Here's how you can kickstart your Machine Learning journey with top online resources.

Here's how you can kickstart your Machine Learning journey with top online resources. Start with ??????????????-?????????? ????????????????, a highly effective method because it helps you apply theoretical knowledge to real-world problems. Websites like Coursera, LinkedIn Learning and Udemy offer comprehensive courses that include hands-on projects. Platforms like Kaggle provide datasets and competitions where you can practice building models. GitHub is an excellent place to find open-source projects to contribute to and learn from. By working on projects, you reinforce your learning and build a portfolio showcasing your skills to potential employers.

If you want to appreciate the subtleties of relativity theory and quantum mechanics, you first need to be comfortable with differential equations, multivariate calculus, and differential geometry. In a similar vein, before you start learning Machine Learning, you should familiarize yourself with descriptive statistics, probability theory, inferential statistics, regressions. Also focus on Bayesian statistics and Markov models. You should also be familiar with Linear Algebra (matrices, eigenvalues/eigenvectors, PCA), Calculus (Derivatives and Integrations), and Graph Theory.

To effectively understand relativity theory and quantum mechanics, a solid foundation in differential equations, multivariate calculus, and differential geometry is essential. Similarly, before delving into Machine Learning, it is crucial to be well-versed in descriptive statistics, probability theory, inferential statistics, and regressions, with an emphasis on Bayesian statistics and Markov models. Additionally, proficiency in Linear Algebra (including matrices, eigenvalues/eigenvectors, and PCA), Calculus (both derivatives and integrations), and Graph Theory is necessary.

Begin with foundational courses on platforms like Coursera and edX. Courses like Andrew Ng’s Machine Learning on Coursera provide a strong grounding in algorithms, linear regression, and data preprocessing, setting a solid base for your ML journey.

Embarking on a Machine Learning journey can be both exciting and overwhelming. The first step is to build a strong foundation in the basics. Coursera offers an excellent starting point with the "Machine Learning" course by Andrew Ng. This course covers fundamental concepts such as supervised learning, unsupervised learning, and best practices in machine learning. With its mix of theory and practical exercises, it provides a comprehensive introduction that equips beginners with the necessary skills to tackle more advanced topics.

Machine learning is inherently practical, with coding at its core. Mastering languages like Python, prevalent in the ML community, is essential. Engaging in interactive coding exercises on online platforms is invaluable, reinforcing theoretical knowledge and providing hands-on experience in solving real-world problems.

Reinforce your learning by coding along with tutorials. Utilize platforms like Kaggle and GitHub to follow along with notebooks and repositories. Practical coding experience with languages like Python and libraries like scikit-learn and pandas is crucial for mastering ML concepts.

After mastering the basics and gaining coding experience, it's important to explore advanced topics like neural networks, deep learning, and reinforcement learning. Specialized online courses and interactive tools can help you grasp these complex subjects, allowing you to learn at your own pace and deepen your expertise.

Advance your knowledge by exploring specialized courses on Udacity and DataCamp. Delve into topics like deep learning, NLP, and reinforcement learning using frameworks like TensorFlow and PyTorch. This deep dive helps in understanding complex ML algorithms and their real-world applications.

Engaging with machine learning forums is highly beneficial. Joining a community of learners and experts allows you to discuss ideas, ask questions, and share resources. This collaborative environment offers support, motivation, and access to collective knowledge, helping you overcome challenges and stay updated on the latest ML trends.

Engage with the ML community through forums like Stack Overflow, Reddit’s r/MachineLearning, and LinkedIn groups. Participate in discussions, ask questions, and share your insights. This interaction not only broadens your perspective but also helps in solving specific challenges you might encounter.

Steps to learn Machine Learning : 1. Start from the easiest Machine Learning projects like building models with Scikit-learn 2. Now, you need to understand some importants parameters like : (random state : used to make the machine learning modelling result same when you want to run the model again, max_depth: to prevent overfitting) these 2 are the most important parameter 3. Learn how to do Hyperparameter tuning (I'd reccomend you to use GridSearchCV because it will try every possible inputted parameter and will give the best parameter combination) 4. Learn Deep Learning with Tensorflow or Pytorch (I'd reccomend you to learn Tensorflow before learning PyTorch

There are many platforms where projects ides , datasets are availabe. Anyone who know some concepts of machine learning can try those projects. While building projects you learn a lot.

While theory is important, practical experience is crucial for solidifying your learning. Start with small projects that interest you, such as recommendation systems or image recognition tools. Building projects from scratch will challenge you to apply your knowledge and develop essential problem-solving skills for a successful career in machine learning.

Apply your skills by working on projects. Use platforms like GitHub to host your projects and participate in competitions on Kaggle. Building end-to-end projects, such as sentiment analysis or predictive modeling, showcases your practical abilities and enhances your portfolio.

Having a thirst and curiosity is one of the essential skills to thrive in this field, especially in the beginner stage. Learning is always continuous and curiosity drives learning and skill development. Always be curious and open-minded to new technologies and tools. Experiment and get your hands dirty on various tools and software. I feel this is the best way to learn them, try using different buttons and interactions to figure out how they work. The level of exposure and learning you get out of this cannot be given by any instructor or online material!

Keep your curiosity alive by exploring the latest research papers on arXiv and following ML blogs and podcasts. Stay updated with new trends, techniques, and breakthroughs in the field. Continuous learning ensures you stay ahead in this rapidly evolving domain.

Focus on developing a strong understanding of data ethics and the implications of ML models. Use resources like the Data Science Ethics course on DataCamp to learn about fairness, accountability, and transparency in AI. This knowledge is essential for responsible and impactful machine learning practice.

Online resources are great and mostly free of cost, but the downside is that there wont be proper monitoring and progress evaluation, which is the best advantage of offline learning. However I would say that one can periodically evaluate themself to identity where they stand in the field. Always stay hungry to learn new concepts and dont forget to take a break or else you will lead to a burnout which would lead to negativity. It is a tough journey, but if you stick and hang on, you can shine as a great data scientist or machine learning engineer!

Consider joining a competition on Kaggle or likewise. There are many approachable competitions on various online websites where you can start to 'run your hands through' machine learning and establish a higher baseline of technique the more problems you approach. Competitions will also give you a good opportunity to network and build teams, which would help you advance your knowledge and career.