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>Understand how to filter a DataFrame based on filter conditions</li>
  <li>Write code to filter a dataset of MLB statistics</li>
</ul>

<h2>A. Filter conditions</h2>
<p>In the Data Manipulation section, we used relation operations on NumPy arrays to create <i>filter conditions</i>. These filter conditions returned boolean arrays, which represented the locations of the elements that pass the filter.</p>
<p>In pandas, we can also create filter conditions for DataFrames. Specifically, we can use relation operations on a DataFrame's column features, which will return a boolean Series representing the DataFrame rows that pass the filter.</p>
<p>The code below demonstrates how to use relation operations as filter conditions.</p>

df = pd.DataFrame({
  'playerID': ['bettsmo01', 'canoro01', 'cruzne02', 'ortizda01', 'cruzne02'],
  'yearID': [2016, 2016, 2016, 2016, 2017],
  'teamID': ['BOS', 'SEA', 'SEA', 'BOS', 'SEA'],
  'HR': [31, 39, 43, 38, 39]})
  
print('{}\n'.format(df))

cruzne02 = df['playerID'] == 'cruzne02'
print('{}\n'.format(cruzne02))

hr40 = df['HR'] > 40
print('{}\n'.format(hr40))

notbos = df['teamID'] != 'BOS'
print('{}\n'.format(notbos))

<p>In the code above, we created filter conditions for <code>df</code> based on the columns labeled <code>'playerID'</code>, <code>'HR'</code>, and <code>'teamID'</code>. The boolean Series outputs have <code>True</code> for the rows that pass the filter, and <code>False</code> for the rows that don't.</p>

<h2>Chapter Goals:</h2>
<ul>
  <li>Understand how to filter a DataFrame based on filter conditions</li>
  <li>Write code to filter a dataset of MLB statistics</li>
</ul>

Filter DataFrames for values that fit certain conditions.

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

Filtering

Chapter Goals:

A. Filter conditions