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 use grid search cross-validation for exhaustive hyperparameter tuning</li>
</ul>

<h2>A. Grid-search cross-validation</h2>
<p>If our application requires us to absolutely obtain the best hyperparameters of a model, and if the dataset is small enough, we can apply an exhaustive grid search for tuning hyperparameters. For the grid search cross-validation, we specify possible values for each hyperparameter, and then the search will go through each possible combination of the hyperparameters and return the model with the best combination.</p>
<p>We implement grid search cross-validation with the <code><a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html#sklearn.model_selection.GridSearchCV" target="_blank">GridSearchCV</a></code> object (part of the <code>model_selection</code> module).</p>

<h2>Chapter Goals:</h2>
<ul>
  <li>Learn how to use grid search cross-validation for exhaustive hyperparameter tuning</li>
</ul>

Use exhaustive grid search techniques for hyperparameter tuning.

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

Exhaustive Tuning

Chapter Goals:

A. Grid-search cross-validation