Introduction to Machine Learning
Explore Introduction to Machine Learning: Linear Regression, Hackathon, Kaggle, Supervised learning, Regression, Unsupervised Learning, Recommender System, & ML on Cloud. Unlock the potential of data-driven intelligence!
Instructor:
Dr. Abhinanda Sarkar
Ratings
Level
Learning hours
Learners
Skills you’ll Learn
About this Course
Machine Learning is a go-along domain with Artificial Intelligence in Computer Science and Technology, which deals with training the machines with previously trained models. The system self-learns the process improves without munch of human intervention required. With the world driven by advancements in Artificial Intelligence and its technologies today, Machine Learning is gradually making its stand in various fields. Machine Learning is also one of the most sought job choices, and thus many aspirants learn it. This course introduces you to the world to Machine Learning. You will undersand niche concepts such as Supervised and Unsupervised learning, Regression and Classification. This course will educate you about different platforms where you can participate in competitions conducted world wide such as hackathon, kaggle. You also get to know the concepts behind recommendation systems and how ML on cloud is emerging.
The faculty for the course is Dr. Abhinanda Sarkar, Ph.D. from Stanford University and Ex-Faculty MIT, is Academic Director at Great Learning for Data Science and Machine Learning Programs.
Check out our PG Course in Machine learning Today.
Course Outline
Data is the soul of Machine Learning, and there are specific methods to deal with it efficiently. This module first introduces Machine Learning and talks about the mathematical procedures involved. You will learn about supervised and unsupervised learning, Data Science Machine Learning steps, linear regression, Pearson's coefficient, best fit line, and coefficient of determinant. Lastly, you will be going through a case study to help you effectively comprehend Machine Learning concepts.
Machine learning algorithms involve seven steps: Collect data, Prepare the data, Choose the model, Train the machine model, Evaluation, Parameter tuning, Prediction or Inference.
Kaggle supports a no-setup, customizable Jupyter Notebooks environment. It helps access free GPUs and a vast community published code and data repository. Hackathons are designed sprint-like events that focus on creating a functioning software or hardware where programmers, graphic designers, interface designers, project managers, domain experts, and others collaborate intensively to contribute to software projects.
Regression helps predict a continuous quantity. On the other hand, classification predicts discrete class labels, and they can sometimes overlap while working with machine learning algorithms.
Our course instructor
Dr. Abhinanda Sarkar
Faculty Director, Great Learning
Dr. Sarkar’s publications, patents, and technical leadership have been in applying probabilistic models, statistical data analysis, and machine learning to diverse areas such as experimental physics, computer vision, text mining, wireless networks, e-commerce, credit risk, retail finance, engineering reliability, renewable energy, and infectious diseases, His teaching has mostly been on statistical theory, methods, and algorithms; together with application topics such as financial modeling, quality management, and data mining.
Dr. Sarkar is a certified Master Black Belt in Lean Six Sigma and Design for Six Sigma. He has been visiting faculty at Stanford and ISI and continues to teach at the Indian Institute of Management (IIM-Bangalore) and the Indian Institute of Science (IISc). Over the years, he has designed and conducted numerous corporate training sessions for technology and business professionals. He is a recipient of the ISI Alumni Association Medal, IBM Invention Achievement Awards, and the Radhakrishan Mentor Award from GE India
Share your certificate & get noticed
