Machine Learning Engineering
Machine learning is a branch of artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy.Machine learning algorithms use computational methods to “learn” information directly from data without relying on a predetermined equation as a model.
Various skills required for becoming a succesful Machine Learning Engineering are as follows:
1.Applied Mathematics
Maths is quite an important skill in the arsenal of a Machine Learning engineer.You can apply various mathematical formulas in selecting the correct ML algorithm for your data, you can use maths to set parameters, approximate confidence levels. Many of the ML algorithms are applications derived from statistical modeling procedures.Some of the important topics of maths that you need to know include linear algebra, probability, statistics, multivariate calculus, distributions like Poisson, normal, binomial, etc.
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2.Computer Science Fundamentals and Programming
You need to be familiar with different CS concepts like data structures (stack, queue, tree, graph), algorithms (searching, sorting, dynamic and greedy programming), space and time complexity, etc.You should be well versed in different programming languages like Python and R for ML and statistics, Spark and Hadoop for distributed computing, SQL for database management, Apache Kafka for data pre-processing, etc. Python is a very popular programming language especially for Machine Learning and Data Science so it’s great if you are well versed in its libraries like NumPy, Pandas, Matplotlib, Scikit-learn, TensorFlow, etc
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3.Machine Learning Algorithms
Mostly ML algorithms are divided into 3 common types namely, Supervised, Unsupervised, and Reinforcement Machine Learning Algorithms.In detail, some of the common ones include Naïve Bayes Classifier, K Means Clustering, Support Vector Machine, Apriori Algorithm, Linear Regression, Logistic Regression, Decision Trees, Random Forests, etc.
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4.Data Modeling and Evaluation
Data modeling involves understanding the underlying structure of the data and then finding patterns that are not obvious to the naked eye. you also need to evaluate the data using an algorithm that is suitable for the data.For example, the type of machine learning algorithms to use such as regression, classification, clustering, dimension reduction, etc. depends on the data. A classification algorithm well suited to large data and speed may be naive beyes, or a regression algorithm for accuracy might be a random forest. Similarly, a clustering algorithm for categorical variables is k mode while for probability is k means. You need to know all these details about various algorithms to contribute to data modeling and evaluation effectively.
5.Neural Networks
The Neural Networks are modeled after the neurons in the human brain. They have multiple layers that include an input layer that receives data from the outside world which then passes through multiple hidden layers that transform the input into data that is valuable for the output layer. These demonstrate a deep insight into parallel and sequential computations that are used to analyze or learn from the data. There are many different types of neural networks like Feedforward Neural Network, Recurrent Neural Network, Convolutional Neural Network, Modular Neural Network, Radial basis function Neural Network,etc.
6.Natural Language Processing
Natural Language Processing is naturally quite important and a fundamental part of Machine Learning.This is so that machines can understand and interpret the human language to eventually understand human communication in a better way. There are many different libraries that provide the foundation of Natural Language Processing. These libraries have various functions that can be used to make computers understand natural language by breaking the text according to its syntax, extracting the important phrases, removing extraneous words, etc. You can be familiar with some or even one of these libraries like the Natural Language Toolkit which is the most popular platform for creating applications relating to NLP.
7.Communication Skills
And finally, we come to a skill that is a soft skill and may not be considered that important. However, if you are good at communication skills, it can make a world of difference in your career trajectory. That’s because while you understand the data and the insights obtained using machine learning better than anyone else, it is equally important that you can convey these insights to a non-technical team, your shareholders, or clients.The data analysis is less important to a company than the actionable insights that can be obtained from the data.