Free Machine Learning Course
Introduction to Machine Learning
4.46
Beginner level
1.5 learning hrs
Learn the fundamentals of machine learning, including supervised and unsupervised learning, regression, and recommendation systems. Join this free machine learning course to apply these skills in real-world business scenarios.
Instructor:
Dr. Abhinanda Sarkar
About this course
This Machine Learning course provides a comprehensive foundation in both supervised and unsupervised learning, with a focus on key concepts such as linear regression, data preprocessing, and model evaluation. You'll learn essential techniques like Pearson's coefficient, the best-fit line, and the coefficient of determination to understand how machine learning models make predictions. Through hands-on projects and a real-world case study, you will apply these concepts to solve practical problems, ensuring you can effectively implement machine learning models.
The course will also introduce you to machine learning workflows, covering the seven essential steps: data collection, preparation, model selection, training, evaluation, parameter tuning, and prediction. You will gain hands-on experience with Kaggle and hackathons, using tools like Jupyter Notebooks and exploring real-world applications such as recommendation systems. By the end of the course, you'll be capable of applying machine learning techniques to business problems, with skills in both regression and classification, and the ability to deploy machine learning models on the cloud.
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.
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.
Unsupervised Learning
Unsupervised learning is a known machine learning method in which algorithms are not given pre-assigned labels to train the data. It self-discovers naturally occurring patterns in training the data sets.
Netflix Price
A recommendation engine is a machine learning technology used in Netflix to suggest shows and movies to its customers. A recommendation system processes on the back end to provide services based on the previously collected data from the customers.
Recommender System
Recommender systems are designed to recommend products and services to the users. It predicts the user interests based on the previously calculated metrics, which benefits both the user and the system.
ML on Cloud
Machine learning is applied to work with the cloud since it eliminates the time spent managing infrastructure using TensorFlow and other Python machine learning libraries such as scikit-learn. Google cloud uses machine learning methods to work with managing the cloud space.
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4.46
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Practice exercises
Guided Projects
AI Resume Builder
AI mock interviews
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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?
What will I learn in this free online Machine Learning course?
In this free machine learning course, you'll learn core concepts such as supervised and unsupervised learning, linear regression, classification, and recommendation systems. You’ll also get hands-on experience with tools like Kaggle, hackathons, and applying machine learning on cloud platforms.
Who should take this free machine learning training course?
This course is designed for beginners with no prior experience in machine learning. It's perfect for students, aspiring data scientists, or professionals seeking a foundational understanding of machine learning concepts and techniques.
How long does the course take to complete?
The course includes about 1.5 hours of self-paced learning material, making it flexible for learners to complete at their own pace while balancing other commitments.
What skills will I gain from this course?
You'll gain the following skills:
- Introduction to Machine Learning
- Supervised Learning
- Unsupervised Learning
- Linear Regression
- Classification
- Recommender System
- Kaggle
- Hackathon
- ML on Cloud
- Data Science
- Model Training
- Machine Learning Platforms
- Data-Driven Intelligence
Is this course self-paced?
Yes, the course is fully self-paced, allowing you to start at any time and progress at your own speed.
How will this course help my career?
By learning machine learning fundamentals, you’ll be prepared to move into more advanced machine learning courses or data science roles, increasing your job market competitiveness in tech and data-driven industries.
What is the difference between supervised and unsupervised learning?
Supervised learning uses labeled data to train models, while unsupervised learning finds patterns in data without predefined labels. Both techniques are essential for solving different types of machine learning problems.
What modules/topics are covered in this free online machine learning course?
You will learn the following topics in this course:
Introduction to Machine Learning and Linear Regression
Steps of Machine Learning
Hackathon and Kaggle
Supervised learning
Regression and Classification
Unsupervised Learning
Netflix Price
Recommender System
ML on Cloud
Does this course include practical case studies?
Yes. The course includes real-world examples and a case study to help you apply machine learning concepts to solve practical business problems.
Can I take other machine learning courses after this one?
Yes. Once you've completed this course, you can move on to more advanced machine learning and data science courses to further your knowledge and skills.
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.
Can I sign up for multiple courses from Great Learning Academy at the same time?
Yes, you can enroll in as many courses as you want from Great Learning Academy. There is no limit to the number of courses you can enroll in at once, but since the courses offered by Great Learning Academy are free, we suggest you learn one by one to get the best out of the subject.
Why choose Great Learning Academy for this free Introduction to Machine Learning course?
Great Learning Academy provides this Introduction to Machine Learning course for free online. The course is self-paced and helps you understand various topics that fall under the subject with solved problems and demonstrated examples. The course is carefully designed, keeping in mind to cater to both beginners and professionals, and is delivered by subject experts. Great Learning is a global ed-tech platform dedicated to developing competent professionals. Great Learning Academy is an initiative by Great Learning that offers in-demand free online courses to help people advance in their jobs. More than 5 million learners from 140 countries have benefited from Great Learning Academy's free online courses with certificates. It is a one-stop place for all of a learner's goals.
What are the steps to enroll in this Introduction to Machine Learning course?
Enrolling in any of the Great Learning Academy’s courses is just one step process. Sign-up for the course, you are interested in learning through your E-mail ID and start learning them for free online.
Will I have lifetime access to this free Introduction to Machine Learning course?
Yes, once you enroll in the course, you will have lifetime access, where you can log in and learn whenever you want to.
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