This module equips you with the fundamental skills you will need as an AI Scientist / Engineer. This is an X-level module, ideally intended for beginners. If you have prior background in AI fundamentals you might find AI Y more suitable. You will become familiar with and gain expertise in regression and classification models. You will then learn about Neural Networks. Then, on to image processing with Convolutional Neural Networks.
At the end of this course, you will be able to train a variety of advanced neural network models, and apply them to image recognition and other problems.
You will now be ready to read research papers, and succeed in entry-level to intermediate AI and ML jobs.
Introductory Price: $1,000
List Price: $2,250
After successfully completing the program, you will be:
1. Able to run a variety of advanced neural network models on your own
2. Awarded the Professional Certification in Artificial Intelligence
3. Eligible to attend the Artificial Intelligence: Y program, the second part of the 3-part Master Series
4. Prepared to pursue advanced learning (graduate and above) studies in AI and Machine Learning
5. Eligible for junior to intermediate positions in industry
Anyone who seeks to learn machine learning and Artificial Intelligence, and who has the necessary prerequisites. The course is however tailored to students as well as working professionals, who have some background in maths as well as programming.
1. Basic statistics and mathematics (upto probability distributions, linear algebra and univariate calculus)
2. Working knowledge of python as represented by the subjects in python-preparator.
Bi-weekly live, cohort-based lessons with labs and quizzes
Ten or more hours/week to interact with an experienced & accomplished mentor
Complex problems that challenge you to apply what you learn
10–12 week long Capstone Project with one of our partner companies or faculty
kNN Regression and Linear Regression
Multi-Regression, Polynomial-Regression, Model Selection using Train/Validation and Cross Validation
Inference in Linear Regression
Regularization: Ridge & Lasso Regressions
Metrics and Data Imbalance, Precision, and Recall
Neural Networks 1 - Perceptron and MLP, Anatomy of Neural Networks and Design Choices
Neural Networks 2 - Fitting Neural Networks
Introduction, Review of Feed Forward Network Networks (Network Architecture and Design Choices)
Backpropagation and Optimizers
Regularization for Neural Networks, Early Stopping, Data Augmentation, Dropout, and Batch Normalization
Convolutional Neural Networks: Basic Concepts and Architectures
Receptive Fields, Strides and Saliency Maps
Convolutional Neural Networks: State of the Art CNNs
Neural Net Transfer Learning
It is a 3-part series where each program leads to an individual certification.
You can take these programs consecutively or at your own pace.
We welcome students from all academic backgrounds pursuing undergraduate programs and also those who are working, and have a deep interest in AI and Machine Learning. Knowledge of a minimum of high school level mathematics and basic python programming is important for this program.
Our Foundations program will help you prepare for the AI:X program and get you ready to build your skill sets in Artificial Intelligence. The course is self paced and is available absolutely free.
If your basics are strong, and you are familiar with topics covered with AI:X, you can directly take the Artificial Intelligence: Y program which is the second part of the Master AI series.
Yes, provided you meet the course prerequisites. We welcome both students and working professionals from all academic backgrounds in our programs.
This program is an ideal stepping stone to a top tier university. The program will equip you to read, interpret, and apply concepts in most leading-edge academic papers; and enable you to conduct original research in your area of interest. Our internationally renowned faculty will fully support your applications to major graduate programs.