Get updates on Eligibility, Admission, Placements Fees Structure
Quick Facts
| Medium Of Instructions | Mode Of Learning | Mode Of Delivery |
|---|---|---|
| English | Self Study, Virtual Classroom | Video and Text Based |
Course Overview
The Professional Certificate Course in AI and Machine Learning is an 11-month online bootcamp course. This course and its certificate are offered by E&ICT Academy, IIT Kanpur along with Simplilearn. With this certificate course, the candidates can think of advancing their careers by learning in-demand skills like machine learning, ChatGPT, artificial intelligence, and much more.
The Professional Certificate Course in AI and Machine Learning training offers tons of facilities to the candidates like live masterclasses from the different distinguished facilities of IIT Kanpur. During this Cohort-Based Program, the candidates will undergo experiential learning and will also be a part of learning more than 14+ practical frameworks and tools that make them ready for a job.
Also Read:
The Highlights
- 11 months course duration
- Online Bootcamp learning
- Programme completion certificate
- Learn from the experts
- Cohort-based programme
Programme Offerings
- Live masterclasses
- job assistance
- Simplilearn Career Service
- 25+ industry-relevant projects
- 14+ practical tools
- 4 live sessions
- Mock interview sessions
- Capstone Projects
Courses and Certificate Fees
| Certificate Availability | Certificate Providing Authority |
|---|---|
| yes | IIT Kanpur |
Eligibility Criteria
Academic Qualifications
- A valid bachelor's degree with a minimum of 50%
- Prior knowledge in topics like programming and mathematics
Work Experience
A formal work experience of 2+ years shall be preferred
Certification Qualifying Details
Upon successful completion a student will receive a Professional Certificate Course in AI and Machine Learning by E&ICT Academy, IIT Kanpur.
What you will learn
After the completion of the Professional Certificate Course in AI and Machine Learning syllabus, the candidates will be able to do the following:
- Understand the meaning, stages, applications, purpose, scope, and effects of AI and ML
- Understand the different processes of data science processes like data wrangling, data visualization, data exploration, hypothesis building, and
- testing
- Gain expertise in using the NumPy and scikitlearn package.
- Learning to keep a validation of machine learning models by decoding various accuracy metrics
- Understand and appreciating the different applications of deep Learning and its applications
- Learning to perform distributed and parallel computing with the help of high-performance GPUs
- Learn about the fundamentals and working of the natural language generation, and natural language processing
- Understanding ways for applying Deep Learning, and Machine Learning to NLP
- Performing text-to-speech conversion with the help of automated speech recognition
- Understanding the theory of reinforcement learning
- Gaining latest skills trending AI topics like generative AI, explainable AI, GPT, and generative modeling
- Learning about the applications of OpenAI, ChatGPT, Dall-E, Midjourney
Who it is for
If the candidates are interested in creating self-running AI tools, and want to get into any of the job roles mentioned below then this course is for this set of candidates.
- Data Analyst
- Data Engineer
- Data Scientist
- ML Engineer
- Business Intelligence Developer
Admission Details
The admission process to the Professional Certificate Course in AI and Machine Learning classes has only 3 steps
Step 1: Browse the URL https://ifacet.iitk.ac.in/professional-certificate-course-in-ai-and-machine-learning/
Step 2: Next, the candidates will have to submit an application by explaining something about themselves and why they need this course.
Step 3: Then an admission panel will be constructed after shortlisting candidates based on how the candidates have applied.
Step 4: Selected candidates will be joining the course after they pay the admission fee of the course.
Application Details
The candidates will have to enter some very basic details for initiating the application like name, country, mobile number, and total years possessed by the candidates.
The Syllabus
- Decoding Artificial Intelligence
- D Fundamentals of Machine
- Learning and Deep Learning
- D Machine Learning Workflow
- D Performance Metrics
- Data Science Overview
- D Data Analytics Overview
- D Statistical Analysis and Business Applications
- D Python Environment Setup and Essentials
- D Mathematical Computing with Python (NumPy)
- D Scientific Computing with Python (SciPy)
- D Data Manipulation with Pandas
- D Data Visualization in Python using Matplotlib
- D Introduction to Artificial
- Intelligence and Machine Learning
- D Data Preprocessing
- D Supervised Learning
- D Feature Engineering
- D Supervised Learning Classification
- Unsupervised Learning
- D Time Series Modelling
- D Ensemble Learning
- D Recommender Systems
- AI and Deep Learning
- Introduction
- D Artificial Neural Network
- D Deep Neural Network andTools
- D Deep Neural Net Optimization,
- Tuning, and Interpretability
- D Convolutional Neural Net (CNN)
- D Recurrent Neural Networks
- D Autoencoders
- Computer Vision Basics with Python
- D Advanced Computer Vision with OpenCV 4, Keras, and TensorFlow 2
- D Computer Vision for OCR and Object Detection
- D PyTorch for Deep Learning and Computer Vision
Use Cases of ChatGPT
Programming Refresher
- Sample or Population Data?
- The Fundamentals of Descriptive Statistics
- Measures of Central Tendency, Asymmetry, and Variability
- Practical Example: Descriptive Statistics
- Distributions
- Estimators and Estimates
- Confidence Intervals: Advanced Topics
- Practical Example: Inferential Statistics
- Hypothesis Testing:Introduction
- Hypothesis Testing: Let’s Start Testing!
- Practical Example: Hypothesis Testing
- The Fundamentals of Regression Analysis
- Subtleties of Regression Analysis
- Assumptions for Linear Regression Analysis
- Dealing with Categorical Data
- Practical Example: Regression Analysis
- Introduction to Natural Language Processing
- Feature Engineering on Text Data
- Natural Language Understanding Techniques
- Natural Language Generation
- Natural Language Processing Libraries
- Natural Language Processing with Machine Learning and Deep Learning
- Introduction of Speech Recognition
- Signal Processing and Speech Recognition Models
- Speech-to-Text
- Text-to-Speech
- Voice Assistant Devices
- Introduction to Reinforcement Learning
- Reinforcement Learning
- Framework and Elements
- Multi-Armed Bandit
- Markov Decision Process
- Solution Methods
- Q-value and Advantage based Algorithms
- Project 1 - Social Media
- Project 2 - Ecommerce
- Project 3 - Manufacturing
- Project 4 - EdTech
- Project 5 - Healthcare
- Project 6 - Entertainment
- Project 7 - Consumer Electronics