You’re struggling to learn Machine Learning. What can you do to make it easier?

Understand the Basics of Mathematics and Statistics: Ensure a solid foundation in basic mathematics (linear algebra, calculus) and statistics. Many machine learning concepts rely on these principles. Start with Fundamentals: Begin with the fundamentals of machine learning. Understand concepts like supervised learning, unsupervised learning, and reinforcement learning before diving into more advanced topics.

Consider a scenario where you want to develop a recommendation system for an e-commerce platform. Before delving into complex algorithms, it's crucial to grasp the basics. Understanding ML helps you recognize that recommendation systems often fall under the category of supervised learning, where the algorithm learns from historical user preferences. Knowing the basics allows you to appreciate the importance of features like user behavior, and feedback in crafting an effective recommendation algorithm. Strengthening your math skills, particularly in statistics, aids in interpreting user data and optimizing the model's performance. By starting with the basics, you lay the groundwork for designing a robust recommendation system

Change your approach: Visualize: Use diagrams, animations, and real-world examples to grasp complex concepts. Break it down: Divide large problems into smaller, manageable steps. Focus on intuition: Before diving into code, understand the "why" behind the algorithms.

Struggling with ML? No problem, let's simplify: Begin with easy ML tasks. Don't rush into complexity; build a foundation for your understanding. Dive into practical scenarios. Understand how things work in real-world applications. Watch project implementations, then research them. Connect theory to real-world use. Focus on Python, especially numpy and pandas. Grasp basics of math (algebra, calculus, stats). Find insightful notebooks on Kaggle. Learn from quality content; it's a valuable resource. Take Andrew Ng's ML specialization on Coursera. It provides a solid theoretical foundation. "Hands-On ML with Scikit-Learn, Keras, TensorFlow" is excellent; it explains concepts practically.

I'm gonna give my opinion based of my experience learning data science / ML. I took a practical approach and didn't focus too much on the math in the beginning. Not saying it isn't important, but if you don't have a strong mathematical background this can deter your or demotivate you. First be decent in python , and learn the fundamentals of numpy, pandas and scikit learn. As a data scientist or ml practitioner 80-90% of your work will be "trying to understand the data" (visualization, cleaning etc) make sure to have a solid foundation in that, before going to feature selection, feature engineering or model building. Also read articles and try to study from various sources. Read other peoples code on Kaggle or Github for example.

Imagine you are working on a machine learning project for a healthcare company, tasked with developing a predictive model to identify potential health risks in patient data. In the initial stages, you decide to use Python and scikit-learn as your primary tools. Python's readability and extensive libraries, coupled with scikit-learn's simplicity for implementing various algorithms, make it a pragmatic choice. Juggling multiple tools would have steepened the learning curve and diverted attention from core objectives. Committing to one tool allowed for mastery, project success, and later exploration of additional tools. This underscores the value of a focused approach for effective machine learning projects.

Choosing a tool and sticking with it is a practical strategy for mastering machine learning. Select a programming language, framework, and set of tools that align with your learning goals and the industry's current standards. Python is a popular and versatile language in the machine learning community, and frameworks like scikit-learn, TensorFlow, and PyTorch are widely used. Staying consistent with a specific toolset allows you to build expertise and deep understanding, as you become familiar with the intricacies of the chosen language and libraries. It also facilitates a more seamless transition between different machine learning projects, fostering efficiency and productivity.

Regarding programming language I fully focused on python, I'm not picking the new hot programming language in ML. Python is tried and tested and I focused on that. After that the usual libraries like pandas, numpy and scikit learn. only when I became decent in classical ML (3 months in) then I went over to deep learning and focused on Tensorflow-Keras. I didn't dive into pytorch. I made sure that I understood and knew how to work with these tools, before deciding to learn a new tool like Pytorch. So my short advice pick your tools, the tried and tested of course, and only once you become good in them or maybe a new project or job requires it switch to learning a new tool.

The most standard Framework used for ML based projects is Python based Frameworks . Programming Language to know at basic level : Python for python based Frameworks . Advantage of using Python based Frameworks : 📌Open Source 📌Libraries and Packages 📌Saves time for Coding whic paves way for more experimentation . Another Tool is MATLAB The toolbox in MATLAB are really useful for ML projects , edge computing with ML based projects can be easily simulated in MATLAB . The one disadvantage of MATLAB is that it is not open source , MATLAB online is open source with limited functionalities .

In my journey into machine learning, I found inspiration in the "learn by doing" philosophy. Choosing Kaggle, a renowned platform for machine learning projects, I embarked on a beginner-friendly competition centered around predicting housing prices. Working with real-world data, I delved into preprocessing techniques, tackled missing values, and identified crucial features for the task. Through iterative experimentation with various regression algorithms, I refined my models based on performance feedback, uncovering the nuances of hyperparameter tuning. Actively engaging in the competition not only sharpened my technical skills but also deepened my understanding of how machine learning algorithms operate in practical scenarios.

By engaging with these platforms, competitions, and repositories, I not only gained practical experience but also immersed myself in a vibrant community of learners and experts. The exposure to different problems and approaches will nurture growth in this dynamic field. The key lies in consistent practice and exploration. Embrace the challenges and learn from each attempt.

When I was learning traditional software engineering (FE) I focused to much on doing courses after courses. Without implementing, although this taught me react, css etc. I only became good enough once I worked on a real world project. I'm not saying courses aren't useful they are. But once you finished the course, implement immediately what you've learned. When learning Data Science / ML I did precisely this, and I grew way faster. I did a course, implemented it right away and learned much faster. Because I was starting to write my own code and started to develop my own intuition.

It is highly probable that you would fall into the loop of course after course while stepping into this field, because we feel overwhelmed with the amount of information we need to grasp. But what helped me in my initial days was taking a real world dataset and applying what I just learned. That gave a lot more perspective and understanding of the concepts and why and where we need to apply; and then innovate with our own ideas. This is the advice I give to most people starting out in this field

Hands-on Practice is one of the best methods to improve your skills. Some of the ways I would approach to learn ML would be as follows: 1. Start with more straightforward problems and understand the implementation of the basic rules. One of the first problems anyone solves is the iris dataset or the Housing Data. The iris dataset gives you a basic understanding of classification problems, whereas the Housing data set provides an understanding of regression problems. 2. Move to complex problems using websites like Kaggle. Once you get the hang of the basics using the more straightforward datasets, try to solve more practical problems on Kaggle. 3. Apply different ML Algorithms to see which model performs better in the given dataset.

Whenever I completed a data science project, I exported the notebook to a pdf file and posted it in AI / Data Science community groups and asked for feedback. I knew in these groups there were professionals with years more experience than me and some of them love to give feedback. Sometimes I explicitly asked for feedback and I found people are more than willing to give their feedback. Look at it from their perspective, here is an opportunity to showcase my expertise.

I would advise to put in a minimal of 1 hour a day focused and dedicated to learning machine learning. It is a broad field and innovation is happening at lightning speed. If you wanna increase your market value, learning new skills in data science or machine learning is essential. Try to get some professional certificates from reputable institutions, although gaining the knowledge is more important, having certifications on your LinkedIn showcases to people that you are dedicated to learning, which is a must if you wanna stay relevant as Data Scientist or ML engineer.

Consistency is key to learning machine learning. I tried to cram ML concepts initially by pulling all-nighters and crashed. As my colleague suggested, dedicating just 1-2 focused hours daily proved far more effective long-term. I set aside time to methodically strengthen my Python, algebra, and statistics abilities. With this foundation solidified, I was able to better grasp core techniques like regression, classification, neural networks. Whenever hitting roadblocks on Kaggle competitions or work projects, I returned to fundamentals, reviewed, and iterated.Consistent effort in revising foundations and applying knowledge to real problems, the path became clearer and very rewarding.

What helps me the most in understanding machine learning is through the mistakes and differences in approaches. Actually sitting down and analysing what does not work is important for your growth. A person might use a different approach than you, think critically on why they use that specific approach and how you can use it. I agree with the last advice. Be persistently curious.

Master math basics and Python. Take online courses and practice coding with real datasets. Visualize results to grasp concepts. Join ML communities for support. Focus on understanding, not just implementation. Work on meaningful projects, applying ML to your interests. Be patient and consistent. Use textbooks and videos. Start simple, progress gradually. Seek feedback and visualize to improve. Remember, ML is complex – take it step-by-step. Begin with core concepts before advancing. Break down topics, review regularly, and try teaching others. With persistence and practice, you'll gain proficiency in this challenging field.

To effectively learn ML,start with fundamental concepts and mathematics.Choose a primary coding tool like Python or R and stick with it.Apply ML to real-world problems to enhance understanding.Join online ML communities for feedback and mentorship,and keep abreast of the latest trends and research.Additionally,break down complex theories,use visual aids,engage in small projects, and participate in study groups.Balance theory with practical application,choose quality online resources carefully,and regularly reflect on your learning progress. Set realistic goals,explore different ML applications, strengthen your programming skills, and understand the ethical implications of ML.This multifaceted approach aids in mastering ML more effectively.

In my experience, especially recently when attempting to further educate myself in Gen AI but having jumped off straight from Neural Networks, it is important to learn and understand the concepts behind your code. You don’t have to deeply understand them, as they are very complex, but at the very least you should know how they work at a surface level so you can easily comprehend your code and outputs. A great resource that I use is StatQuest on YouTube. They provide extremely easy to understand videos. With that being said, this is my current resource to study-up on transformers and word embeddings so I can understand the Gen AI models I am experimenting with. In a world of copy-pasted & AI generated code, this is essential.

Starting with the fundamentals before diving into tools has been hugely beneficial. I spent time studying basic ML concepts, linear algebra, and statistics first. Once I developed an intuition, I chose Python and focused deeply on libraries like NumPy, Pandas, Scikit-Learn, and TensorFlow. Rather than jumping between tools, I stuck with Python long enough to really understand it. This patience and focus made picking up other tools much easier later on. Though tempted by shiny new libraries, restraint and mastery of one catalyzed my ML learning.

Try going into to the depth of the math as much as possible. Instead of using Tensorflow try making a neural network using just numpy. It would really engrave the concepts within you