELP FROM PEERS & TRAINERS -
● Connect with our trainers and other learners with their debate ideas, discuss course material, and get help to master the concepts.
CERTIFICATE -
● You will earn an official recognition of your work by getting certified by Jazari Research Institute of Artificial Intelligence after successful completion of the training.
JOB INTERVIEWS -
● After completion of the AI Advanced Course, JAZARI guarantees at least 3 interviews for you.
PLACEMENT ASSISTANCE -
● After the completion of the Foundation Course you will be prepared for interviews and also for project communication.
MR. ARNAV ARORA
Lead Trainer at Jazari
Arnav Arora is a graduate in Computer Science and Engineering from SRM Institute of
Science and Technology in Chennai, India. Arnav has over 4 years of machine and deep
learning experience.
Arnav’s area of expertise is natural language processing. He has over 2 years of
research experience and has worked with some of the best academic and industry
research labs in the country.
Arnav is one of the core members of PyData Delhi, one of the biggest data science and
machine learning communities in the world. He’s also an international speaker having
given talks at PyCon and PyData conferences around the world.
Arnav is a contributing author at Skynet Today, a publication aiming to provide informed
coverage of news about Artificial Intelligence in order to fight the overhyped articles
about the capabilities of these techniques.
Course Name : AI Machine Learning
The machine learning course for AI excluding foundation
This course covers the full machine learning syllabus excluding the AI Foundation course topics. So this program is for students who already have basic knowledge of AI principles, python for AI, data visualization and cleanup.
In this course you will be trained to develop new ML models by combining existing models. Applications and common algorithms used in Machine Learning and implementation of advanced Machine Learning models, including prediction and classification. You will also learn a visual approach to understand ML and analysis. Machine Learning is extensively used in financial services, retail, and healthcare.
By the end of this course you will be equivalent to a Machine Learning Engineer.
Mode : Classroom
Course Fees : INR 40,000 + GST
Level : Intermediate
Language : English
Software/Hardware required : At least I5 laptop with 4 GB RAM
How to Enroll : Score at least 50% in the Enrollment Test
How to Pass : Must have aggregate at least 3.3 GP out of 10 throughout the course
Total course duration : 48 hours | 32 hours of Training Sessions | 16 hours of Practical Sessions
Session 1:
Intro to Machine Learning and Partition clustering
Session 2:
Clustering Analysis and its implementations
Session 3:
Hierarchical clustering and model
Session 4:
Linear Regression, the fundamentals of parametric algorithms
Session 5:
Logistic Regression model in Python
Session 6:
Naive Bayes algorithm using the Sklearn python Library
Session 7:
SVM Classifiers
Session 8:
Decision Tree classifier : Learning from Data and its working
Session 9:
Algorithm of Random Forest Classifier
Session 10:
Principal Components Analysis
Session 11:
SVD and LDA
Session 12:
Project - 1
Session 13:
Project - 2
Session 14:
Project - 3
Session 15:
Project - 4
Session 16:
Final Test
● Recommendation System
● Stock Price Prediction
● Healthcare: Test based Disease Prediction
● Twitter Feed Analysis
● Search Engine Optimizer
● Customer Segmentation
● Target Marketing
● Basic knowledge of any Programming Language
● Decent Knowledge of Statistics, Probability, and Data Analysis
● Pass Enrollment test - 1 ( JGET - 1)
COURSEWORK -
● Each course is like an interactive textbook, featuring classroom sessions, quizzes and projects.
PRACTICALS -
● There will be a lab session of 2 hours once in a week on a certain week day apart from the classes where you will be solving the assignments.
TESTS -
There are 2 tests throughout the AI Machine Learning course:
● Enrollment Test (JGET-2)
● Assessment Test after the completion of the course
HELP FROM PEERS & TRAINERS -
● Connect with our trainers and other learners with their debate ideas, discuss course material, and get help to master the concepts.
CERTIFICATE -
● You will earn an official recognition of your work by getting certified by Jazari Research Institute of Artificial Intelligence after successful completion of the training.
JOB INTERVIEWS -
● After completion of the AI Advanced Course, JAZARI guarantees at least 3 interviews for you.
PLACEMENT ASSISTANCE -
● After the completion of the Foundation Course you will be prepared for interviews and also for project communication.
MR. ARNAV ARORA
Lead Trainer at Jazari
Arnav Arora is a graduate in Computer Science and Engineering from SRM Institute of
Science and Technology in Chennai, India. Arnav has over 4 years of machine and deep
learning experience.
Arnav’s area of expertise is natural language processing. He has over 2 years of
research experience and has worked with some of the best academic and industry
research labs in the country.
Arnav is one of the core members of PyData Delhi, one of the biggest data science and
machine learning communities in the world. He’s also an international speaker having
given talks at PyCon and PyData conferences around the world.
Arnav is a contributing author at Skynet Today, a publication aiming to provide informed
coverage of news about Artificial Intelligence in order to fight the overhyped articles
about the capabilities of these techniques.
Course Name : AI Deep Learning
The deep learning course
This course covers the full deep learning syllabus excluding the AI Foundation and AI Advanced course topics. So this program is for students who already have basic knowledge of AI principles, python for AI and machine learning algorithms. In this course you will learn different types of Neural Networks and where they are used, including: Convolutional and Recurrent Neural Networks, Q-Learning and Reinforcement Learning. You will understand neural networks in Natural Language Processing, Image Processing, Video Processing, Object Recognition and Consumer Behaviour.
By the end of this course you will be equivalent to a Deep Learning Engineer
Mode : Classroom
Course Fees : INR 75,000 + GST
Level : Expert
Language : English
Software/Hardware required : At least I5 laptop with 4 GB RAM
How to Enroll : Score at least 50% in the Enrollment Test
How to Pass : Must have aggregate at least 3.3 GP out of 10 throughout the course
Total course duration : 72 hours | 48 hours of Training Sessions | 24 hours of Practical Sessions
Session 1:
Intro to AI, Bayesian statistics and Turing Test
Session 2:
Different types of Neural Networks and Activation Functions
Session 3:
Classification using Neural networks
Session 4:
Project 1
Session 5:
Intro to Tensorflow with Linear regression
Session 6:
Project 2 - Part 1
Session 7:
Project 2- Part 2
Session 8:
Project 3 - Part 1
Session 9:
Project 3 - Part 2
Session 10:
Project 3 - Part 3
Session 11:
Project 4 - Part 1
Session 12:
Project 4 - Part 2
Session 13:
Project 5 - Part 1
Session 14:
Project 5 - Part 2
Session 15:
Project 5 - Part 3
Session 16:
Project 6 - Part 1
Session 17:
Project 6 - Part 1
Session 18:
Project 6 - Part 1
Session 19:
Intro to other DL libraries like Theano, keras and Pytorch
Session 20:
Transfer learning using keras
Session 21:
Approaches to Transfer learning
Session 22:
Custome Project 1
Session 23:
Custom Project 2
Session 24:
Final Assessment
● Text classification using ANN
● Finding objects using Tensorflow
● Facial Recognition Model
● Sentiment analysis
● Chatbot design using Neural Nets
● Content generation for Youtube videos
● Facial Expression Analysis
● Image Colorization
● Human Age Estimation
● Style Transfer projects
● Transfer Learning Application
● Custom project
● Basic knowledge of any Programming Language
● Decent Knowledge of Statistics, Probability, and Data Analysis
● Pass Enrollment test - 3 ( JGET - 3)
COURSEWORK -
● Each course is like an interactive textbook, featuring classroom sessions, quizzes and projects.
PRACTICALS -
● There will be a lab session of 2 hours once in a week on a certain week day apart from the classes where you will be solving the assignments.
TESTS -
There are 4 tests throughout the AI Expert course:
● Enrollment Test (JGET - 3)
● Assessment Test after the completion of AI Deep Learning Course
HELP FROM PEERS & TRAINERS -
● Connect with our trainers and other learners with their debate ideas, discuss course material, and get help to master the concepts.
CERTIFICATE -
● You will earn an official recognition of your work by getting certified by Jazari Research Institute of Artificial Intelligence after successful completion of the training.
JOB INTERVIEWS -
● After completion of the AI Advanced Course, JAZARI guarantees at least 3 interviews for you.
PLACEMENT ASSISTANCE -
● After the completion of the Foundation Course you will be prepared for interviews and also for project communication.
MR. ARNAV ARORA
Lead Trainer at Jazari
Arnav Arora is a graduate in Computer Science and Engineering from SRM Institute of
Science and Technology in Chennai, India. Arnav has over 4 years of machine and deep
learning experience.
Arnav’s area of expertise is natural language processing. He has over 2 years of
research experience and has worked with some of the best academic and industry
research labs in the country.
Arnav is one of the core members of PyData Delhi, one of the biggest data science and
machine learning communities in the world. He’s also an international speaker having
given talks at PyCon and PyData conferences around the world.
Arnav is a contributing author at Skynet Today, a publication aiming to provide informed
coverage of news about Artificial Intelligence in order to fight the overhyped articles
about the capabilities of these techniques.