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:
- 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.
- 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.
- 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).
- Applying Machine Learning Algorithms and Libraries: The standard implementation of algorithms of Machine Learning are widely available via APIs, packages, libraries but applying them properly would need picking a suitable model (like neural net, decision tree, ensemble of multiple models, nearest neighbor, support vector machine), a learning procedure so as to fit data ( that can be gradient descent, linear regression, bagging etc), as well as understanding how hyper-parameters can affect learning. Knowing pros and cons of the different approaches is also important. Machine Learning and Data Science challenges can be taken up for practice after sufficient training.
- System Design and Software Engineering: Machine Learning engineer’s deliverable and typical output is always software. Therefore it can be just a small component that will fit into a bigger ecosystem of services and products. One will need to understand how these pieces work in coordination, communicate with them with use of APIs, library calls, database queries etc and be able to build appropriate interfaces for sake of the component that others will depend on. Carrying out a careful system design is always necessary in order to avoid bottlenecks and allow a careful scaling of algorithm with increase of data volume.
Machine Learning Jobs are growing at a fast rate as companies want to make the most of emerging technologies. This makes Machine Learning a viable career option. But make sure you are prepared enough – with all pre requisite training needs met. IIHT’s AI/ML stack gives you the most thorough training with a unique opportunity to learn both R and Python approach to Machine Learning. For more details check: aimlstack.iiht.com