Are you interested in Machine Learning? The present generation is abuzz with interest in Artificial Intelligence and Machine Learning and this interest is only growing. But being just interested is very different from actually beginning to work in the field. This article aims at letting you know what skills you will need to train in, if you seriously want to plan a career in machine learning:
1. Programming and Fundamentals of Computer Science: Fundamental concepts that include data structures like queues, arrays, stacks, graphs are important for Machine Learning engineers. A good understanding of computer architecture that includes deadlocks, memory, bandwidth, cashe, processing etc is a must. An in-depth knowledge of complexity and computability like NP-complete problems, big-O notation, P vs NO, approximate algorithms etc is a must. One must be able to apply, adapt, implement or address programming problems. Make sure you pick a Machine Learning course that also trains you with programming.
3. Probability and Statistics: Machine Learning algorithms use formal characterization of conditional probability, probability, Bayes rule, likelihood independence, etc and other techniques that are derived from them like Markov Decision Process, Bayes Nets, Hidden Markov Models etc. Statistical principles like distributions – Poisson, uniform, binomial, and measures like variance, median, mean etc are a must learn. Analysis methods like hypothesis testing, ANOVA are also helpful. A good number of Machine Learning algorithms are basically extensions of statistical modeling procedures.
3.Evaluation and Data Modeling: Data modeling is a process making an estimation of underlying structure of any dataset with being able to find useful patterns like clusters, eigenvectors, correlations and more as predicting properties of unseen instances (regression, classification, anomaly detection and others). A major part of this estimation process would be continually making evaluation of any given model. Based on the task that needs to be performed, one will need to pick an appropriate error or accuracy measure and a strategy for evaluation (randomized cross-validation, testing split, sequential etc).