Way before Machine Learning existed, programmers had a tedious task to enter the code every time they wanted to enable their computers to perform new tasks. Now with Machine Learning, computers are able to perform intelligent tasks and no explicit coding is needed. This has become possible by training the computer with lots of data. Now, computers can easily identify spam, fraud transactions and more.
In recent times, Machine Learning has created a lot of buzz and more people are curious to learn about it. While software engineering or data science experience is crucial to learn Machine Learning, there are also other skills which you need to master before you make your way into Machine Learning.
Computer Science Fundamentals and Programming
In order to make your debut into Machine Learning, it is important that you are well aware of data structures like stacks, queues, multi-dimensional arrays, trees, graphs, algorithms like searching, sorting, optimization, dynamic programming, computability and complexity and computer architecture like memory, cache, bandwidth, deadlocks, distributed processing, etc.
Nothing can help you better than learning programming languages like Python, R, Java, and C++. While you can choose to excel in one language, it is always advisable to learn a bit or two about other languages too.
Machine Learning Algorithms
You need to be aware of the Machine Learning algorithms like linear regression, logistic regression, decision trees, random forest, clustering, reinforcement learning, and neural networks and should also be able to implement them.
Machine Learning Frameworks
Being familiar with ML frameworks like sci-kit-learn, TensorFlow, Azure, Caffe, Theano, Spark, and Torch is also an important skill to possess.
Probability and Statistics
Since many ML algorithms are essentially extensions of statistical modelling procedures, it will be helpful if you are familiar with the fundamentals of statistics and probability theory, descriptive statistics, Baye’s rule and random variables, probability distributions, sampling, hypothesis testing, regression, and decision analysis.