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

<h2 style="font-size:1.1em">Chapter Goals:</h2>
<ul>
  <li>Learn how to convert a DataFrame to a NumPy matrix</li>
  <li>Write code to modify an MLB dataset and convert it to a NumPy matrix</li>
</ul>

<h2>A. Machine learning</h2>
<p>The DataFrame object is great for storing a dataset and performing data analysis in Python. However, most machine learning frameworks (e.g. TensorFlow), work directly with NumPy data. Furthermore, the NumPy data used as input to machine learning models must solely contain quantitative values.</p>
<p>Therefore, to use a DataFrame's data with a machine learning model, we need to convert the DataFrame to a NumPy matrix of quantitative data. So even the categorical features of a DataFrame, such as gender and birthplace, must be converted to quantitative values.</p>

<h2>B. Indicator features</h2>
<p>When converting a DataFrame to a NumPy matrix of quantitative data, we need to find a way to modify the categorical features in the DataFrame.</p>
<p>The easiest way to do this is to convert each categorical feature into a set of <i>indicator features</i> for each of its categories. The indicator feature for a specific category represents whether or not a given data sample belongs to that category.</p>
<p>The code below shows a DataFrame with indicator features.</p>

<h2>Chapter Goals:</h2>
<ul>
  <li>Learn how to convert a DataFrame to a NumPy matrix</li>
  <li>Write code to modify an MLB dataset and convert it to a NumPy matrix</li>
</ul>

Understand how DataFrames can be converted to 2-D NumPy arrays.

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

To NumPy

Chapter Goals:

A. Machine learning