You want to understand machine learning. What's the best way to start?

Learning ML can seem daunting at first, given its complexity and the breadth of its applications. However, breaking down the learning process into the following steps can make it much easier. 1. Building a strong foundation in Mathematics and Programming (preferably Python) 2. Understanding ML Fundamentals using online courses like Coursera's "Machine Learning" by Andrew Ng. 3. Engaging in hands-on practice, starting with simple projects and gradually increasing complexity using platforms like Kaggle and GitHub. 4. Dig deeper into specialized ML areas like deep learning and NLP. 5. Join a community of ML enthusiasts and professionals on sites like LinkedIn for inspiration and knowledge sharing. 6. Stay updated and keep learning.

First start with the basic concepts of Machine Learning and Understand the difference between training data, validation data, and test data. Explore common machine learning algorithms like how each algorithm works, its strengths, and its limitations. Gain practical experience on that. Stay Updated with the new trend and do Continuous learning.

Understanding basics related to terminology/algorithm like Supervised learning, Unsupervised learning, Regression model, K-means , cluster techniques , can be a good starting point assuming one has a good grasp around python programming .💻

Before delving into the nitty-gritty, grasp machine learning basics. Understand terms like supervised/unsupervised learning, classification/regression. Explore real-world applications and challenges. Measure model performance. Plenty of online resources break down these concepts in straightforward ways, from books to blogs.

To grasp machine learning, start with foundational skills in math, statistics, and programming. Learn Python or R and explore online courses for basic algorithm understanding. Practice with small projects, engage with open-source libraries like scikit-learn, and stay updated through online communities. Continuous learning and practical application will deepen your understanding of machine learning concepts.

First, learn the basics of Python: different data types, the methods for each data structure, how functions work (user-defined functions, lambda functions), looping (for, while), and conditional statements. Try to understand how each concept works in Python. There is no need to remember the syntax at first; you will remember it as you code and solve problems. It is okay if you get stuck initially. You need to have a problem to solve. Try to apply your Python knowledge first. If there is an error, Google it, use large language models wisely, and don't just copy-paste code. Instead, write it on your own and understand it. Happy coding :)

1️⃣Start with Basics: Begin by understanding the fundamental concepts of machine learning, such as what it is, how it works,& its applications in real-world scenarios. 2️⃣Learn Mathematics: Brush up on your mathematics skills, particularly in areas like linear algebra, calculus,& probability theory. 3️⃣Pick a Programming Language: Choose a programming language commonly used in machine learning, such as Python or R. 4️⃣Online Courses &Tutorials: Explore beginner-friendly online courses &tutorials available on platforms like Coursera, Udemy, or Khan Academy. 5️⃣Practice, Practice, Practice: Put theory into practice by working on small projects or exercises. Start with small-scale projects & gradually scale up as you become more proficient.

There are many languages to do ML but: - Python with Jupyter Notebook is the best thing to start ML Learn basics to intermediate python and you are ready.

- Choose a programming language suited for machine learning tasks. - Popular choices include Python and R due to their extensive libraries. - Other options like Java, C , or MATLAB may also be suitable. - Learn the basic syntax and features of your chosen language. - Familiarize yourself with an integrated development environment (IDE) or notebook tool.

For a Data scientist or ML Engineer , programming language is a medium to communicate. so one should have good understanding of atleast one language, it could be python or R or any other for that matter. Just learn basic libraries first and later can venture in to Tensorflow, keras or pytorch.Best way to learn language is by programming , you may face lot of challenges once you start programming and overcoming one by one challenges one will learn more about the language. Its also advisable to learn how to code as per standards as well as in an optimized way . In this current era most of the working ML models are in cloud , so it good to have cloud knowledge along with language knowledge

Statistics is the core of many parametric machine learning models like linear and logistic regression. Understanding probability and statistical hypotheses tests (t test, ANOVA, and AB testing) can help interpret these models and check for model assumptions. There is also a machine learning model called Naive Bayes that is based on the Bayes Theorem (stats concept). Highly recommend learning probability and Bayes Theorem.

Learn about the core concepts of ML, including supervised learning, unsupervised learning, reinforcement learning, feature engineering, model evaluation, and bias-variance tradeoff. Start with simple projects to apply theoretical knowledge. Kaggle and GitHub offer datasets and project ideas.

After learning the basics, next one can start exploring tools .He/She can explore tools or packages like Scikit learn ,Keras , Tensor flow and pytorch which are widely used for model development and deployment .Best source for it to go through the documentation which is available.Also hands on practice of a project using these packages helps you to learn quickly. One should atleast be clear about how to use these tools for EDA , feature engineering, model development , hyper parameter tuning , model validation and deployment .Its also good to learn fundamentals of cloud or atleast the ML functionalities of the cloud .

To truly grasp machine learning, get hands-on. Dive into projects where you apply your knowledge to real data and problems. Explore available datasets or use your own. Follow the machine learning workflow steps: define the problem, collect and clean data, analyze it, build and train your model, evaluate and refine it, and communicate results. Document your code and share your findings through a report or blog post.

Practice makes perfect: The best way to solidify your understanding is by working on hands-on projects. Kaggle is a great platform to find datasets and participate in machine learning competitions, giving you practical experience. Start small and gradually increase complexity: Begin with beginner-friendly projects and gradually progress to more intricate ones as you gain experience and confidence.

Here's a concise guide to start ML journey: 1. Learn Prerequisites: - Start with Python if new to programming. - Acquire basic knowledge of statistics and data manipulation. 2. Study ML Theory:Grasp supervised/unsupervised learning, reinforcement learning, and ML algorithms. 3. Explore key topics: linear regression, classification, clustering, neural networks and feature engineering 4. Work on Projects:Apply knowledge through hands-on projects aligned with your interests. 5. Learn ML Tools and Algorithms: Use scikit-learn,TensorFlow,PyTorch and other ML algorithms 6. Enroll in online courses on platforms like Coursera and edX. 7. Gain practical exposure through internships with companies or research labs.

Machine learning is a field which demands continous learning .Since its an evolving field , every day new discoveries are happening . One can follow the experts of the field or can read from linkedin , medium or other publications to keep themselves updated.For eg till recent past , one used to heavily rely on transformers for all NLP tasks , where as now it got shifted to LLMs . So learning continously is key in Machine learning field

Even though the ideal roadmap would be to learn Linear Algebra, Calculus, and Statistics before moving on to Machine Learning, I believe it’s important to consider student engagement and/or the particular moment in a person’s life. For instance, I’ve seen many students become more inclined to study the math behind the algorithms after gaining some initial understanding of them. Also, beginners in the field of Data Science are usually not expected to understand all the math behind each model. All that being said, you should consider the individual’s circumstances before choosing his/her roadmap.

Start with a solid foundation in math (linear algebra, calculus), learn a programming language, and understand basic statistics and probability. Take online courses (e.g., Andrew Ng's ML course), read key books, and engage in practical projects on platforms like Kaggle. Explore deep learning basics and gain hands-on experience with TensorFlow and PyTorch. Join ML communities, attend meetups, and stay updated through research papers, blogs, and podcasts. Practical application is key—apply your knowledge to real-world problems and iterate on solutions for a well-rounded understanding of machine learning.

algo que puede funcionar, una vez se haya alcanzado un nivel base, experimentar con conjuntos de datos reales y variados, no limitarse a un solo tipo de conjunto de datos. Y también practicar la resolución de problemas y la depuración de código, te automatiza o te agiliza la detección de errores y sus posibles soluciones.

As a Senior Data Scientist, I recommend beginning with a strong base in statistics and linear algebra to understand machine learning. Learn Python or R, essential for data science, and engage in hands-on online courses and tutorials. Deepen your knowledge by reading key literature and participate in communities like Kaggle for practical experience. Start with simple models and progressively tackle more complex ones. Consistent practice and applying your skills to real-world scenarios are crucial for mastering machine learning.

Starting with stats is better before going to ML. Learning math, key concepts and why to use which model in usecase along with their cons and pros is necessary.