Who should attend
The program is for you if, you:
- Aspire to build a technical career in AI and Machine Learning.
- Like solving complex problems in a structured manner.
- Are comfortable in dealing with advanced algorithms.
- Have prior programming experience and want to learn Python.
- Want to build AI/ML solutions integrated into tech infrastructures.
- Wish to learn advanced AI, ML and Deep Learning techniques, and their applications.
About the course
Our carefully-crafted curriculum has been designed to provide you with the breadth and depth you need to lead Artificial Intelligence efforts at any organisation. Covering the most widely-used tools and technologies, and a variety of industry examples, it builds your foundations to make you job-ready.
The Foundations block comprises of two courses where we get our hands dirty with Statistics and Code, head-on. These two courses set our foundations so that we sail through the rest of the journey with minimal hindrance.
Module 1: Fundamentals of AIML
This introductory module will help you get comfortable with the foundational concepts that will help you with the rest of the modules in the course. You will also learn the essentials of the Python programming language and its packages for data analysis and computing, including NumPy, SciPy, Pandas, Seaborn and Matplotlib.
- Introduction to Python
- NumPy, Pandas
- Exploratory Data Analysis
- Matplotlib, Seaborn
Self-paced Module: Statistical Learning
Statistical Learning is a branch of applied statistics that deals with Machine Learning, emphasizing statistical models and assessment of uncertainty. This course on statistics will work as a foundation for the Artificial Intelligence and Machine Learning concepts learnt in this program.
- Descriptive Statistics
- Inferential Statistics
- Probability & Conditional Probability
- Hypothesis Testing
- Chi-square & ANOVA
The ML block will teach us Machine Learning techniques and all the algorithms popularly used in Classical ML that fall in each of the categories.
Module 2: Supervised Learning
The aim of Supervised Machine Learning is to build a model that makes predictions based on evidence in the presence of uncertainty. In this course, you will learn about the different algorithms of supervised learning such as Linear Regression, Logistic Regression and Decision Trees.
- Intro to Machine Learning
- Linear Regression
- Logistic Regression
- Model Evaluation
Module 3: Ensemble Techniques
Ensemble methods help to improve the predictive performance of Machine Learning models. In this course, you will learn about different Ensemble methods such as ‘Random Forest’ that combine several Machine Learning techniques into one predictive model in order to decrease variance, bias or improve predictions.
- Decision Trees
- Ensemble Methods - Bagging, Boosting and Stacking
- Random Forest
Module 4: Feature Engineering, Model Selection and Tuning
Model building is an iterative process. Employing Feature Engineering techniques along with a careful model selection exercise helps to improve the model. Further, tuning the model is an important step to arrive at the best possible result. This module talks about the steps and processes around the same.
- Feature Engineering
- Sampling and Smote, Regularization
- Model Performance Measures
Module 5: Unsupervised Learning
Unsupervised Learning finds hidden patterns or intrinsic structures in data. In this course, you will learn about commonly-used clustering techniques like K-Means Clustering and Hierarchical Clustering along with Dimension Reduction techniques like Principal Component Analysis.
- K-Means Clustering
- Hierarchical Clustering
Artificial Intelligence & Deep Learning
The AI and Deep Learning block will take us beyond the traditional ML into the realm of Neural Nets. From the regular tabular data we move on to training our models with unstructured data like Text and Images.
Module 6: Neural Networks
Deep Learning carries out the Machine Learning process using an ‘Artificial Neural Net’, which is composed of a number of levels arranged in a hierarchy. In this course, you will learn about the basic building blocks of Artificial Neural Networks using Python libraries such as Keras and TensorFlow. You’ll learn how Deep Learning Networks can be successfully applied to data for knowledge discovery, knowledge application, and knowledge-based prediction.
- Activation Function, Loss Function
- Optimizers, Regularization-Drop-outs
Module 7: Computer Vision
The module will reflect on the ability of a computer system to see and make sense of visuals using CNN (Convolutional Neural Networks). It will enable you to efficiently handle image data for the purpose of feeding into CNNs and will discuss CNN architectures.
- Business Applications of Computer Vision
- Forward Propagation & Backpropagation for CNNs
- Working with Images
- Convolutions, VGGNet
- Transfer Learning
Module 8: Natural Language Processing
This module talks about yet another interesting implementation of Neural Networks that revolves around equipping computers to understand human language. You will learn to work with text data and explore the interesting world of RNNs and LSTMs.
- Business Applications of NLP
- Text Extraction Techniques and Text Pre-processing
- GLoVe, Word2Vec, Word Embeddings, POS Tagging
- RNNs, LSTMs
Career Assistance : Resume and LinkedIn profile review, interview preparation, 1 : 1 career coaching
Post Graduate Certificate from The University of Texas at Austin
Key Learning Outcomes
- Build your expertise in the most widely-used AI & ML tools and technologies.
- Acquire the ability to independently solve business problems using AI & ML.
- Master the skills needed to build machine learning and deep learning models.
- Develop know-how of the applications of AI in areas such as Computer Vision & NLP.
- Understand the possibilities and implications of AI in different industries.
- Build a substantial body of work and an industry-ready portfolio in AI & ML.
Artificial Intelligence Course, AI Courses, Artificial Intelligence Training Online | Great Learning
Sunil Kumar joined the Chicago Booth faculty on January 1, 2011 as Dean and Professor of Operations Management. Since then, Kumar has concentrated on broadening and strengthening the intellectual footprint of the school, including expanding the faculty, supporting increasingly diverse student asp...
Biography Kumar Muthuraman is the H. Timothy (Tim) Harkins Centennial Professor in the Department of Information, Risk and Operations Management and the Department of Finance. He received his Ph.D. from Stanford University. Dr. Muthuraman’s research focuses on decision making under uncertainty. A...
Dr. Abhinanda Sarkar is the Academic Director at Great Learning for Data Science and Machine Learning Programs. Dr. Sarkar received his B.Stat. and M.Stat. degrees from the Indian Statistical Institute (ISI) and a Ph.D. in Statistics from Stanford University. He has taught applied mathematics at ...
Prof. Mukesh Rao is an Adjunct Faculty at Great Lakes for Big Data and Machine Learning. Mukesh has over 20 years of industry experience in Market Research, Project Management, and Data Science. Mukesh has conducted over 100 corporate trainings. Data Science training covers all the stages of CRI...
Prof. Sethi is currently a faculty member at IIT Bombay where his research is focused on applying deep learning methodologies to digital pathology for analysis of cancer tissues. He was previously a faculty member at IIT Guwahati and spent many years at ZS Associates, a leading management consult...
Dan received his PhD in Information, Risk, & Operations Management from the University of Texas. He then was an assistant professor at Singapore University of Technology and Design in the Engineering Systems and Design pillar. His main research interests are focused on financial engineering, ...
Videos and materials
Because of COVID-19, many providers are cancelling or postponing in-person programs or providing online participation options.
We are happy to help you find a suitable online alternative.