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!
4.46
Beginner
1.5 Hrs
66.7K+
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.
Read More
Course Outline
Introduction to Machine Learning and Linear Regression
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.
Steps of Machine Learning
Machine learning algorithms involve seven steps: Collect data, Prepare the data, Choose the model, Train the machine model, Evaluation, Parameter tuning, Prediction or Inference.
Hackathon and Kaggle
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.
Supervised learning
Regression and Classification
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.
Trusted by 1 Crore+ Learners globally
