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 indexing arrays in NumPy</li>
  <li>Write code for indexing and slicing arrays</li>
</ul>

<h2>A. Array accessing</h2>
<p>Accessing NumPy arrays is identical to accessing Python lists. For multi-dimensional arrays, it is equivalent to accessing Python lists of lists.</p>
<p>The code below shows example accesses of NumPy arrays.</p>

arr = np.array([1, 2, 3, 4, 5])
print(arr[0])
print(arr[4])

arr = np.array([[6, 3], [0, 2]])
# Subarray
print(repr(arr[0]))

<h2>B. Slicing</h2>
<p>NumPy arrays also support slicing. Similar to Python, we use the colon operator (i.e. <code>arr[:]</code>) for slicing. We can also use negative indexing to slice in the backwards direction.</p>
<p>The code below shows example slices of a 1-D NumPy array.</p>

arr = np.array([1, 2, 3, 4, 5])
print(repr(arr[:]))
print(repr(arr[1:]))
print(repr(arr[2:4]))
print(repr(arr[:-1]))
print(repr(arr[-2:]))

<p>For multi-dimensional arrays, we can use a comma to separate slices across each dimension.</p>
<p>The code below shows example slices of a 2-D NumPy array.</p>

arr = np.array([[1, 2, 3],
                [4, 5, 6],
                [7, 8, 9]])
print(repr(arr[:]))
print(repr(arr[1:]))
print(repr(arr[:, -1]))
print(repr(arr[:, 1:]))
print(repr(arr[0:1, 1:]))
print(repr(arr[0, 1:]))

<h2>C. Argmin and argmax</h2>
<p>In addition to accessing and slicing arrays, it is useful to figure out the actual indexes of the minimum and maximum elements. To do this, we use the <code><a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.argmin.html" target="_blank">np.argmin</a></code> and <code><a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.argmax.html" target="_blank">np.argmax</a></code> functions.</p>
<p>The code below shows example usages of <code>np.argmin</code> and <code>np.argmax</code>. Note that the index of element <code>-6</code> is index <code>5</code> in the flattened version of <code>arr</code>.</p>

<h2>Chapter Goals:</h2>
<ul>
  <li>Learn about indexing arrays in NumPy</li>
  <li>Write code for indexing and slicing arrays</li>
</ul>

Index into NumPy arrays to extract data and array slices.

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

Indexing

Chapter Goals:

A. Array accessing

B. Slicing