In this post you will find everything you need to know in order to become a Machine Learning Engineer using Python.
What is machine learning?
Apart from its fancy name, machine learning is nothing more than giving a computer the ability to “think” for itself with no human interference, only based on example data or previous experiences . Think about how difficult it would be to program by hand a spam filtering, a face recognition system, or even a translator.
So the general idea is to collect the most possible amount of data, in order to build an intelligent system and make it able to learn and adapt for changes. That is why the field of machine learning is highly connected to the concept of Artificial Intelligence.
What does a machine learning engineer do?
The job of a machine learning engineer is to design self-running softwares to automate predictive models. For example, when you watch a movie that was recommended to you on a streaming platform, such as Netflix, someone had to design the algorithm behind that to perform this operation. Later, the machine uses those results to carry out future operations with a greater level of accuracy.
How do I start in machine learning with Python?
Python is probably the most famous programming language when it comes to machine learning and artificial intelligence. Therefore, if you want to build models using the powerful tools this language provides, you must understand a few things before starting. Take your notebook and write them down!
Basic Skills to become a ML Engineer
1. Data Structures
When working with large data sets to train the computer, you need to know how to organize them. The most used data structures in machine learning are: arrays (maybe the most important and used), linked lists, binary trees (or decision trees), heap, stacks, queues and sets. Of course you don’t need to master all of them, but having a general idea of their operation is essential to any machine learning engineer.
2. Linear Algebra
Many people are not fan of mathematics, even when it comes to programming. However linear algebra is highly related to the field of machine learning. Subjects like matrices (multiplication, addition, inverse, transpose) and vectors are very used and have an important role in ML.
3. Libraries and Frameworks
Many libraries and frameworks are used to simplify tasks in machine learning (and any other field in programming). The most used libraries in Python for machine learning are: Numpy, Scipy, Pandas, TensorFlow, PyTorch, Scikit-learn and Matplotlib. All of these libraries have different purposes, such as array-processing, neural networks and data analysis.
Have in mind that chasing the path to become a machine learning engineer is definitely not easy. This is a new field in technology and it is growing each day, with many more people interested in working with Artificial Intelligence. The process of learning these concepts could be time-consuming, but it surely brings great results.