Skill components of a Machine Learning Engineer - A Syllabus on ML


Machine Learning is like money, sometimes it looks everything but sometimes on converse looks shit. But, it is rather a really just a number which we can depend on to make decisions with the provided numbers.



Math Skills: 

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.


Programming skills: 

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++.

Having the idea of other languages and what their advantages and disadvantages are over your preferred one is always welcome.


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.

Knowledge of Machine Learning Algorithms: 

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.

The Knowledge of Machine Learning Frameworks:

Familiar with popular machine learning frameworks such as sci-kit learn, TensorFlow, Azure, caffe, theano, spark, and torch

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