Best Online Learning Platforms for Beginners in Machine Learning

Embarking on a journey to learn machine learning (ML) can be exciting, but choosing the right online learning platform is crucial. Whether you are a complete beginner or aspiring to build your own ML models, there are several platforms that can help you on your way. Let's explore some of the top options and provide guidance on how to select the most suitable one for your needs.

Coursera

Coursera is a popular online learning platform that offers a wide range of courses from top universities and organizations around the world. For beginners, Andrew Ng's Machine Learning course is an excellent choice. This course is completely free and introduces you to the field of machine learning in a comprehensive manner. Andrew Ng is a renowned figure in the field of ML, having started ML research at Google. His teaching style is straightforward and emphasizes clarity, making it easy for beginners to grasp complex concepts.

Udemy

Udemy is another popular platform offering both free and paid courses. While the free courses may not be as extensive, they are still valuable for getting started. Udemy's paid courses are highly regarded and often cover a wide range of topics in depth. If you prefer to invest in a more comprehensive course, Udemy is a good option to consider.

Udacity

Udacity offers free courses in both machine learning and deep learning. While these courses might not be as extensive as those on Coursera, they are a great starting point for beginners. The courses on Udacity are project-driven, which is beneficial for hands-on learners.

Foundation Building

As a beginner in machine learning, it's essential to have a solid foundation in programming and mathematics. While online courses can provide a great starting point, building a strong foundation in these areas is crucial. Here are some suggestions:

Take the Coursera course by Andrew Ng to get a basic understanding of machine learning principles. Explore additional MOOCs from universities such as IIT, CMU, etc., to strengthen your theoretical knowledge. Learn Python thoroughly, as it is the primary programming language used in ML.

Building Your Own Model

Building your own machine learning model is a challenging task that requires deep knowledge and experience. While online courses can provide a great foundation, they are unlikely to cover all the advanced topics and practical skills needed to develop a model from scratch. Here are some suggestions for further learning:

Earn a degree in ML/AI/MnC from reputable institutions like IITs or foreign universities. Focus on areas like TWC-LDA, which involves advanced statistical implementations.

Choosing a Credible Course

If you decide to go with an online or training institute course, here are some factors to consider to ensure the credibility of the course or institution:

Avoid getting lured by job placement guarantees, as no institution can guarantee employment. Ask about case studies and real-world applications, as general case studies may not be relevant for practical use. Ensure that the course covers extensive Python to advanced levels, including package publishing and production-grade coding. Prepare a list of questions for demo sessions or webinars to better understand the delivery method. Verify if the curriculum includes coding algorithms from scratch without the use of Python libraries. Since data science, ML, AI, and Python are open-source, avoid courses that cost a hefty fee. Ensure that the curriculum covers modern techniques such as MLops, Kubernetes (K8s) for cloud scaling.

Conclusion

Choosing the right online learning platform for machine learning can be a daunting task, but with the right knowledge and preparation, you can find the best fit for your goals. Whether you start with free courses or invest in more comprehensive training, the key is to build a solid foundation and be prepared for the challenges ahead. Happy learning!