There are many people who are interested in machine learning these days. One thing is very clear that machine learning has arrived. Nobody is alone, there are multiple candidates which are into this field.
Before entering into the machine learning field there are multiple things you need to keep in mind and here are some of them.
1. Computer Science Fundamentals and Programming
There are some important key points of computer science for every machine learning engineer which includes the data structures, algorithms, computability and complexity and the last one the architecture of a computer.
2. Probability and Statistics
A formal characterization of probability (conditional probability, Bayes rule, likelihood, independence, etc.) and techniques derived from it (Bayes Nets, Markov Decision Processes, Hidden Markov Models, etc.) are at the heart of many Machine Learning algorithms; these are a means to deal with uncertainty in the real world. Closely related to this are the field of statistics, which provides various measures (mean, median, variance, etc.), distributions (uniform, normal, binomial, Poisson, etc.) and analysis methods that are necessary for building and validating models from observed data. Many Machine Learning algorithms are essentially extensions of statistical modeling procedures.
3. Data Modeling and Evaluation
Data modeling is the process of estimating the underlying structure of a given dataset, with the goal of finding useful patterns (correlations, clusters, eigenvectors, etc.) and/or predicting properties of previously unseen instances (classification, regression, anomaly detection, etc.). A key part of this estimation process is continually evaluating how good a given model is. Depending on the task at hand, you will need to choose an appropriate accuracy/error measure (e.g. log loss for classification, a sum of squared errors for regression, etc.) and an evaluation strategy (training-testing split, sequential vs. randomized cross-validation, etc.). Iterative learning algorithms often directly utilize resulting errors to tweak the model, these measures need to be understood even for just applying standard algorithms.
4. Applying Machine Learning Algorithms and Libraries
Standard implementations of Machine Learning algorithms are widely available through libraries/packages/APIs (e.g. sci-kit learn, Theano, Spark MLlib, H2O, TensorFlow, etc.), but applying them effectively involves choosing a suitable model (decision tree, nearest neighbor, neural net, support vector machine, ensemble of multiple models, etc.), procedure of learning for fitting the data (linear regression, gradient descent, genetic algorithms, bagging, boosting, and other model-specific methods), as well as understanding how hyperparameters affect learning.
5. Software Engineering and System Design
So if we see that at the last machine learning engineer outputs the software only. And machine learning is a component which will fit into a large ecosystem of some products and service. You just need to get the understanding, working of the different pieces communicate with them (using library calls, REST APIs, database queries, etc.) and build appropriate interfaces for your component that others will depend on. Careful system design may be necessary to avoid bottlenecks and let your algorithms scale well with increasing volumes of data.