Where to learn machine learning free
What Is Machine Learning?
In the subfield of artificial intelligence known as machine learning, which aims to accelerate innovation and process optimization, systems can “learn” from data, statistics, and trial-and-error. Machine learning, which gives computers the ability to learn in a manner that is comparable to that of humans, enables them to solve some of the most challenging problems in the world, such as the study of cancer and the effects of climate change.
The majority of computer programs (also referred to as explicit knowledge) use code to tell them what to do or what information to keep. Anything that can be quickly written down or recorded, like manuals, videos, and textbooks, is included in this knowledge. Through machine learning, computers acquire tacit knowledge in a manner analogous to our own personal experience and context. Composed or verbal correspondence is challenging to use to pass this sort of information on to someone else.
One type of facial recognition is tact knowledge. Although we are aware of a person’s face, it is challenging for us to precisely describe how or why we remember it. Because we rely on our personal knowledge banks to connect the dots, we immediately recognize a person based on their face. Another example is riding a bicycle. It’s much easier to show someone how to ride a bicycle correctly than it is to understand it.
No longer are billions of lines of code used to perform calculations. With the help of machine learning’s tacit knowledge, computers can connect dots, recognize patterns, and make predictions based on previous knowledge. Machine learning has become a preferred technology for almost every industry, including government, weather, and financial technology, due to its use of implicit knowledge.
How Does Machine Learning Work?
In machine learning, the input data can come from a variety of places, such as open data registries like Amazon Web Services, data set search engines, and government websites. Just like previous experiences do for humans, this data provides historical information for machine learning models to use when making future decisions.
Algorithms search this data for patterns and trends as the next step in making accurate predictions. As a result, machine learning can learn from the past to predict what will happen in the future. Expectation precision normally increments with the size of a group’s contribution to AI programming.
The idea is that these tasks should be handled by machine learning algorithms on their own with little assistance from humans. This speeds up various processes as machine learning begins to automate many aspects of various industries.
Best Website to learn Machine Learning:
1. TensorFlow: Google’s TensorFlow is a popular open-source machine learning framework. It provides an adaptable ecosystem for developing and deploying machine learning models on a number of different platforms. TensorFlow users can get started with the extensive documentation, tutorials, and examples on the website.
2. PyTorch: PyTorch is another famous open-source AI system that underscores dynamic computational diagrams and a Pythonic interface. It gives you a lot of tools and libraries for making models for deep learning. The site offers exhaustive documentation, instructional exercises, and assets for learning PyTorch.
3. scikit-learn: The Python machine learning library scikit-learn is comprehensive. It offers a simple API for classification, regression, clustering, and dimensionality reduction, among other machine-learning operations. To assist users in making efficient use of Scikit-learn in their projects, the website provides documentation, examples, and tutorials.
4. Keras: Keras is a Python-based high-level API for neural networks. It supports a number of backend frameworks, including TensorFlow and Theano, and aims to provide a user-friendly interface for building deep learning models. Keras usage guides, examples, and documentation are all available on the website.
5. OpenAI: OpenAI is a group that works to improve and promote artificial intelligence. They work on a variety of projects, including robotics, reinforcement learning, and natural language processing. Their research, publications, and initiatives, including their well-known GPT models, are described on their website.
6. Machine Learning Mastery: Jason Brownlee, a well-known data scientist, and author, runs the website Machine Learning Mastery. On a variety of machine learning topics, including algorithms, methods, and best practices, the website provides tutorials, articles, and resources. It is a useful resource for new and seasoned practitioners alike.
https://machinelearningmastery.com/
7. Towards Data Science: A forum for discussing data science, machine learning, and artificial intelligence is Towards Data Science. It contains opinions, tutorials, and articles written by data scientists and industry professionals. A useful tool for staying up to date in the field, the website covers a wide range of subjects.
https://towardsdatascience.com/
8. Kaggle: Kaggle is a platform for data science and machine learning competitions that is driven by the community. It hosts competitions with cash prizes, houses a wide range of datasets, and makes it possible for data science projects to work together. The site likewise includes a gathering where clients can clarify pressing issues, share bits of knowledge, and gain from one another.
9. MIT Deep Learning: MIT Deep Learning is a website dedicated to deep learning research and education. It provides resources such as tutorials, lecture videos, and research papers from MIT’s deep learning courses. The website is a valuable source of knowledge for those interested in delving into the field of deep learning.
10. Stanford CS229: Stanford University’s machine learning course website is CS229. It gives address notes, tasks, and other course materials for self-study. The site covers an extensive variety of AI subjects, from essential ideas to cutting-edge calculations, making it a brilliant asset for learning.
11. Microsoft Research: Microsoft Exploration is the examination division of Microsoft, zeroing in on different areas of software engineering, including man-made consciousness and AI. Their publications, collaborations, and research projects are all described on the website. It is a useful resource for keeping up with the most recent developments in the field.
12. Google AI: Google AI is the official Google website for projects and research on artificial intelligence. It presents research papers, tools, and applications from the company’s AI initiatives. Additionally, the website provides developers and researchers with AI-related news, events, and resources.
13. NVIDIA Developer: NVIDIA Developer is a resource center for NVIDIA GPU-based machine learning and deep learning. It offers libraries, tools, and software development kits (SDKs) for accelerating machine learning tasks with NVIDIA hardware. The site additionally offers instructional exercises, documentation, and gatherings for designers working with NVIDIA GPUs.
14. DataCamp: The interactive DataCamp courses on data science and machine learning can be found on an online learning platform. It offers projects and exercises that users can use to learn and apply machine-learning techniques. From basic courses to advanced machine learning concepts, the website covers a wide range of subjects.
15. fast.ai: Fast.ai is a website that offers deep learning tutorials and practical courses. Through a simplified approach and examples, it aims to make deep learning accessible to a broad audience. The website provides learners with access to free courses, notebooks, and forums where they can investigate and debate deep learning concepts.
16. Papers with Code: A platform called Papers with Code offers research papers along with their code implementations. By gaining access to the research papers and the code that was used to achieve the results, it makes it possible for researchers and practitioners to investigate the most recent developments in machine learning. The website is a useful tool for comprehending and replicating research and covers a variety of machine-learning topics.
17. Distill: Distill is a journal that emphasizes concise machine learning concepts and explanations. Articles and interactive visualizations are included with the intention of improving comprehension of intricate machine-learning topics. The website provides a one-of-a-kind learning experience and is known for its high-quality content.
18. Arxiv: Arxiv is a repository for research papers in a variety of scientific fields, including machine learning, that is accessible to the public. It houses an extensive collection of research papers and preprints from researchers all over the world. The website is a great place to get access to cutting-edge research and stay up to date on machine learning developments.
19. Reddit: A community for machine learning-related discussions, news, and resources is the machine learning subreddit on Reddit. It is a platform where machine learning enthusiasts can ask questions, share insights, and talk to one another. The subreddit is a useful resource for learning about the community’s experiences and points of view.
20. Medium: Machine learning tutorials and articles can be found on Medium, a popular blogging platform. Medium is a useful resource for learning about and staying up to date in the field due to a large number of experts and practitioners who share their insights and knowledge regarding machine learning.
Anyone who is interested in machine learning can find a lot of information, tutorials, research papers, and community engagement opportunities on these websites. You will gain a comprehensive understanding of the field and be able to delve deeper into specific topics of interest by investigating them.
Best Book to learn Machine Learning:
>The Hundred-Page Machine Learning Book by Andriy Burkov
>Machine Learning for Dummies by John Paul Mueller and Luca Massaron
>Introduction to Machine Learning with Python: A Guide for Data Scientists by Andreas C. Müller and Sarah Guido
> Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems by Aurélien Géron
>Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies by John D. Kelleher, Brian Mac Namee, and Aoife D’Arcy
>Data Mining: Practical Machine Learning Tools and Techniques by Ian H. Witten, Eibe Frank, Mark A. Hall, and Christopher J. Pal
>Machine Learning for Hackers by Drew Conway and John Myles White
>AI and Machine Learning For Coders: A Programmer’s Guide to Artificial Intelligence by Laurence Moroney
>Machine Learning in Action by Peter Harrington
>Artificial Intelligence: A Modern Approach by Stuart Rusell and Peter Norvig
>Machine Learning: A Probabilistic Perspective by Kevin P. Murphy
>Advanced Machine Learning with Python: Solve data science problems by mastering cutting-edge machine learning techniques in Python by John Hearty
>Reinforcement Learning: An Introduction by Richard S. Sutton and Andrew G. Barto
>Causal Inference in Statistics: A Primer by Judea Pearl, Madelyn Glymour, and Nicholas P. Jewell
Table of content
Best Youtube to learn Machine Learning:
1. Sentdex: A YouTube channel focused on machine learning and Python programming tutorials: [Sentdex](https://www.youtube.com/user/sentdex)
2. 3Blue1Brown: A channel that covers various mathematical concepts behind machine learning and deep learning: [3Blue1Brown](https://www.youtube.com/c/3blue1brown)
3. Andrew Ng: The YouTube channel of Andrew Ng, a renowned AI researcher, and educator, featuring lectures on machine learning and AI: [Andrew Ng](https://www.youtube.com/c/andrewng)
4. StatQuest with Josh Starmer: A channel that provides intuitive explanations of complex machine learning and statistical concepts: [StatQuest with Josh Starmer](https://www.youtube.com/c/joshstarmer)
5. deeplizard: A channel that offers tutorials on machine learning, deep learning, and computer vision: [deeplizard](https://www.youtube.com/c/deeplizard)
6. Two Minute Papers: A channel that covers the latest research papers in computer science and AI, including machine learning: [Two Minute Papers](https://www.youtube.com/c/TwoMinutePapers)
7. Siraj Raval: A YouTube personality known for his engaging tutorials on machine learning and AI: [Siraj Raval](https://www.youtube.com/c/sirajraval)
8. Google Developers: The official YouTube channel for Google Developers, featuring videos on machine learning, TensorFlow, and AI applications: [Google Developers](https://www.youtube.com/c/GoogleDevelopers)
9. CodeEmporium: A channel that provides practical tutorials on machine learning projects and algorithms: [CodeEmporium](https://www.youtube.com/c/CodeEmporium)
10. Computerphile: A channel that explores computer science topics, including machine learning, through interviews and explanations: [Computerphile](https://www.youtube.com/c/Computerphile)
11. Machine Learning TV: A channel by Google Cloud Platform that offers tutorials and talks on machine learning concepts and applications: [Machine Learning TV](https://www.youtube.com/c/MachineLearningTV)
12. Krish Naik: A channel that covers a wide range of machine learning and data science topics with practical examples and tutorials: [Krish Naik](https://www.youtube.com/c/KrishNaik19)
13. Lex Fridman: The YouTube channel of Lex Fridman, an AI researcher and host of the popular “AI Podcast,” featuring interviews with experts in the field: [Lex Fridman](https://www.youtube.com/c/lexfridman)
14. Data School: A channel that focuses on data science and machine learning tutorials using Python and popular libraries: [Data School](https://www.youtube.com/c/dataschool)
15. TensorFlow: The official YouTube channel for TensorFlow, featuring tutorials, guides, and talks on using the framework for machine learning tasks: [TensorFlow](https://www.youtube.com/c/TensorFlow)
16. PyTorch: The YouTube channel for PyTorch, providing tutorials, deep learning concepts, and updates on the framework: [PyTorch](https://www.youtube.com/c/PyTorch)
17. Tech With Tim: A channel that covers machine learning, AI, and Python programming with tutorials and projects: [Tech With Tim](https://www.youtube.com/c/TechWithTim)
18. Applied AI Course: A channel that offers a comprehensive course on applied AI, including machine learning and deep learning topics: [Applied AI Course](https://www.youtube.com/c/AppliedAICourse)
19. Krish Naik: A channel by Krish Naik, an AI and data science educator, featuring tutorials, projects, and case studies: [Krish Naik](https://www.youtube.com/c/KrishNaik19)
20. The Coding Train: A channel focused on creative coding and machine learning using frameworks like TensorFlow.js and p5.js: [The Coding Train](https://www.youtube.com/TheCodingTrain)
These YouTube channels cover a wide range of machine-learning topics, from beginner-level tutorials to advanced concepts and research papers. They are excellent resources for learning and staying updated in the field of machine learning.