How do you learn and master Machine Learning frameworks and tools?

To master Machine Learning frameworks and tools, begin by choosing your goal like selecting a star in the night sky. Each framework is a constellation, each tool a celestial body. Define a specific project, the North Star guiding your learning journey. Then, navigate the cosmos of resources—tutorials, courses, and documentation—constantly adjusting your course as you encounter challenges. Embrace the principle of "learn by doing," applying your knowledge to real-world problems. Collaborate with fellow stargazers, share your findings, and peer through different telescopes to gain diverse perspectives. Mastery isn't about knowing every star; it's about understanding the universe within your chosen constellation.

In connecting ML to business value, the first step should always be to determine if this is a problem that actually requires ML. Is there a simpler solution that will drive the same amount of value? Or drive value quicker? What will you or the business do with the outputs of your models? Is it realistic that you can drive value in the real world? Are there any stakeholders you need to get buy in or resources from? It’s perfectly fine to move on to a more appropriate problem and apply your skills and time to where it will drive more value. And if it’s purely for learning, we don’t need to worry about value. By checking if we need ML, we can avoid conflict and disappointment later down the line where value doesn’t match expectations.

A sensible, roadmap could be one that starts from the simplest techniques and builds on what you’ve learned previously. Here’s one that would give you a good foundation. 1. Linear regression - estimating using a single line 2. Logistic regression - using a combination of variables 3. Decision trees - using rules to split the data 4. Ensemble methods - such as Random Forest, combining multiple methods 5. Boosted methods - creating a strong model from lots of weak models that build on each others mistakes. 6. Neural nets and deep learning - continually updating weights on complex network of inputs, called nodes. Said to be inspired on the human brain. You could then move onto advanced techniques and how to apply these to different problems.

Keep your end goal in mind. Where will you deploy it? What type of device/hardware will your model run on? For example: 1. Deployment on a mobile device (ARM based processors) with on-device computation: Tensorflow is the best option as you can optimise your model with TFLite and deploy with flutter on both Android & iOS with a single code base. 2. Deployment on edge devices like Cars, Drone, etc. with Nvidia jetson or similar: Pytorch is better as you can optimise your deployment with TensorRT for Real-time computation. 3. Deployment on Server side: depending on your end goal, you can go with either Tensorflow (deployment with TensorFlowServe/TensorRT Optimisation) or PyTorch (TorchServe, TensorRT Optimisation).

Certainly, when it comes to learning machine learning tools and frameworks, practical experience through projects is invaluable. It's not just about understanding the theory; it's about putting it into action. By working on projects, you gain a deeper insight into how algorithms function in real-world scenarios. The CRISP-DM framework provides a structured approach, helping you plan, execute, and evaluate your projects systematically. Additionally, comparing your solutions with others on platforms like Kaggle, HackerEarth, DataHack, and MachineHack provides valuable exposure to diverse techniques and best practices. This hands-on experience is the key to mastering machine learning and its practical application.

There's so much value in pursuing real-life projects. I would start with resources such as Kaggle and build a portfolio using the datasets available. It doesn't hurt to look at what others have done and find ways to improve on that.