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 <a href="https://xgboost.readthedocs.io/en/latest/python/index.html" target="_blank">XGBoost</a>, a library for highly efficient gradient boosted decision trees. It is one of the premier libraries used in data science for classification and regression.

<h2>A. XGBoost vs. scikit-learn</h2>
<p>In the previous three chapters, we used scikit-learn for a variety of data-related tasks. In this chapter, we cover XGBoost, a state-of-the-art data science library for performing classification and regression. XGBoost makes use of <a href="https://en.wikipedia.org/wiki/Gradient_boosting#Gradient_tree_boosting" target="_blank">gradient boosted decision trees</a>, which provides better performance than regular decision trees.</p>
<p>In addition to the performance boost, XGBoost implements an extremely efficient version of gradient boosted trees. The XGBoost models train much faster than scikit-learn models, while still providing the same ease of use.</p>
<p>For data science and machine learning competitions that use small- to medium-sized datasets (e.g., <a href="https://en.wikipedia.org/wiki/Kaggle" target="_blank">Kaggle</a>), XGBoost is always among the top performing models.</p>

<h2>B. Gradient boosted trees</h2>
<p>The problem with regular decision trees is that they are often not complex enough to capture the intricacies of many large datasets. We could continuously increase the maximum depth of a decision tree to fit larger datasets, but decision trees with many nodes tend to overfit the data.</p>
<p>Instead, we make use of gradient boosting to <i>combine</i> many decision trees into a single model for classification or regression. Gradient boosting starts off with a single decision tree and iteratively adds more decision trees to the overall model to correct the model's errors on the training dataset.</p>
<p>The XGBoost API handles the gradient boosting process for us, which produces a much better model than if we had used a single decision tree.</p>

An intro to XGBoost and gradient boosted decision trees.

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. XGBoost vs. scikit-learn

B. Gradient boosted trees