Let's check out what are the 5 must-have skills to become a machine learning engineer.
First, let's understand what machine learning is.
Machine learning is all about making computers to perform intelligent tasks without explicitly coding. This is achieved by training the computer with lots and lots of data.
For example: Detecting whether a mail is a spam or not, recognizing handwritten digits, Fraud detection in Transactions and many such applications.
Now let's see what are the top 5 skills to get a machine learning job.
1). Math Skills: Under math skills, we need to know probability and statistics, linear algebra
Probability and Statistics: Machine learning is very much 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 with matrices and some basic operations on matrices such as matrix addition, subtraction, scalar, and vector multiplication, inverse, transpose and vector spaces.
Calculus: In calculus, you need to know the basics of differential and integral calculus.
2). Programming skills: A few coding skills is enough. But it's preferred to have the knowledge of data structures, algorithms and Object Oriented Programming (or OOPs) concepts.
Some of the popular programming languages to learn for machine learning is Python, R, Java, and C++.
It's your preference to master any one programming language. But it's advisable to have a little understanding of other languages and what their advantages and disadvantages are over your preferred one.
3). Data engineer skills: Ability to work with large amounts of data (or big data), Data preprocessing,
the knowledge of SQL and NoSQL, ETL (or Extract Transform and Load) operations,
data analysis and visualization skills.
4). Knowledge of Machine Learning Algorithms: you should be familiar with popular machine learning
algorithms such as linear regression, logistic regression, decision trees, random forest, clustering (like K means, hierarchical), reinforcement learning and neural networks.
5).The Knowledge of Machine Learning Frameworks:
You Should be Familiar with popular machine learning frameworks such as sci-kit learn, TensorFlow, Azure, Caffe, Theano, spark, and torch.