Machine Learning Algorithms Free Course

Machine Learning Algorithms

star 4.49  Beginner level 2.25 learning hrs 32.4K+ Learners

Enroll in this Machine Learning Algorithms course to understand the machine learning methods, algorithms, and techniques employed to analyze and present data for decision-making. Gain a finer hold through demonstrated projects.

Instructor:

Mr. Anirudh Rao

Key Highlights

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About this course

This online Machine Learning Algorithms course has been designed keeping in mind that a novice learner should be able to grasp the concepts and understand algorithms with examples. This course covers the introduction to Machine Learning and the basics of algorithms, along with a theoretical and practical understanding of supervised, unsupervised, and reinforcement learning. You will also gain skills to employ K-nearest Neighbor, Naive Bayes and Random Forest algorithms, and Linear Regression and Support Vector Machines (SVM) techniques to accomplish Machine Learning tasks. A tonne of practical Python demonstrations is offered to comprehend the concepts better. 

 

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Course outline

Introduction to Machine Learning

This section defines Machine Learning and explains it with an example. 

Types Of Machine Learning

This section discusses Supervised and Unsupervised Machine Learning methods to accomplish various tasks. 

How does a Machine Learning Model Learn?

This section explains how a machine understands to work on a dataset to deliver desired results. It explains the role of pre-fed data set and the process involved in building a Machine Learning model. 
 

Linear Regression Algorithm

This section explains the Linear Regression algorithm with demonstrated example. 

Naïve Bayes Algorithm

This section explains the Naive Bayes algorithm with demonstrated examples. 

KNN Algorithm in Machine Learning

This section explains the KNN algorithm with demonstrated examples. 

Support Vector Machines in Machine Learning

This section explains Support Vector Machine with demonstration example and discusses its applications. 

Random Forest Algorithm in Machine Learning

This section explains the Random Forest algorithm with demonstrated example.

Get access to the complete curriculum once you enroll in the course

Machine Learning Algorithms

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2.25 Hours

Beginner

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32.4K+ learners enrolled so far

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Machine Learning Essentials with Python
1 project 12 hrs video content
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Learner reviews of the Free Courses

4.49
69%
22%
6%
1%
2%
Reviewer Profile

4.0

“Insights from My Machine Learning Journey”
I really enjoyed the comprehensive overview of various machine learning algorithms. The hands-on approach, especially with the Linear Regression and KNN algorithms, allowed me to apply theoretical concepts in practical scenarios. The clarity in explaining complex topics, like Support Vector Machines and Random Forest, made them more accessible. I appreciated how the sessions encouraged questions and discussions, fostering a collaborative learning environment. Overall, this experience significantly enhanced my understanding and appreciation of machine learning!
Reviewer Profile

5.0

Country Flag Morocco
“Learned ML Algorithms, Data Preprocessing, Feature Engineering, and Model Evaluation Techniques.”
I appreciated how the course covered essential machine learning concepts, from supervised and unsupervised learning to feature engineering and model evaluation. The practical projects, like building predictive models, were both challenging and rewarding, helping me understand the real-world applications of algorithms. The balance between theory and implementation was excellent, making the learning process engaging and effective. Overall, it deepened my understanding of ML principles and techniques.
Reviewer Profile

5.0

Country Flag India
“Very good, easy to understand, and more interactive because of quizzes.”
Great Learning's Machine Learning course provided a solid foundation in the field. The curriculum covered a wide range of topics, from supervised and unsupervised learning to deep learning. The instructors were knowledgeable and explained complex concepts in a clear manner. The hands-on projects were particularly valuable, allowing me to apply my learning to real-world problems. The course also provided a good balance of theory and practice. Overall, I would highly recommend Great Learning's Machine Learning course to anyone looking to gain a strong understanding of the subject.
Reviewer Profile

5.0

Country Flag India
“Highlight of My Machine Learning Experience with KNN”
I enjoyed learning about K-Nearest Neighbors (KNN) in machine learning. The simplicity and intuitiveness of the algorithm make it easy to understand and apply. I liked how KNN classifies data points based on proximity to other data points, making it an effective method for classification tasks. The concept of using distance metrics, such as Euclidean distance, was particularly interesting. It was insightful to learn how KNN can be fine-tuned with hyperparameters like 'k' and distance metrics for better accuracy.
Reviewer Profile

4.0

Country Flag Morocco
“Une expérience d'apprentissage enrichissante et bien structurée”
J'ai beaucoup apprécié la manière dont l'instructeur a rendu les concepts clairs et accessibles, tout en approfondissant les sujets techniques. Le contenu était bien structuré et les quiz ainsi que les devoirs m'ont permis de consolider mes connaissances efficacement. Cela m'a vraiment motivé à continuer d'apprendre.
“Machine Learning Theory and Algorithms”
I am very satisfied with this particular knowledge experience as it represents an important chapter in the field of Machine Learning. The specific algorithms, the theory of classification in Learning, and the techniques applied are very useful and can be used to solve real-time problems.
Reviewer Profile

5.0

Country Flag India
“(Machine Learning Algorithms)...”
This course offered a comprehensive overview of machine learning concepts, including supervised and unsupervised learning, and algorithms like KNN and K-means. The content was clear, engaging, and well-structured, with practical examples that helped reinforce the material. Some hands-on coding exercises or real-world case studies could enhance the learning experience. Overall, it's a solid foundation for anyone new to machine learning or looking to refresh their knowledge.
Reviewer Profile

5.0

Country Flag India
“MACHINE LEARNING ALGORITHMS using Python”
I liked its curriculum, skills and tools, instructor, dealing with topic depth, ways of conducting quizzes and assessments, and it is very easy to follow. Also, the quality of videos is really very good.
Reviewer Profile

5.0

“Great foundation for start-ups”
I strongly recommend taking this course to improve your understanding of machine learning before facing real-world challenges. Although I encountered some difficulties, I found it beneficial to take notes for future reference. I believe this will enhance my confidence in the field of predictive modeling.
Reviewer Profile

4.0

Country Flag India
“Quiz and video understanding”
Quiz, video understanding, development in skills and tools, and topic understanding in depth.

Our course instructor

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Mr. Anirudh Rao

Machine Learning Expert

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780.1K+ Learners
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79 Courses
Anirudh has been working in the field of Data Science and has expertise over Python, Machine Learning and other concepts in the field of data analysis. He is also proficient in the concept of Deep Learning and its usage in a production environment. Expertise extends towards working on various projects in the domain of Artificial Intelligence and Neural Networks as well.

Frequently Asked Questions

Will I receive a certificate upon completing this free course?

Yes, upon successful completion of the course and payment of the certificate fee, you will receive a completion certificate that you can add to your resume.

Is this course free?

Yes, you may enroll in the course and access the course content for free. However, if you wish to obtain a certificate upon completion, a non-refundable fee is applicable.

What are the prerequisites required to learn Machine Learning Algorithms?

Basic computer literacy, Math would be an added advantage; some basic understanding of how to code in Python can ​speed up learning Machine Learning Algorithms. 

 

How long does it take to complete learning basic algorithms for Machine Learning?

It takes about 1 and a half hours to complete the course. 

 

What are Machine Learning Algorithms?

With Machine Learning algorithms, software programs can predict outcomes more accurately without having to be explicitly instructed. They use these algorithms to forecast new output values by feeding historical data.

 

Why is Machine Learning important?

Machine Learning is significant because it uses various algorithms to help companies build new goods by providing insights into consumer behavior trends and operational business patterns. Machine learning is a key component of the operations of many of the world's most successful businesses today, like Facebook, Google, and Uber. For numerous businesses, machine learning has significantly increased their competitive edge.

 

Why is Machine Learning popular?

Machine learning is one of the most important technologies today. Since it is used in practically every field, it is widely used by professionals, academics, and students. You probably already know how effective and potent a well-trained machine-learning model is in solving issues. This is possible since the algorithms are fed with data, and the result is a model. Since this is a fundamental idea, everyone in the class must fully grasp the algorithms.

 

How to choose a suitable Machine Learning model?

If not done carefully, selecting the best machine learning model to address a problem can take a lot of time. The basic guide to choosing a suitable model:
Step 1: Align the issue with potential data sources that should be considered for the solution. Data scientists and skilled professionals with in-depth knowledge of the issue are needed for assistance with this phase.
Step 2: Gather information, format it, and, if necessary, label it. With assistance from data wranglers, data scientists often take the lead in this step.
Step 3: Select the algorithm(s) to employ, then test them to see how they perform. Data scientists typically handle this stage.
Step 4: Once outputs are accurate enough, they can be further fine-tuned. Data scientists often complete this step with input from subject matter experts who thoroughly understand the issue.
Will I get a certificate after completing this course?
Answer: Yes, you will get a course completion certificate after qualifying in the quiz. 
 

What knowledge and skills will I gain upon algorithms for Machine Learning course?

By the end of this course, you will understand the basics of Machine Learning and fundamental algorithms that can be used in Machine Learning, like Linear Regression, Naive Bayes, KNN, Random Forest algorithms, and Support Vector Machines.

 

Can I take the Machine Learning course multiple times?

Yes. You will have free lifetime access to this course, so you can access the course at your leisure. 

How much does this Machine Learning Algorithms course cost?

It is an entirely free course from Great Learning Academy. Anyone interested in learning the basics of Machine Learning Algorithms can get started with this course.

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 Machine Learning Algorithms course?

Great Learning Academy provides this Machine Learning Algorithms 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 Machine Learning Algorithms 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 Machine Learning Algorithms course?

Yes, once you enroll in the course, you will have lifetime access, where you can log in and learn whenever you want to. 

Is there any limit on how many times I can take this free course?

Once you enroll in the Machine Learning Algorithms course, you have lifetime access to it. So, you can log in anytime and learn it for free online.

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