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 about random operations in NumPy</li>
  <li>Write code using the <code>np.random</code> submodule</li>
</ul>

<h2>A. Random integers</h2>
<p>Similar to the Python <code>random</code> module, NumPy has its own submodule for pseudo-random number generation called <code>np.random</code>. It provides all the necessary randomized operations and extends it to multi-dimensional arrays. To generate pseudo-random integers, we use the <code><a href="https://docs.scipy.org/doc/numpy-1.14.0/reference/generated/numpy.random.randint.html#numpy.random.randint" target="_blank">np.random.randint</a></code> function.</p>
<p>The code below shows example usages of <code>np.random.randint</code>.</p>

print(np.random.randint(5))
print(np.random.randint(5))
print(np.random.randint(5, high=6))

random_arr = np.random.randint(-3, high=14,
                               size=(2, 2))
print(repr(random_arr))

<p>The <code>np.random.randint</code> function takes in a single required argument, which actually depends on the <code>high</code> keyword argument. If <code>high=None</code> (which is the default value), then the required argument represents the upper (exclusive) end of the range, with the lower end being 0. Specifically, if the required argument is <i>n</i>, then the random integer is chosen uniformly from the range [0, <i>n</i>).</p>
<p>If <code>high</code> is not <code>None</code>, then the required argument will represent the lower (inclusive) end of the range, while <code>high</code> represents the upper (exclusive) end.</p>
<p>The <code>size</code> keyword argument specifies the size of the output array, where each integer in the array is randomly drawn from the specified range. As a default, <code>np.random.randint</code> returns a single integer.</p>

<h2>B. Utility functions</h2>
<p>Some fundamental utility functions from the <code>np.random</code> module are <code><a href="https://docs.scipy.org/doc/numpy-1.14.0/reference/generated/numpy.random.seed.html#numpy.random.seed" target="_blank">np.random.seed</a></code> and <code><a href="https://docs.scipy.org/doc/numpy-1.14.0/reference/generated/numpy.random.shuffle.html#numpy.random.shuffle" target="_blank">np.random.shuffle</a></code>. We use the <code>np.random.seed</code> function to set the <a href="https://en.wikipedia.org/wiki/Random_seed" target="_blank">random seed</a>, which allows us to control the outputs of the pseudo-random functions. The function takes in a single integer as an argument, representing the random seed.</p>
<p>The code below uses <code>np.random.seed</code> with the same random seed. Note how the outputs of the random functions in each subsequent run are identical when we set the same random seed.</p>

<h2>Chapter Goals:</h2>
<ul>
  <li>Learn about random operations in NumPy</li>
  <li>Write code using the <code>np.random</code> submodule</li>
</ul>

Generate numbers and arrays from different random distributions.

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

Random

Chapter Goals:

A. Random integers