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 how to use the Keras API, a simple and compact API for creating neural networks. You'll use Keras to build a multilayer perceptron model for multiclass classification.

<h2>A. The Keras API</h2>
<p>The most popular deep learning framework in the world is <a href="https://tensorflow.org/" target="_blank">TensorFlow</a>. It is incredibly powerful, efficient, and widely used in industry. However, a downside to TensorFlow is that the code can be a bit complex, especially when setting up a model for training or evaluation.</p>
<p>A simpler alternative to TensorFlow is <a href="https://keras.io/" target="_blank">Keras</a>. The Keras API is easier to use than TensorFlow, allowing us to create, train, and evaluate a deep learning model with considerably less code. Interestingly, Keras is often run on top of TensorFlow, acting as a wrapper API to make the coding simpler.</p>
<p>Keras is excellent for building small deep learning projects, but TensorFlow is still the preferred framework for industry-level projects since it provides more utilities and efficient training mechanisms.</p>

<h2>B. Multilayer perceptron</h2>
<p>The MLP model is one of the most important neural networks for deep learning. It is a relatively simple model, but versatile enough for a variety of different applications. In this section, we'll be focusing on the Keras implementation of an MLP, rather than go into details on how it works or what it can be used for.</p>
<p>For specific details on the MLP model, see the previous chapter, "Deep Learning with TensorFlow."</p>

An overview of the Keras API and how it compares to 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. The Keras API

B. Multilayer perceptron