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 the methods used to combine DataFrame objects</li>
  <li>Write code for combining DataFrames</li>
</ul>

<p>In the previous chapter, we discussed the <code>append</code> function for concatenating DataFrame rows. To concatenate multiple DataFrames along either rows or columns, we use the <code><a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.concat.html" target="_blank">pd.concat</a></code> function.</p>
<p>The code below shows example usages of <code>pd.concat</code>.</p>

df1 = pd.DataFrame({'c1':[1,2], 'c2':[3,4]},
                   index=['r1','r2'])
df2 = pd.DataFrame({'c1':[5,6], 'c2':[7,8]},
                   index=['r1','r2'])
df3 = pd.DataFrame({'c1':[5,6], 'c2':[7,8]})

concat = pd.concat([df1, df2], axis=1)
# Newline to separate print statements
print('{}\n'.format(concat))

concat = pd.concat([df2, df1, df3])
print('{}\n'.format(concat))

concat = pd.concat([df1, df3], axis=1)
print('{}\n'.format(concat))

<p>The <code>pd.concat</code> function takes in a list of pandas objects (normally a list of DataFrames) to concatenate. The function also takes in numerous keyword arguments, with <code>axis</code> being one of the more important ones. The <code>axis</code> argument specifies whether we concatenate the rows (<code>axis=0</code>, the default), or concatenate the columns (<code>axis=1</code>).</p>
This works very similarly to <a href='https://www.educative.io/courses/machine-learning-numpy-pandas-scikit-learn/aggregation/'>concatenation in NumPy</a>
<p>In the code example, the final call to <code>pd.concat</code> resulted in a DataFrame with many <code>NaN</code> values. This is because the row labels for <code>df1</code> and <code>df3</code> did not match, so result was padded with <code>NaN</code> in locations where values did not exist.</p>

<h2>Chapter Goals:</h2>
<ul>
  <li>Understand the methods used to combine DataFrame objects</li>
  <li>Write code for combining DataFrames</li>
</ul>

Combine multiple DataFrames through concatenation and merging.

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

Combining

Chapter Goals: