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Could someone tell me what is the quickest way to learn Machine Learning and build a Neural Network using Python?

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Deep learning and neural networks work best on data without much structure.

Dataframes have structure, images, videos, audio files, and natural language text have structure but not as much.

Tidbit: For most cases, you’ll want to use an ensemble of decision trees (Random Forests or an algorithm like XGBoost) for structured data and you’ll want to use deep learning or transfer learning (taking a pre-trained neural network and using it on your problem) for unstructured data.

You could start a note with little tidbits like this for yourself and collect them as you go.

Resources for learning by Andrew Ng on Coursera — deep learning taught by one of the best in business. deep learning courses by Jeremy Howard — a hands-on approach to deep learning taught by one of the industry's best practitioners.

Neural networks (NN), also called artificial neural networks (ANN) are a subset of learning algorithms within the machine learning field that are loosely based on the concept of biological neural networks.

Andrey Bulezyuk, who is a German-based machine learning specialist with more than five years of experience, says that “neural networks are revolutionizing machine learning because they are capable of efficiently modeling sophisticated abstractions across an extensive range of disciplines and industries.”

Basically, an ANN comprises of the following components:

An input layer that receives data and passes it on

A hidden layer

An output layer

Weights between the layers

A deliberate activation function for every hidden layer. In this simple neural network Python tutorial, we’ll employ the Sigmoid activation function.

There are several types of neural networks. In this project, we are going to create the feed-forward or perception neural networks. This type of ANN relays data directly from the front to the back.

Training the feed-forward neurons often needs back-propagation, which provides the network with corresponding sets of inputs and outputs. When the input data is transmitted into the neuron, it is processed, and an output is generated.

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