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.

ml.tar.gz

mnist-fashion

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 cross-validate parameters in XGBoost</li>
</ul>

<h2>A. Choosing parameters</h2>
<p>Since there are many parameters in XGBoost and several possible values for each parameter, it is usually necessary to <i>tune</i> the parameters. In other words, we want to try out different parameter settings and see which one gives us the best results.</p>
<p>We can tune the parameters using <a href="https://en.wikipedia.org/wiki/Cross-validation_(statistics)" target="_blank">cross-validation</a> (for a detailed explanation of cross-validation, see the <b>Data Modeling</b> section). In XGBoost, the <code><a href="https://xgboost.readthedocs.io/en/latest/python/python_api.html#xgboost.cv" target="_blank">cv</a></code> function performs cross-validation for a set of parameters on a given training dataset.</p>
<p>The code below demonstrates cross-validation in XGBoost.</p>

<h2>Chapter Goals:</h2>
<ul>
  <li>Learn how to cross-validate parameters in XGBoost</li>
</ul>

Use cross-validation to evaluate parameters for XGBoost.

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

Cross-Validation

Chapter Goals:

A. Choosing parameters