Here's how you can acquire the most in-demand skills in Machine Learning right now.

Some suggestions: 1) don't ever start with mathematics. Begin with a programming language particularly Python 2) build some basic projects using pre-cooked code in python ml/dl libraries (house price prediction, image generation etc). Try to understand the mathematics behind your project or the algorithm you used intuitively. 3) Try writing the algorithms you used in your project from scratch

Start with mathematics. Understands the concepts of linear algebra, and calculus. Then combine the knowledge with statistics ( and of course the probability !!! ) Computer science skills are necessary. Learn the basics of programming, data structures, and OOP. Many resources include books, videos, code samples, and online courses you can benefit from.

Learn prompt engineering. If you know how to talk to existing AI models like ChatGPT, you can grow as quickly as possible since they can asnwer fundamental questions. The next is definetely learn python which is widely used and, i think, one of easiest tools to learn. Also, it is important to understand what job do ML Engineers do. It is better to not to learn the field only because it looks/sounds fancy these days.

More than what we learn, is important to be cautious about from whom we learn. I believe Linear Algebra and calculus I & II from MIT open courseware and Andrew NG Deep Learning Specialization are the best way to build a solid foundation for Machine Learning. Wait, we also need to do “Active Recall” after learning it. We cannot store everything permanently in our brain like computers. If only …

Start with foundational concepts like statistics, linear algebra, and probability. Personal Experience: "Online courses on Coursera helped me grasp the basics."

Exploring different ML algorithms is crucial for building a robust understanding of machine learning. Starting with simpler models like linear regression and decision trees allows you to grasp foundational concepts such as overfitting, bias-variance tradeoff, and model interpretability. As you progress to more complex algorithms like neural networks and SVMs, you'll encounter advanced topics like hyperparameter tuning and feature engineering, which are essential for optimizing model performance and tackling real-world problems effectively.

There are many machine learning algorithms. Each of them has different use cases and requirements. Understand supervised learning and unsupervised learning. Start with the easy ones like Linear Regression, Decision Trees, and K-means. Try to understand the concepts of overfitting and underfitting. When you are comfortable try to understand how the complex models work. Learn how to use XGBoost and Neural Networks. Learn the hyperparameter tuning, feature engineering, and evaluating the model performance.

I think gaining an intuitive understanding of simpler algorithms like linear regression would be a good starting point. This can be a motivation to gradually learn the math behind it with the help of linear algebra, learning about maximum likelihood, optimization etc, which would appear in most of other more complex algorithms as well.

Before studying algorithms, it is important to get to know what tasks are available in ML. In classic ML for example, we have Regression and Classification tasks. Once you are comfortable with tasks, then start picking up different algorithms depending on the task you working on and experiment to see which one you should go for. The field of ML/AI is all about experimenting and seeing which one way is working and which one is not.

Understand key algorithms like linear regression, decision trees, and neural networks. Personal Experience: "Studying these algorithms through hands-on coding was essential."

Working on projects is of course very important. Also, it is crucial to choose what project you need to work on. At the beginning try to independently work on widely known tasks such as house prediction. Next, try to train simple AI models that solve your daily problems. This is how you build an intuition about the field. More importantly, you are motivated to see your solutions that are actually working in real world scenarios.

Engaging in real-world projects is the most effective way to acquire in-demand machine learning skills. Projects provide hands-on experience with data preprocessing, model selection, training, and evaluation, mimicking real industry challenges. Start with datasets from platforms like Kaggle, focusing on diverse problems like image classification, NLP, or time-series analysis. Building end-to-end projects helps solidify theoretical knowledge and exposes you to practical issues like overfitting, feature engineering, and hyperparameter tuning. Collaboration on open-source projects and contributing to GitHub repositories can further enhance your practical skills and showcase your capabilities to potential employers.

Apply your skills and knowledge by working on real-world problems. Hands-on experience in end-to-end projects helps to make the skills and knowledge better and stronger. Share your projects and codes on platforms like Github, and Medium. Sharing will promote you.

Apply your knowledge by working on real-world projects. Personal Experience: "Building a sentiment analysis model improved my practical skills."

Apply your skills by working on real-world projects. Kaggle competitions, open-source projects, and internships are excellent ways to gain practical experience. Working on diverse datasets and problem statements enhances your understanding and showcases your abilities to potential employers.

Become a member of communities to stay updated — Linkedin, Stackoverflow, Github, etc. Become members of communities such as Google Developer Groups, and Amazon Communities. Communities help network and track the latest trends in many industries

Acquiring the latest in-demand ML skills often transcends traditional learning. As an open-source enthusiast, I actively engage with communities on platforms like HuggingFace, GitHub, and Kaggle. These platforms are invaluable for staying updated, sharing knowledge, and collaborating in general. Real-life projects and interactions within these communities are crucial as they provide hands-on experience and insights that go beyond conventional form of understanding.

Join online communities and forums to stay updated and seek help. Personal Experience: "Active participation in LinkedIn groups and Reddit communities kept me informed."

There are lots of resources that you can benefit from while learning ML. For theoretical knowledge, there are books, articles, Linkedin posts, and research papers. For hands-on experience, there are online platforms that you can learn and practice to gain hands-on experience

Leverage books, online courses, and tutorials to learn and improve. Personal Experience: "Books like 'Deep Learning' by Ian Goodfellow were invaluable."

To acquire the most in-demand skills in Machine Learning, focus on continuous practice. Regularly work on diverse ML projects to apply theoretical knowledge. Participate in online competitions like Kaggle to solve real-world problems. Engage with the ML community through forums and study groups to learn from others' experiences. Keep up with the latest research and advancements by reading academic papers and attending webinars. This hands-on approach, coupled with consistent learning and community engagement, will help you master the essential ML skills.

Consistently practice by solving problems and coding daily. Personal Experience: Regular practice on platforms like Kaggle enhanced my skills.

Read a lot of research papers ! Deeping your knowledge in the latest research findings, directions, and trends keep you updated with the recent innovations and how they actually work which affects your understanding in applying them. After that, get involved in complex projects that applies the research you read or in your own research instead of valueless kaggle portfolio projects or simple youtube tutorials !. Having a solid scientific base will definitely boost your ML learning journey.