Which libraries do you really need to get started with Machine Learning and why?

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First and foremost, make sure that you have nailed the fundamental concepts of Python because machine learning requires a lot of programming!

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If you know these topics, then you are good to go with machine learning in Python

- Object-oriented programming in Python:Classes,Objects,Methods
- Lists & List functions
- List comprehension
- List slicing
- String formatting
- List,Dictionaries & Tuples

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Now, let's understand what popular python machine learning libraries do.

We will talk about👇
- TensorFlow (+ Keras)
- PyTorch
- Pandas
- Numpy
- Matplotlib
- SciKit Learn
- Seaborn

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Now let's cover the libraries that you have to learn (at least the basics) for machine learning

1. Pandas

Pandas is a python library that allows you to store and read data from spreadsheets ( .csv, .xlsv files ) in structures called Dataframes.

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Machine learning terms you must know about as a beginner.

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These terms won't mean anything unless you know what Machine learning is all about.

> Machine learning is the process of making a program which allows a computer to learn from data.

The data could be anything, images, audio or even text.

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In machine learning we use something called a neural network, this is essentially an imitation of the human brain.

> Neural Networks are a digital imitation of the neurons you see in the human brain.

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In these neural networks, data flows through them and each neuron (the circle) has a numerical value which will change.

> The value of a neuron gets changes to something which is close to what we want each time the data passes through the neural network.

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Think of the neurons as dials on a lock, you have to tune every dial to open the lock.

It is almost impossible for a human to tune thousands of dials like these, but a computer certainly can.

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