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There is a lot of dispersed, and somewhat conflicting information on the internet when it comes to data science, making it tough to know where to start. Don't worry. 

This course will get you familiar with the state of data science and the related fields such as machine learning and big data. You will be going through the fundamental concepts and libraries which are essential to solve any problem in this field. 

You will work on real-time projects from Kaggle while also honing your mathematical skills which will be used extensively in most problems you face. You will also be taken through a systematic approach to learning about data acquisition to data wrangling and everything in between. This is your all-in-one guide to becoming a confident data scientist.

An Introductory Guide to Data Science and Machine Learning

## Multi-Layer Perceptrons

We covered perceptrons in the last lesson. You saw how they cannot be used for the XOR problem. In this chapter, we will look into multi-layer perceptrons and how they can solve non-linear problems. Multi-layer perceptrons are also referred to as **Feedforward Networks**. 

# Multi-Layer Perceptrons

We covered perceptrons in the last lesson. You saw how they cannot be used for the XOR problem. In this chapter, we will look into multi-layer perceptrons and how they can solve non-linear problems. Multi-layer perceptrons are also referred to as **Feedforward Networks**. 

In this lesson, we will extend our single layer perceptron to include more layers, so that it can learn more complex representations.

What is Data Science ?

Applications of Data Science

Overview of Libraries

Probability and Statistics

Machine Learning Part-1

Machine Learning Part-2

Machine Learning Part-3

Deep Learning

Machine Learning Tools and Libraries

Big Data Tools and Technologies

Where to go next ?

Feedforward Neural Networks

Multi-Layer Perceptrons