You're interested in Machine Learning. What's the best way to develop your skills?

No matter where you are in your journey, the way/path to develop the skills stays the same: ?? Read more and ??? Apply more Push yourself in an environment where it becomes easier for you to do it. Leverage communities/groups for the it. When you see people around you doing the same, it'd naturally drive you to do "more". With this, you will get better in no time - if consistent. I wish you all the best! :)

In my experience, learning in layers has been very beneficial. Instead of watching complex advanced lectures on a topic as a beginner, try learning in layers. I first get a summary of the concept, what it achieves and how it works. I then learn the next layer using explainer videos from channels like StatQuest on YouTube. Only after this, I try to watch the lectures from resources like Stanford and MIT, or textbooks with complex mathematical notation to understand in detail information.

Before delving into advanced machine learning topics, it's crucial to establish a solid foundation in the basics. From my perspective, this entails grasping core concepts like supervised, unsupervised, and reinforcement learning, as well as familiarizing oneself with common algorithms such as linear regression, logistic regression, k-means clustering, decision trees, and neural networks. Understanding principles of data preprocessing, feature engineering, model evaluation, and optimization is equally essential. Learning the basics from various resources like online courses, books, blogs, podcasts, or videos provides a strong groundwork for further exploration in machine learning.

After a lot of trial and error, I realized that the best way to learn is the cross method. In the form - you learn one or more skills superficially and in the form | You learn a skill in depth. In the end, you will realize that you are an expert in one skill and you have useful information in a few other skills that you will learn deeply in the future by spending the least amount of time.

Start your journey in machine learning (ML) with the basics! ?? - Math Foundations: Dive into linear algebra, statistics, and calculus. ?? - Programming: Get comfortable with Python, the go-to language for ML. ?????? - Key Libraries: Explore essential libraries like NumPy for numerical computations and pandas for data manipulation. ?? Focus on understanding these core areas to build a strong foundation in ML. Happy learning! ??

Engage in real-world projects to hone your machine learning skills effectively. Practice applying learned concepts to solve meaningful problems, which challenges your abilities and fosters practical expertise. Work on diverse projects spanning various domains, each offering unique learning opportunities. Collaborate with peers, seek mentorship, and actively participate in online communities to gain insights and feedback. Continuous project-based learning not only strengthens technical proficiency but also cultivates problem-solving abilities crucial for success in machine learning endeavors.

If you're keen on developing your machine learning skills, there's no better way than practicing with projects. Dive into hands-on projects that tackle real-world problems, whether it's predicting stock prices, analyzing sentiment in social media, or identifying objects in images. By working on projects, you'll gain valuable experience in data preprocessing, model selection, and evaluation techniques. Additionally, you'll encounter challenges that prompt you to explore new algorithms and methodologies, fostering continuous learning and growth. Collaborate with peers, contribute to open-source projects, and participate in online competitions to further enhance your skills and expand your knowledge base in machine learning.

One useful tip with regards to practising with projects is to add something functional on your own apart from the tutorials you learn from. This sets you apart from everyone else. Also, try applying related skills in the project such as deploying your app, using Git etc

Start with a Kaggle challenge like Titanic or another, but then try to do a project where you have to collect the data and choose which features to use, as this is a very important step for practice in companies

Business leaders must grasp the potential of leveraging machine learning(ML) for their organizations. They don't necessarily need a science background or expertise in mathematics and coding; instead, focusing on practical application-level learning enables them to innovate effectively. Passive learning might not suffice for understanding the fundamentals of ML ; hands-on experience is invaluable. Learning 1. how important data is in ML, what sort of data makes an impact and how much data is necessary. 2. how to train a ML model, and 3. how to evaluate and improve its performance. ML is depicted as an evolutionary, iterative, and experimental process, underscoring the absence of a singular path to problem-solving.

ML is fantastic as it can be integrated into almost all domains. Before starting work in a particular domain, try to get some domain expertise and perform a preliminary survey of existing applications and projects. This will help a long way in assimilating existing resources and coming up with something new.

In the dynamic field of Machine Learning, continuous skill development is essential. Beyond mastering foundational concepts, expanding your toolkit is crucial for staying competitive. Embrace diverse learning resources such as online courses, textbooks, and interactive platforms like Kaggle. Engage in collaborative projects and contribute to open-source initiatives to gain practical experience and build a network within the ML community. Cultivate a curiosity for new technologies and methodologies, exploring areas like deep learning, reinforcement learning, and natural language processing. Never underestimate the value of hands-on practice and experimentation, iterating and refining your skills along the way.

Apart from completing your projects, exhibit your knowledge through an online portfolio and ask community members for feedback. Provide feedback for members who are relatively new to ML and contribute to the community by writing tutorials and articles. Also try to contribute to open source projects, as they will showcase your skills effectively.

The best way to learn is to put yourself in uncomfortable situations where you may not know everything. There is no perfect day to get started on a new technology after learning everything about it. Rather, learn by making mistakes quickly and from mentors.