Skills required to master machine learning
Are you a machine learning enthusiast?
Machine learning/Artificial intelligence is one of the few buzz words in technology and business these days. Since AlphaGo won 4 out of 5 matches against the world’s best Go player and recently, GPT-3 used deep learning to auto-generate human text, it seems like all the science fiction movies will soon longer be just fiction.
But it does not matter how many opportunities are there in machine learning, mastering machine learning is a tough task. Whoever says that you can learn Machine learning in 5 or 10 weeks is probably lying to you for their own benefit.
One of the main reasons for this is that mastering Machine learning involves learning multiple sets of skills. These skills include – Computer programming, Mathematics, Probability & statistics, Problem-solving, Data modeling, and analysis.
Below is the description of these skills in relation to machine learning –
1. Computer programming – If you want to get your hands dirty and apply what you have learned, you should have a basic understanding of at least one programming language like Python or R. This will help you test, train or deploy machine learning models.
2. Basic mathematics – Understanding how different algorithms in machine learning works requires a basic understanding of mathematical concepts like Matrices, Linear algebra, and Calculus. Without understanding these mathematical concepts, you can touch the surface of machine learning but cannot understand them in depth.
3. Probability & Statistics – A lot of the concepts of machine learning is derived from statistical concepts. Various statistical concepts like hypothesis testing, confidence intervals, etc. are widely used in many machine learning models. Also, various probabilistic concepts like Bayes theorem, Markov models form the foundation of many machine learning algorithms.
4. Data Analysis & Modeling – If you want to be a Data Scientist, you must know how to play with Data. Some of the important skills required in terms of data are – Data collection, Data formatting, Data Visualization, and Data analysis to make decisions. Some of these tasks are challenging & interesting and many other tasks are tedious. But you really need to do it to get the correct results.
5. Problem solving – Sometimes even after doing everything right in training, developing, and deploying the machine learning models, we don’t get the desired result out of our machine learning models. Solving these requires complex problem solving & analytical skills. Domain knowledge (fintech, healthcare, etc) is also important in order to solve specific domain problems with machine learning.
If you want to dive into the sea of machine learning, the time is perfect right now in terms of market opportunities. You just need to be aware of what it takes to be a Machine learning professional and start polishing your skills in the above 5 domains.