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'll use <a href="https://en.wikipedia.org/wiki/Pandas_(software)" target="_blank">pandas</a> to analyze Major League Baseball (MLB) data. The data comes courtesy of <a href="https://en.wikipedia.org/wiki/Sean_Lahman" target="_blank">Sean Lahman</a> and contains statistics for every player, manager, and team in MLB history. The full database can be found and downloaded <a href="http://www.seanlahman.com/baseball-archive/statistics/" target="_blank">here</a>.</p>

<h2>A. Data analysis</h2>
<p>Before doing any task with a dataset, it is a good idea to perform preliminary <a href="https://en.wikipedia.org/wiki/Data_analysis" target="_blank">data analysis</a>. Data analysis allows us to understand the dataset, find potential outlier values, and figure out which features of the dataset are most important to our application.</p>

<h2>B. pandas</h2>
<p>Since most machine learning frameworks (e.g. TensorFlow) are built on Python, it is beneficial to use a Python-based data analysis toolkit like pandas. pandas (all lowercase) is an excellent tool for processing and analyzing real world data, with utilities ranging from parsing multiple file formats to converting an entire data table into a NumPy matrix array.</p>
<p>In the following chapters we'll dive into the main data analysis functionalities of pandas. For a complete overview of the pandas toolkit, you can visit the <a href="https://pandas.pydata.org/pandas-docs/stable/" target="_blank">pandas website</a>.</p>

<h2>C. Matplotlib and pyplot</h2>
<p>An essential part of data analysis is creating charts and plots to visualize the data. Similar to the saying, "a picture is worth a thousand words", data visualization can convey key data trends and correlations through a single figure.</p>
<p>The library we will use for data visualization in Python is <a href="https://en.wikipedia.org/wiki/Matplotlib" target="_blank">Matplotlib</a>. Specifically, we'll be using the <a href="https://matplotlib.org/api/pyplot_api.html" target="_blank">pyplot</a> API of Matplotlib, which provides a variety of plotting tools from simple line plots to advanced visuals like heatmaps and 3-D plots. While we will only touch on the basic necessities for our data analysis (e.g. line plots, boxplots, etc.), a full overview of Matplotlib can be found at its <a href="https://matplotlib.org/index.html" target="_blank">website</a>.</p>

An overview of data analysis with pandas.

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. Data analysis

B. pandas

C. Matplotlib and pyplot