You want to become a data scientist. How can you develop your machine learning skills?

Start with a project that interests you, even if it's simple, and apply what you learn as you go. This approach will keep your learning fun and motivating, gradually building your skills. Choose structured learning paths from sources that offer certifications or other rewards to stay engaged. A great platform to try, known for its data science competitions and learning resources, is Kaggle. Prioritize courses that consistently build upon one another. Join online machine learning communities to connect with others. As you progress, aim to become a Kaggle Grandmaster, AWS ML Hero, Google's GDE, Microsoft's MVP, or a Deep Learning.AI & NVIDIA ambassador. :)

Start with solid foundations in statistics and programming. Dive into machine learning theory and then apply it through projects using datasets that challenge you. Utilize platforms like Kaggle for hands-on experience. Read research papers to stay on the cutting edge. Seek feedback, iterate on projects, and never stop learning. Engage with the data science community for insights and networking.

Laying a strong foundation is the key to success in any field. Before delving into coding and ML algorithms, understand the big picture of Machine Learning and applications. -Start by exploring introductory courses on platforms like Coursera, edX etc to gauge your interest. -Strengthen your understanding of probability and statistics. I personally started with a course on Applied Statistics before even starting with a course on Machine learning to check if I really find math behind the machine learning interesting -Pickup a dataset that interests you from kaggle and start with some basic EDA using Python -Build basic models, try to deploy it on cloud and create an inference API. May be enhance it with a simple front end UI using streamlit.

I would consider the following topics if you are to understand the core of machine learning: - Understand linear algebra well, methods like singular value decomposition, component analysis. - Understand optimization ; Start with convex optimization and then understand gradient descent based optimizations. - Start building a Multi layer perceptron from scratch. Doing this will clear all the basics of machine learning from transforming vector inputs to backpropagation. If your understanding of MLP is clear, extending that knowledge to any deep neural network models like CNN, RNN, Transformers should be easy.

- Study mathematics, especially linear algebra, calculus, and statistics. - Gain a solid understanding of programming languages like Python or R. - Familiarize yourself with data structures, algorithms, and databases.

To develop machine learning skills as a data scientist, engage in real world projects that apply theoretical knowledge to solve problems, build a portfolio, and foster problem solving and critical thinking.

Learn the basic building blocks of ML: math and statistics (linear algebra, calculus, probability, and statistics). Understand what kinds of problems can be solved by ML and the core concepts of ML through a plethora of online resources. Be proficient in SQL and Python. Use online platforms like Kaggle and Colab to gain hands-on experience. Choose a business domain and specific problem statement, and try to build a solution based on your learnings. Look at solutions built by other developers or data scientists, and find the latest research papers in that area to see the latest or most advanced models used. Try to improve your model based on these insights. Learn frameworks like Flask to create APIs.

- Apply your knowledge to real-world datasets and problems. - Participate in online competitions like Kaggle to practice and showcase your skills. - Build a portfolio of projects demonstrating your ability to solve various machine learning tasks.

By engaging in diverse projects, individuals can gain exposure to varied datasets, challenges, and problem domains, allowing them to adapt their skills and techniques to different contexts. Moreover, working on different projects enables individuals to explore a variety of machine learning algorithms, techniques, and methodologies. Additionally, engaging in collaborative projects provides opportunities for learning from peers & mentors, accelerating professional growth and development. Individuals who practice on different projects are better equipped to tackle complex machine learning tasks & make meaningful contributions in their careers as data scientists.

The best and easiest way to wrap your head around ML is wading into the deep end and getting your hands dirty. Try your hand at some of the numerous well-studied problems, e.g. Kaggle challenges, and see what you can do with it. Most common ML methods are readily implemented through any of the numerous libraries, so rapidly experimenting is quite easy. In this process, keep an eye on building up an intuition of why you are seeing the results that you are. This intuition serves as the basis for more advanced ML techniques, i.e., figuring out why they work or don't.

One important reason why using frameworks & libraries is essential for developing machine learning skills lies in their ability to abstract away complex algorithms & streamline the development process. Frameworks such as TensorFlow, PyTorch & scikit-learn provide high-level APIs and pre-implemented algorithms for tasks such as classification, regression, clustering & dimensionality reduction. For example, instead of manually coding the backpropagation algorithm for training neural networks, data scientists can use frameworks like TensorFlow or PyTorch, which provide built-in functions for defining neural network architectures, specifying loss functions, and optimizing model parameters.

To learn machine learning using frameworks and libraries, begin by familiarizing yourself with popular tools such as TensorFlow, PyTorch, or scikit-learn. Utilize online tutorials, documentation, and educational resources such as Kaggle or provided by these frameworks to grasp their functionalities . Engage in hands-on projects to apply these libraries to real-world datasets, and participate in forums or communities to seek guidance and share knowledge with fellow learners.

utilize frameworks and libraries such as TensorFlow and PyTorch for deep learning, Scikit-Learn for traditional machine learning tasks, and Keras for a user-friendly entry into deep learning. These tools offer comprehensive sets of tools for creating and deploying ML models, enhancing your ability to tackle complex data science problems effectively.

Using machine learning frameworks and libraries is like having a toolkit for building something. You don't need to create each tool from scratch; you use what's already there, making your work quicker and easier. These tools help you handle complex tasks without getting bogged down in details. For instance, Scikit-learn is great for basic machine learning tasks, TensorFlow for complex neural networks, Pandas for data organization and analysis, NumPy for numerical computing, especially with arrays and matrices, and SciPy for advanced scientific computing. Each library has its specialty, helping you become a skilled data scientist more efficiently.

To enhance machine learning skills for data science, embrace frameworks like TensorFlow or PyTorch for deep learning models. Libraries such as scikit-learn provide tools for traditional machine learning algorithms. Practice using these tools to manipulate data, train models, and evaluate performance. Engage in projects and competitions to apply these skills in real-world scenarios. Continuous learning and experimentation are essential for mastering machine learning techniques.

- Machine learning is a rapidly evolving field, so it's essential to keep learning and staying updated with the latest techniques and advancements. - Follow blogs, podcasts, or newsletters related to machine learning to stay informed about new developments.

Staying updated with the latest trends in data science is like keeping your toolkit sharp. It helps you use the newest and coolest tools, making your work better and faster. You also stay competitive in job hunts because employers like people who know the latest stuff. Plus, it's great for solving problems more cleverly and connecting with other smart people in data science. So, keeping up with trends is a smart move to stay on top of your game in data science.

There is a wealth of resources that people can learn from. There are newsletters, blogs, YouTube videos, papers, datasets and much more that are updated on a nearly-daily basis. HuggingFace datasets/models, ML Researchers on X (formerly Twitter) and some specific blogs like those from Sebastian Raschka, Deeplearning.ai, etc are great places to stay up to date with the every new trend that captures the attention.

To keep up with trends in machine learning, regularly engage with reputable sources such as academic journals, conference proceedings, and online platforms like arXiv and Medium. Follow influential researchers, practitioners, and organizations on social media platforms to stay updated on recent developments and discussions. Participate in workshops, webinars, and conferences focused on machine learning to network with experts and gain insights into emerging trends and techniques.

In the fast-paced world of AI, staying current is vital. Despite a recent Data Science master's degree, I found the curriculum lacking, especially in advanced algorithms. This led me to learn LightGBM, an advanced algorithm not covered in my coursework. This underscores the importance of self-education where formal education may fall short. Keeping up with industry leaders, analyzing new trends, and participating in webinars and conferences are key for a data scientist to stay innovative. These efforts enhance understanding of advanced algorithms and promote continuous learning. You need to be pro-active to keep up with trends, it won't happen on its own.

To seek guidance in learning machine learning, consider joining online communities and forums such as Stack Overflow, Reddit's MachineLearning, or LinkedIn groups dedicated to the topic. Additionally, reach out to experienced professionals, mentors, or instructors through networking platforms, conferences, or educational institutions for personalized advice and support. Participating in study groups, bootcamps, or mentorship programs can also provide structured guidance and accountability in your learning journey.

Solicit feedback from experienced data scientists on your projects and code. Engage with online communities and forums to receive guidance and advice. Actively seek mentorship from professionals in the field. Continuously iterate on your skills based on constructive criticism. Stay updated with the latest advancements in machine learning through courses, workshops, and reading research papers.

Choosing the right platform for machine learning feedback involves considering factors like individual learning preferences and feedback needs. Online communities offer diverse expertise but may lack consistency. Forums provide targeted feedback, while groups foster belonging and support. Newcomers can start with Reddit, technical challenges can be addressed on forums, and comprehensive feedback may be found in specialized groups. Observing community tone, being specific in queries, and engaging reciprocally are key for a successful experience.

Ask questions, plenty useful questions. Be open to learn something new even if it's not trending, you just never know. When researching on new projects to work on, read on existing research done in that area. It would help you make meaningful contributions.

Machine learning is a field that's constantly evolving, so continuous learning is key. Beyond the technical skills, it's important to develop a strong foundational understanding of statistical principles and data ethics. Engaging with the ML community through forums, attending conferences, and contributing to open-source projects can provide invaluable experience and networking opportunities. Remember, real-world problems are often messy and data is imperfect; learning to deal with these challenges is a crucial part of becoming a proficient data scientist.

Valuable advice for aspiring ML engineers: Establish technical excellence, develop business acumen, cultivate proactive growth, enhance networking, and visibility for career advancement. Consistent growth and value delivery are key to success in the ML field.