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This course specifically created for AI Aspirants who are eager to know about the road map to AI and ML
49 m
Enrol in this course that teaches you Python and R Programming. This detailed course on Data Science, Machine Learning, Data Analysis and Data Visualisation helps you understand all its essential concepts.
8 h 7 m
Supervised, Unsupervised, and Reinforcement Learning with Python Demos
1 h 57 m
Learn how to use CatBoost in Python for classification and regression, and how it compares to XGBoost.
5 m
An Introduction to Kubeflow Training and Certification is provided at no cost.
1 h 11 m
Learn how to plan, deploy and run a Machine Learning problem on AWS and Meeshkan
1 h 27 m
Machine Learning from Stanford University. Machine learning is the science of getting computers to act without being explicitly programmed.
60 h
Learn Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning from deeplearning.ai. If you are a software developer who wants to build scalable AI-powered algorithms
30 h
Learners will apply their deep learning knowledge and expertise to a real-world challenge in this capstone. They will create and test a deep learning model using a library of their choice.
16 h
By the end of this course, you will have learned various strategies for recognising biases in data, how to maintain and build trust with user data and privacy, and what it takes to construct a responsible governance strategy.
10 h
Learn Sequences, Time Series and Prediction from deeplearning.ai. If you are a software developer who wants to build scalable AI-powered algorithms, you need to understand how to use the tools to build them.
8 h
This course focuses on models currently in production at a fictitious streaming media company. There is an overview of IBM Watson Machine Learning. You will create your own API in a Docker container and learn about container management with Kubernetes.
17 h
By the end of this course, you'll understand how to use AI to streamline all HR functions and how to work with data to harness the power of machine learning.
9 h
Free Machine Learning courses online cover core topics like supervised learning, neural networks, and data science. These self-paced ML courses help you understand model building, data processing, and AI applications. Whether you're a beginner or advanced learner, these free classes provide a solid introduction to machine learning concepts.
Online machine learning courses free of charge offer flexibility and accessibility. From beginner tutorials to advanced AI resources, you can start learning at your own pace without a financial commitment. Many courses also offer free certifications, adding value to your professional profile.
Free ML courses with certificates are available on platforms like Coursera and edX. To earn these, complete all modules and pass assessments. These certificates validate your knowledge in machine learning, which can be displayed on your resume or LinkedIn profile.
Free machine learning courses offer numerous advantages like cost-effectiveness and flexible schedules. However, they may lack personalized support and detailed feedback compared to paid courses. On the upside, free courses cover most introductory topics, making them perfect for learners just starting in AI and ML.
To make the most of free AI and ML courses online, engage with community forums, participate in hands-on projects, and review your progress regularly. Supplement these courses with additional resources to deepen your understanding of AI and data science.
Yes, many free courses offer certificates upon completion, particularly on platforms like Coursera and edX.
Coursera, edX, and Udemy provide high-quality free ML courses with extensive resources and tutorials for beginners.
Courses range from a few weeks to several months, depending on your pace and the course content.
You'll learn core machine learning concepts, including data processing, supervised learning, AI integration, and model building.
Most platforms allow continued access to materials even after course completion, though some may have time limits.
Free courses cover essential topics and are ideal for beginners, but paid courses often offer more in-depth content and personalized support.
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