Delve into practical machine learning with NumPy, pandas, scikit-learn, and more. Gain insights into data analysis, feature engineering, and deep learning using industry-standard frameworks. Basic Python required.

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If you're a software engineer looking to add machine learning to your skillset, this is the place to start. 

This course will teach you to write useful code and create impactful machine learning applications immediately. From the start, you'll be given all the tools that you need to create industry-level machine learning projects. Rather than reading through dense theory, you’ll learn practical skills and gain actionable insights. Topics covered include data analysis/visualization, feature engineering, supervised learning, unsupervised learning, and deep learning. All of these topics are taught using industry-standard frameworks: NumPy, pandas, scikit-learn, XGBoost, TensorFlow, and Keras.

Basic knowledge of Python is a prerequisite to this course.

This course was created by AdaptiLab, a company specializing in evaluating, sourcing, and upskilling enterprise machine learning talent. It is built in collaboration with industry machine learning experts from Google, Microsoft, Amazon, and Apple.

Machine Learning with NumPy, pandas, scikit-learn, and More

In this chapter, you'll learn about one of the most essential neural networks used in deep learning: the multilayer perceptron. You'll also learn how to use the TensorFlow framework to manipulate this neural network model.

<h2>A. Multilayer perceptron</h2>
A multilayer perceptron (MLP) is used for a variety of tasks, such as stock analysis, spam detection, and election voting predictions. In this chapter and the next one, you'll learn how to code your own MLP and apply it to the task of classifying 2-D points in the Cartesian plane.
You'll also learn the basics of creating a computation graph—the structure of a neural network. The structure of a neural network can be viewed in layers of neurons:

An overview of the multilayer perceptron neural network and deep learning in TensorFlow.

What you'll learn from this course

Data Manipulation with NumPy

Data Analysis with pandas

Data Preprocessing with scikit-learn

Data Modeling with scikit-learn

Clustering with scikit-learn

Gradient Boosting with XGBoost

Deep Learning with TensorFlow

Deep Learning with Keras

Introduction

A. Multilayer perceptron