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

<p>In this chapter, you will continue to use scikit-learn, creating a variety of models for linear regression and classifying data. You'll also learn how to perform hyperparameter tuning and model evaluation through cross-validation.</p>


<h2>A. Creating models for data</h2>
<p>The main job of a data scientist is analyzing data and creating models for obtaining results from the data. Oftentimes, data scientists will use simple statistical models for their data, rather than machine learning models like neural networks. This is because data scientists tend to work with smaller datasets than machine learning engineers, so they can quickly extract good results using statistical models.</p>
<p>The scikit-learn library provides many statistical models for <a href="https://en.wikipedia.org/wiki/Linear_regression" target="_blank">linear regression</a>. It also provides a few good models for classifying data, which will be introduced in later chapters.</p>
<p>When creating these models, data scientists need to figure out the optimal <a href="https://en.wikipedia.org/wiki/Hyperparameter" target="_blank">hyperparameters</a> to use. Hyperparameters are values that we set when creating a model, e.g. certain constant coefficients used in the model's calculations. We'll talk more about hyperparameter tuning, the process of finding the optimal hyperparameter settings, in later chapters.</p>

An overview of the main models used in scikit-learn.

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. Creating models for data