Machine learning can be considered to be the application of artificial intelligence that provides systems learning ability so that performance is automatically enhanced from experience without being directly programmed. Machine learning stresses the development of computer programs that can access various data for using those data to gain learning ability. Machine learning utilizes statistical algorithms, text analytics, series analytics, and other valuable domains of analytics to reveal the pattern of a specific set of data. As machine learning saves time by automating processes, it has gradually turned to be a study of interest.
How to decide your learning path?
People often ask “How to get started with machine learning?”
The answer to this question is: Machine learning is vast technology, one needs to decide their framework and period to gel in with machine learning technology.
This article helps you with the most popular machine learning videos, tutorials, and courses list in 2021. It is always important to take baby steps and mold it as per your wish.
How to pick courses?
The course should fit these criteria:
- Vast machine learning content.
- Prefer on-demand content or offered every few months.
- Opt for an interactive online course, so no books or read-only tutorials.
- Contain programming assignments for practice and hands-on experience
- Explain how the algorithms work mathematically
- Have engaging instructors and interesting lectures
- Have above-average ratings and reviews
The courses listed below in this article, ask students to have prior programming knowledge, linear algebra and statistics knowledge. These prerequisites are necessary for machine learning as it is an advanced disciple.
Best Courses/resources for Machine Learning
You can learn machine learning online with the below references to aid students/professionals in learning detailed aspects of machine learning.
PG Program in Artificial Intelligence and Machine Learning – Great Learning
Great Learning offers the best artificial intelligence certification in collaboration with Texas University (World top 6th university) to let students/professionals understand all the aspects of machine learning.
Duration: 12 months’ and Online Learning with Mentorship
- Learn anytime, anywhere
- Weekly online mentorship by experts
- Network with people of similar interest
- Dedicated program support
- Certificate from University of Texas
GitHub is the best place to learn to code practically! Many brilliant people provide detailed codes for the entire software community. GitHub helps you to utilize the code that is written by others and fork them on your desktop and also you can test these forked codes, validate them and modify them to suit your requirements.
Yeah! You can even publish your projects and codes to the entire community and help other developers to enhance their knowledge.
Google AI is a section of Google that is for free and provides information related to Artificial Intelligence and Machine learning and this is one of the extremely helpful, useful, and resourceful websites. Google aims at educating all the users who are interested in AIML hence, it is a great resource to learn and gain knowledge about AIML.
YouTube is a fantastic platform for learning Machine Learning through video content. There are umpteen videos put up on youtube to learn and gain knowledge. Some of my recommendations are Google cloud tech, Great Learning, Sentdex, Corey Schafer, etc., among so many other fabulous channels. These are some of the best YouTube channels to check out for brilliant in-depth explanations of concepts.
TensorFlow is one of the best deep learning frameworks to develop Machine Learning projects. The TensorFlow website provides official documentation, guides, useful resources, to get started, as well as create projects from scratch.
Reddit, Quora, And Stack Overflow
These 3 resources tend to serve a similar purpose, i.e., community interaction and answers to several questions.
Reddit is a place for users to interact with Machine Learning groups that are extremely informative and obtain further answers to some of the queries that you might encounter.
Quora is a question-answer community, where you can drop questions and tag/asks experts to answer your questions and there are also Quora spaces for dedicated programs like machine learning, artificial intelligence, data science, etc.
Stack Overflow is the best resource for receiving precise answers to your technical questions.
Many AI researchers have a strong online presence in their websites, wiki pages, Twitter profiles, Quora profiles. So, I have included all of those here:
- Sebastian Thrun (GScholar / Quora / AMA)
- Yann Lecun (GScholar / Quora / AMA)
- Nando de Freitas (Wikipedia / Twitter /)
- Andrew Ng ( GScholar / Quora / AMA)
- Daphne Koller (GScholar / Quora / Quora Session)
- Adam Coates (Twitter / GScholar / AMA)
- Jürgen Schmidhuber (Wikipedia / GScholar / AMA)
- Geoffrey Hinton (Wikipedia / GScholar / AMA)
- Terry Sejnowski (GScholar / AMA)
- Michael Jordan (Wikipedia / GScholar / AMA)
- Yoshua Bengio ( GScholar / AMA)
- Ian Goodfellow (Wikipedia / Twitter /)
- Andrej Karpathy ( GScholar / Quora)
- Richard Socher (Twitter / GScholar / Interview)
- Christopher Manning (Twitter / GScholar)
- Larry Carin (GScholar)
- Dan Jurafsky (Wikipedia / Twitter / GScholar)
There are well-known organizations that are dedicated to furthering AI research. Below are the ones with websites and Twitter accounts.
- OpenAI / Twitter
- GreatLearning / Twitter
- DeepMind / Twitter
- Google Research / Twitter
- AWS AI / Twitter
- Microsoft Research / Twitter
- Baidu Research / Twitter
- IntelAI / Twitter
There are many video courses and tutorials available online now for free. These courses would keep you busy for months:
- Coursera — Machine Learning
- Machine Learning
- Practical Machine Learning Tutorial with Python
Every one of the resources mentioned here contains tons of content and valuable information which helps you to choose/decide on a course that’s worth your time and energy.
If you have any questions, leave them in the comments below.
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