5 Must-Have Skills to Become a Machine Learning Engineer
Following are the skills that you need to become a machine learning engineer. These skills are described in more detail in the video at the end of this article.
Machine learning is all about making computers perform intelligent tasks without explicitly coding them to do so. This is achieved by training the computer with lots of data.
Machine learning can detect whether a mail is spam, recognize handwritten digits, detect fraud in transactions, and more.
Following are the skills that you need to become a machine learning engineer. These skills are described in more detail in the video at the end of this article.
Math Skills
Probability and Statistics
Machine learning is very closely related to statistics.
You need to know the fundamentals of statistics and probability theory, descriptive statistics, Baye's rule and random variables, probability distributions, sampling, hypothesis testing, regression, and decision analysis.
Linear Algebra
You need to know how to work with matrices and lmpw some basic operations on matrices such as matrix addition, subtraction, scalar and vector multiplication, inverse, transposing, and vector spaces.
Calculus
You need to know the basics of differential and integral calculus.
Programming Skills
A little bit of coding skills is enough, but it's better to have knowledge of data structures, algorithms, and OOPs concept.
Some of the popular programming languages to learn machine learning in are Python, R, Java, and C++.
It's up to you to decide which programming language you want to master, but it's advisable to have a little understanding of other languages and what their advantages and disadvantages are over your preferred one.
Data Engineer Skills
You need to be able to work with large amounts of data (big data) and have knowledge about data preprocessing, SQL and NoSQL, ETL (extract, transform, load), data analysis, and data visualization.
Knowledge of Machine Learning Algorithms
You need to be familiar with popular machine learning algorithms such as linear regression, logistic regression, decision trees, random forest, clustering (i.e. K means, hierarchical), reinforcement learning, and neural networks.
Knowledge of Machine Learning Frameworks
You need to be familiar with popular machine learning frameworks such as scikit-learn, TensorFlow, Azure, Caffe, Theano, Spark, and Torch.
Learn about these concepts more in the video below:
Published at DZone with permission of Vinay R. See the original article here.
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