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 create a scikit-learn style regression model in XGBoost</li>
</ul>

<h2>A. XGBoost linear regression</h2>
<p>In addition to scikit-learn style classifiers, XGBoost also provides a scikit-learn style linear regression model with the <code><a href="https://xgboost.readthedocs.io/en/latest/python/python_api.html#xgboost.XGBRegressor" target="_blank">XGBRegressor</a></code> object.</p>

model = xgb.XGBRegressor(max_depth=2)
# predefined data and labels (for regression)
model.fit(data, labels)

# new_data contains 2 new data observations
predictions = model.predict(new_data)
print('Predictions:\n{}'.format(repr(predictions)))

<p>Just like the <code>XGBClassifier</code> object, we can specify the model's parameters with keyword arguments. In the code above, we set the <code>max_depth</code> parameter (representing the depth of the boosted decision tree) to 2.</p>

<h2>Chapter Goals:</h2>
<ul>
  <li>Learn how to create a scikit-learn style regression model in XGBoost</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

XGBoost Regressor

Chapter Goals:

A. XGBoost linear regression