Here's how you can master Machine Learning from scratch: the best online resources and courses.

One place to start is Kaggle. It has a series of free online micro-courses that lead to 'getting started' competitions. This structured learning path helps you learn faster by practicing immediately. By coding, you turn abstract theories into skills. Experiment with the code and observe the impact on the results. The important part is not to hurry. Move forward only if you understand what you're doing. Always experiment and tweak. You can also start with a project that interests you, even if it's simple. Apply what you learn as you go. This approach will keep your learning fun and motivating, gradually building your skills.

Python is a great starting point for beginners in machine learning, and it's important to emphasize the supporting libraries. Key Python libraries such as TensorFlow, PyTorch, Scikit-learn, Pandas, NumPy, and Keras are crucial for effectively building and deploying machine learning models. By focusing on these libraries, you can efficiently address multiple needs - learning python and ML - All at once.

To learn ML, begin by learning the fundamentals. Andrew NG's courses on Coursera, with some available on YouTube, offer an excellent starting point. Moreover, Stanford's free courses are excellent for solidifying your ML foundation. As you continue learning, integrate coding with your AI studies rather than focusing on programming alone. Engage with practical applications by exploring projects and short courses on GitHub and Kaggle. Here are my top suggestions: - Machine learning specialization (Coursera) - Deep learning specialization (Coursera) - NLP specialization (Coursera) - Machine Learning Engineering for Production (Coursera) - Deeplearning.ai free short courses - Stanford's free courses - GitHub and Kaggle projects

Machine Learning is a gargantuan field and is only growing larger by the day. It is getting immense traction across all sorts of industry and proving to be a skill desirable by all kinds of techies. To step foot in such a field, it is essential to have a good understanding of mathematics, statistics, probability and curiosity to keep learning. There are a lot of online resources that prove to be very useful. Statquest, AndrewNg and Andrej Karpathy courses are excellent to dive deep into the field. Besides, hands-on project will deepen the knowledge obtained alongside the theory.

Here's the path: Learn Python Start with basic ML (Linear Regression) Learn mathematics related to the ML you are learning. Learn Decision Tree Take the Intro and Intermediate course from Kaggle Solve Titanic problem Andrew Ngs Courses from Coursera.

While online courses are popular for learning core concepts in machine learning, I find YouTube videos to be more interactive and provide step-by-step guidance with examples.

Udacity - Intro to Machine Learning with PyTorch or TensorFlow: These courses are great for learning the core concepts and algorithms in machine learning, with hands-on practice using popular ML frameworks. edX - Principles of Machine Learning by Microsoft: This course covers essential theories and helps develop practical skills in applying machine learning algorithms.

To master machine learning from scratch, start by building a strong foundation in core concepts like linear algebra, calculus, probability, and statistics. Online resources like Andrew Ng's "Machine Learning" course on Coursera, MIT's OpenCourseWare, and the "Deep Learning" book by Ian Goodfellow provide excellent introductions to these fundamental topics. Additionally, explore programming languages like Python and its libraries like NumPy, Pandas, and Scikit-learn, which are essential tools for implementing machine learning algorithms.

Start with foundational concepts like linear algebra, calculus, and statistics. Then, dive into beginner-friendly online resources and courses like Coursera's "Machine Learning" by Andrew Ng, fast.ai's Practical Deep Learning for Coders, and the book "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" by Aurélien Géron. Practice by implementing algorithms and working on projects to solidify your understanding. Consistency and hands-on experience are key to mastering machine learning.

Andrew Ng's courses combine technical knowledge with practical hands-on exercises. It's beneficial to partner with either a fellow beginner or a seasoned expert to review and monitor your progress weekly. While working hands-on can be daunting due to the various challenges that arise, some solutions are readily available through a quick Google search.

Here's what worked for me: - Utilize online platforms like Coursera/Khan Academy for courses on linear algebra/ calculus/probability theory - Master Python through resources like Codecademy /the official Python documentation. - Enroll in Andrew Ng's Machine Learning course on Coursera/other resources to understand foundational ML concepts -Join Kaggle /explore dataset/apply theoretical knowledge into projects - Explore deep learning/NLP/computer vision on platforms like Udacity/Fast.ai - Create GitHub profile/showcase projects/ collaborate with others in the ML community - Engage in forums like Reddit's r/MachineLearning, /LinkedIn groups to connect with experts - Follow blogs/podcasts/academic papers & stay abreast of the latest trends

Try to gain knowledge across various categories where ML is applied, like NLP, Computer Vision, Reinforcement Learning and other such fields. Kaggle datasets can be used to gain practical knowledge on these areas. Some platforms I used- PadhAI by OneFourth labs- a course taught by IIT Madras professors. Coursera- NLP and Computer Vision by DeepLearning.ai

Hands-on experience through practical projects is undeniably crucial in mastering Machine Learning. However, it's not just about completing projects but understanding the nuances and challenges that come with each stage of the ML workflow. While online resources offer project-based learning, it's essential to choose projects that push your boundaries, require problem-solving, and encourage experimentation. Quality over quantity is key; a well-executed, challenging project can speak volumes more than multiple simpler ones.

Dive into advanced topics through online resources and courses like Stanford's CS229, Andrew Ng's Machine Learning on Coursera, and Deep Learning Specialization on Coursera by Andrew Ng. Supplement with books like "Pattern Recognition and Machine Learning" by Christopher M. Bishop and "Deep Learning" by Ian Goodfellow. Practice with real-world datasets and participate in Kaggle competitions to apply your skills. Stay updated with research papers on arXiv and attend conferences like NeurIPS and ICML for cutting-edge advancements.

DeepLearning.AI - Deep Learning Specialization: This series on Coursera is excellent for diving into more advanced topics in deep learning, taught by Andrew Ng. MIT OpenCourseWare - Artificial Intelligence: For a more academic approach to advanced topics, MIT’s free courseware is an invaluable resource.

Stack Overflow and GitHub: Engaging with communities on these platforms can provide support and insights from fellow learners and professionals. Meetup.com: Look for local or virtual ML meetups to connect with like-minded individuals and experts in the field.

Engaging with the Machine Learning community offers more than just networking; it's a resource for collective learning and problem-solving. While forums and social media groups are beneficial, the quality of interactions matters. Look for communities where experts share valuable insights, where discussions are constructive, and where you can contribute meaningfully. Being an active member and providing value can lead to more enriching experiences and connections.

Arxiv.org: Stay updated with the latest research papers in machine learning. Google Scholar Alerts: Set up alerts for specific ML topics to get new papers and articles directly in your inbox.

In the dynamic realm of machine learning, continuous learning isn't just a supplement to mastery—it's the very essence of it. Embracing a perpetual student mindset, I navigate a labyrinth of evolving concepts and techniques, leveraging a diverse array of resources like Udemy, Coursera, and insightful blogs such as Machine Learning Diaries"on Substack. With each webinar attended, research paper devoured, and code snippet dissected, I not only expand my knowledge but also refine my approach, honing my skills to meet the demands of an ever-changing landscape. It's not just about staying ahead; it's about thriving in the exhilarating whirlwind of innovation, where every lesson learned is a stepping stone toward greater understanding and impact.

Udemy, Coursera, and even the demos on Tensorflow.org are very helpful. Focus on getting the code under your fingers, copy everything verbatim so you understand every single line of code.

LLM and other new Multi-modal techniques evolving rapidly in the market all have their roots in Machine Learning. Hence, it is essential for an aspiring ML Engineer to keep themselves up to date. It is hard, but there are many ways to help keep pace with the advancements. Reading new research papers in the field will give a deep understanding of the research activities going on in ML.

Consider pursuing certifications or online courses from reputable institutions and platforms such as Coursera, edX, or Udacity. These courses often provide hands-on experience and access to industry experts, helping you master Machine Learning more effectively.

To master ML you need to follow certain steps : 1. Start with some courses: Machine Learning, Coursera , Andrew NG Youtube Channels , Krish Naik Machine Learning & Deep Learning, iNeuron Machine Learning A-Z™ - udemy IBM Data Science Professional Certificate 2. Start practicing code: GitHub, Kaggle, Analytics Vidya Hackathon 3. Reading Blogs : Medium, arxiv.org, Towards Data science

On top of the many great resources that provide you the necessary knowledge, don't forget that hands-on projects are another valuable starting point. Instead of learning the theory first and then apply it, you can always begin with a problem you want to tackle in a new way and see which alternative methods are available and why they represent better choices in that context. While working on your task, you can build a great deal of knowledge about the topic bit by bit without being overwhelmed by all the literature regarding it beforehand.

- Consider specializing in specific areas within machine learning, such as healthcare, finance, computer vision, or natural language processing, based on your interests and career goals. Look for specialized courses and resources tailored to these domains. - Strengthen your mathematical foundation by delving deeper into topics like optimization theory, convex optimization, probability theory, and information theory. Resources like "Convex Optimization" by Stephen Boyd and Lieven Vandenberghe can be beneficial.