Introduction to Machine Learning Course
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 SarkarLevel
Learning hours
Learners
Skills you will 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
Senior Faculty & Director Academics, Great Learning
Machine Learning Expert
Frequently Asked Questions
Will I receive a certificate upon completing this free course?
Is this course free?
Is machine learning a promising career?
Machine learning is a technology that mimics human actions. There is not much workload on human programmers; they are supposed to supervise the machines and give commands to mimic the activities based on previous results. Therefore, machine learning makes an outstanding career.
What level of mathematics is needed to learn machine learning?
Probability, statistics, linear algebra, and calculus make the base foundation for machine learning. A machine learning professional must have good knowledge in working with these sets of mathematical fields.
Will I get a certificate after completing this Introduction to Machine Learning free course?
Yes, you will get a certificate of completion for Introduction to Machine Learning after completing all the modules and cracking the assessment. The assessment tests your knowledge of the subject and badges your skills.
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