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

# Chapter Goals:

<ul>
  <li>Learn how to group DataFrames by columns</li>
  <li>Write code to retrieve home run statistics through DataFrame grouping</li>
</ul>

<h2>A. Grouping by column</h2>
<p>When dealing with large amounts of data, it is usually a good idea to group the data by common categories. For example, we could group a large dataset of MLB player statistics by year, so we can deal with each year's data separately.</p>
<p>With pandas DataFrames, we can perform dataset grouping with the <code><a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.groupby.html" target="_blank">groupby</a></code> function. A common usage of the function is to group a DataFrame by values from a particular column, e.g. a column representing years.</p>
<p>The code below shows how to use the <code>groupby</code> function, with the example of grouping MLB data by year.</p>

Learn how DataFrames can be grouped based on particular columns.

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

Grouping

Chapter Goals:

A. Grouping by column