Machine Learning Algorithms Free Course

Machine Learning Algorithms

star 4.49  Beginner level 2.25 learning hrs 32.3K+ 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

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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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32.3K+ 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
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Reviewer Profile

5.0

“Feedback on Basic Machine Learning Course”
The basic machine learning course provided a solid introduction to the fundamental concepts and algorithms in the field. The course structure was well-organized, beginning with essential topics such as supervised and unsupervised learning, and gradually advancing to more complex concepts like model evaluation and feature selection. Strengths: Clear Explanations: The instructors did an excellent job of breaking down complex topics into easily understandable segments. The use of real-world examples helped in relating theoretical concepts to practical applications. Engaging Content: The course content was engaging and interactive. The quizzes and hands-on assignments reinforced the learning material, providing a good balance between theory and practice. Resources: The supplementary materials, including reading lists and coding exercises, were helpful for deepening understanding and practicing the skills learned. Supportive Community: The online forums and discussion groups were active and provided valuable support for problem-solving and further discussion on course topics. Areas for Improvement: Pacing: For beginners, some sections of the course, particularly those involving complex mathematical concepts, might feel a bit rushed. A more gradual pace in these areas or additional tutorials could be beneficial. More Practical Examples: While the course included several practical examples, adding more real-world case studies or projects could further enhance the learning experience, giving students a clearer understanding of how machine learning is applied in different industries. Code Walkthroughs: More detailed walkthroughs of coding exercises would be useful, especially for students who are new to programming. This would help in bridging the gap between theoretical understanding and practical implementation.
Reviewer Profile
MUBASHIR HABIB

5.0

“Machine Learning Algorithms improvee ”
Machine learning algorithms are the backbone of artificial intelligence, powering everything from recommendation systems to self-driving cars. While they've made significant strides, there's always room for improvement. Let's explore key strategies to enhance algorithm performance: Understanding the Core Issues Before diving into solutions, it's crucial to identify the root causes of suboptimal performance: Data Quality: Inaccurate, incomplete, or biased data can lead to erroneous models. Algorithm Selection: Choosing the wrong algorithm for a specific problem can hinder performance. Hyperparameter Tuning: Incorrect hyperparameter values can significantly impact results. Overfitting/Underfitting: Models that are too complex or too simple can suffer from these issues. Computational Resources: Insufficient computing power can limit algorithm capabilities. Strategies for Improvement Data Preprocessing: Handle missing values, outliers, and inconsistencies. Feature scaling and normalization. Data augmentation to increase dataset size. Algorithm Selection: Carefully consider the problem type (classification, regression, clustering, etc.) Experiment with different algorithms to find the best fit. Hyperparameter Tuning: Utilize techniques like grid search, random search, or Bayesian optimization. Consider automated hyperparameter tuning tools. Regularization: Prevent overfitting by adding a penalty term to the loss function. Techniques include L1, L2, and dropout regularization. Ensemble Methods: Combine multiple models to improve accuracy and robustness. Examples include bagging, boosting, and stacking. Feature Engineering: Create new features from existing data to improve model performance. Domain knowledge is essential for effective feature engineering. Model Evaluation: Use appropriate metrics (accuracy, precision, recall, F1-score, etc.) Cross-validation to assess model generalization. Continuous Learning: Update models with new data to adapt to changing patterns. Implement feedback mechanisms to improve model performance over time. Advanced Techniques: Explore deep learning, transfer learning, and reinforcement learning for complex problems. Additional Considerations Computational Efficiency: Optimize algorithms for faster training and inference. Interpretability: Understand how models make decisions for transparency and trust. Bias Mitigation: Address biases in data and algorithms to ensure fairness. By following these guidelines and continuously experimenting, you can significantly enhance the performance of your machine learning algorithms.
Reviewer Profile

5.0

Country Flag India
“it was a great experience and it was easy to understand and impliment”
provides a comprehensive learning experience covering machine learning fundamentals and advanced topics like regression analysis, classification, clustering, and association rule learning. The course utilizes Python for machine learning programs and includes detailed instruction on the Python ML ecosystem, libraries such as scikit-learn, NumPy, and Pandas, and the implementation of machine learning algorithms like linear regression, logistic regression, SVM, K-Means clustering and random forest algo.
Reviewer Profile

5.0

Country Flag United States
“Very Easy to Understand and Follow. Explained in a Simple Manner.”
Excellent way of explaining things in a very easy to understand manner.
Reviewer Profile

5.0

Country Flag United States
“Good teaching by instructor and the videos covers the machine learning algorithms clearly”
Good teaching by instructor and the videos covers the machine learning algorithms clearly.
Reviewer Profile

5.0

Country Flag India
“Navigating the World of Machine Learning: My Personal Journey from Novice to Practitioner”
In my machine learning course, I particularly enjoyed the hands-on projects, where I got to work with real-world datasets and build models that addressed actual problems. The interactive nature of the course, including coding exercises and live workshops, made the learning process engaging and dynamic. I appreciated the deep dive into fundamental concepts and algorithms, which provided me with a solid foundation. The instructor’s expertise and ability to clarify complex topics added significant value. Additionally, working collaboratively with peers and exploring the diverse applications of machine learning kept me motivated and excited throughout the course.
Reviewer Profile

5.0

Country Flag Indonesia
“Great Learning: An Excellent Platform for Data Science and Machine Learning Education”
Great Learning is an excellent platform, offering a wide range of high-quality courses with practical insights and hands-on experience. The content is well-structured, making complex topics easier to understand, and the interactive learning approach enhances engagement. Highly recommended for anyone looking to upskill or deepen their knowledge in data science, machine learning, and other tech fields.
Reviewer Profile

5.0

Country Flag India
“Great Learning’s ML Course Covers Algorithms, Hands-On Python Coding, Real-World Projects, Deep Learning Basics, Evaluation, and a Capstone Project with Mentorship”
I particularly enjoy the hands-on approach and real-world projects that make complex concepts in machine learning more tangible and applicable. The focus on practical skills, like using Python libraries and working with real data, is invaluable. Additionally, the mentorship aspect is a great resource for guidance and feedback, which is crucial for mastering ML.
Reviewer Profile

5.0

Country Flag United Kingdom
“Very informative and interesting topic, well explained and presented. ”
It was very easy to understand and following as the instructor was very good at explaining and the topic of machine learning algorithm is very interesting. I enjoyed the practical demonstration and explanations too.
Reviewer Profile

5.0

Country Flag United Kingdom
“created a highly engaging and memorable learning experience”
I thoroughly enjoyed this course because it was both engaging and informative. The topics covered were fascinating and sparked my curiosity, making me eager to learn more. Practical examples helped reinforce my understanding and kept me motivated throughout. Overall, it was a rewarding experience that deepened my knowledge and interest in the subject.
Reviewer Profile

5.0

Country Flag India
“Excellent Machine Learning course”
I can't recommend this machine learning course enough! The content was clear, concise, and covered a wide range of essential topics—from the basics to more advanced techniques like neural networks and deep learning. The hands-on projects helped me gain practical experience, and the step-by-step explanations made even complex concepts easy to grasp. The instructor was knowledgeable, engaging, and always quick to respond to questions. By the end of the course, I felt confident applying machine learning techniques to real-world problems. A must for anyone serious about mastering ML!
Reviewer Profile
Irsa Imtiaz

5.0

“Hand on Practice I enjoyed the most in this course”
Hand on Practice I enjoyed the most in this course. Moreover the instructor guides us in a very fine way
Reviewer Profile

5.0

Country Flag Singapore
“Clear Foundational Learning Machine Learning Algorithm”
Great examples of each type of Machine Learning Algorithm and how to code them.
Reviewer Profile

5.0

Country Flag India
“Comprehensive and Engaging Learning Experience”
The course structure was excellent, with a clear balance between theory and practical applications. The hands-on projects allowed me to apply what I learned immediately, and the community discussions were helpful in clarifying doubts. The instructor's deep knowledge of the subject made complex topics much easier to grasp.
Reviewer Profile

5.0

Country Flag India
“Comprehensive and Engaging: A Valuable Learning Experience with Practical Insights and Skill Development for Future Success”
The Machine Learning Algorithms course provided a clear understanding of key concepts and practical applications. The instructor explained complex topics in an easy-to-follow manner, and the hands-on projects reinforced learning. I feel more confident in applying ML techniques now. Overall, a valuable and enriching experience!
Reviewer Profile

5.0

Country Flag India
“The Training Provided a Solid Foundation in ML Concepts with Practical Applications and Engaging Discussions Enhancing My Understanding”
I liked the course's engaging teaching style and the balance between theory and practice. The instructor's explanations were clear and concise, making complex topics easier to grasp. I also appreciated the hands-on projects, which allowed me to apply what I learned in real-world scenarios. Additionally, the collaborative discussions with peers enriched my understanding and provided diverse perspectives. The resources provided, such as datasets and coding examples, were valuable for deepening my knowledge in machine learning.
Reviewer Profile
Rimsha Tariq

5.0

“Mastering Machine Learning: A Comprehensive Journey Through Algorithms, Data, and Real-World Applications”
Completing this machine learning course was transformative. The well-structured curriculum, clear explanations, and practical projects made learning enjoyable. Hands-on assignments reinforced concepts, while community forums fostered collaboration. The instructors' expertise and passion were evident throughout. Overall, it equipped me with essential skills and confidence in the field. Highly recommended!
Reviewer Profile

5.0

Country Flag India
“Excellent Hands-On ML Algorithm Course”
This course provided a comprehensive introduction to machine learning algorithms with practical applications. I particularly enjoyed the blend of theory and real-world examples that made complex concepts easier to grasp. The hands-on projects were a highlight, allowing me to apply what I learned and reinforce key concepts. The instructors were knowledgeable and clear, offering great support throughout the learning process. Overall, this course significantly boosted my confidence in implementing ML algorithms in real-world scenarios.
Reviewer Profile

5.0

Country Flag India
“The effort of Great Learning to provide free machine learning certification is a plaudit for me.”
I loved the way the course covered so many topics in machine learning algorithms. It became easier to get grasp of complex ideas because of the systematic approach. The practical demonstrations of real-world applications of these algorithms showed how they can be applied to practical problems in realistic scenarios. Practically, I liked the projects for this reason: it was a good avenue for me to understand the theory and its practical application. One had to almost understand what principles are guiding every algorithm.
Reviewer Profile

5.0

Country Flag India
“The learning experience of a machine learning course typically involves several key components that help build foundational knowledge and practical skills. ”
The learning experience of a machine learning course typically involves several key components that help build foundational knowledge and practical skills. Here's what you can generally expect: Understanding of Core Concepts: You'll learn about the different types of learning paradigms such as supervised, unsupervised, and reinforcement learning.

Our course instructor

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Mr. Anirudh Rao

Machine Learning Expert

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778.5K+ 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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