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

# Chapter Goals:

<ul>
 <li>Learn how to index a DataFrame to retrieve rows and columns</li>
 <li>Write code for indexing a DataFrame</li>
</ul>

<h2>A. Direct indexing</h2>
When indexing into a DataFrame, we can treat the DataFrame as a dictionary of Series objects, where each column represents a Series. Each column label then becomes a key, allowing us to directly retrieve columns using dictionary-like bracket notation.
The code below shows how to directly index into a DataFrame's columns.

df = pd.DataFrame({'c1': [1, 2], 'c2': [3, 4],
                   'c3': [5, 6]}, index=['r1', 'r2'])
col1 = df['c1']
# Newline for separating print statements
print('{}\n'.format(col1))

col1_df = df[['c1']]
print('{}\n'.format(col1_df))

col23 = df[['c2', 'c3']]
print('{}\n'.format(col23))

Note that when we use a single column label inside the bracket (as was the case for <code>col1</code> in the code example), the output is a Series representing the corresponding column. When we use a list of column labels (as was the case for <code>col1_df</code> and <code>col23</code>), the output is a DataFrame that contains the corresponding columns.
We can also use direct indexing to retrieve a subset of the rows (as a DataFrame). However, we can only retrieve rows based on slices, rather than specifying particular rows.
The code below shows how to directly index into a DataFrame's rows.

Understand how DataFrame values can be accessed via indexing.

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. Direct indexing