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 filter data in NumPy</li>
  <li>Write code for filtering NumPy arrays</li>
</ul>

<h2>A. Filtering data</h2>
<p>Sometimes we have data that contains values we don't want to use. For example, when tracking the best hitters in baseball, we may want to only use the batting average data above .300. In this case, we should <i>filter</i> the overall data for only the values that we want.</p>
<p>The key to filtering data is through basic relation operations, e.g. <code>==</code>, <code>></code>, etc. In NumPy, we can apply basic relation operations element-wise on arrays.</p>
<p>The code below shows relation operations on NumPy arrays. The <code>~</code> operation represents a boolean negation, i.e. it flips each truth value in the array.

arr = np.array([[0, 2, 3],
                [1, 3, -6],
                [-3, -2, 1]])
print(repr(arr == 3))
print(repr(arr > 0))
print(repr(arr != 1))
# Negated from the previous step
print(repr(~(arr != 1)))

<p>Something to note is that <code>np.nan</code> can't be used with any relation operation. Instead, we use <code><a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.isnan.html" target="_blank">np.isnan</a></code> to filter for the location of <code>np.nan</code>.</p>
<p>The code below uses <code>np.isnan</code> to determine which locations of the array contain <code>np.nan</code> values.</p>

arr = np.array([[0, 2, np.nan],
                [1, np.nan, -6],
                [np.nan, -2, 1]])
print(repr(np.isnan(arr)))

<p>Each boolean array in our examples represents the location of elements we want to filter for. The way we perform the filtering itself is through the <code><a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.where.html" target="_blank">np.where</a></code> function.</p>

<h2>B. Filtering in NumPy</h2>
<p>The <code>np.where</code> function takes in a required first argument, which is a boolean array where <code>True</code> represents the locations of the elements we want to filter for. When the function is applied with only the first argument, it returns a tuple of 1-D arrays.</p>
<p>The tuple will have size equal to the number of dimensions in the data, and each array represents the <code>True</code> indices for the corresponding dimension. Note that the arrays in the tuple will all have the same length, equal to the number of <code>True</code> elements in the input argument.</p>
<p>The code below shows how to use <code>np.where</code> with a single argument.</p>

<h2>Chapter Goals:</h2>
<ul>
  <li>Learn how to filter data in NumPy</li>
  <li>Write code for filtering NumPy arrays</li>
</ul>

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. Filtering data

B. Filtering in NumPy