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 facets of model execution for Keras models</li>
</ul>

<h2>A. Training</h2>
<p>After configuring a Keras model for training, it only takes a single line of code to actually perform the training. We use the <code>Sequential</code> model's <code><a href="https://keras.io/models/sequential/#fit" target="_blank">fit</a></code> function to train the model on input data and labels.</p>
<p>The first two arguments of the <code>fit</code> function are the input data and labels, respectively. Unlike TensorFlow (where we need to use tensor objects for any sort of data), we can simply use NumPy arrays as the input arguments for the <code>fit</code> function.</p>
<p>The training batch size can be specified using the <code>batch_size</code> keyword argument (the default is a batch size of 32). We can also specify the number of epochs, i.e. number of full run-throughs of the dataset during training, using the <code>epochs</code> keyword argument (the default is 1 epoch).</p>

<h2>Chapter Goals:</h2>
<ul>
  <li>Understand the facets of model execution for Keras models</li>
</ul>

Learn how to train, evaluate, and make predictions with a Keras model.

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

Model Execution

Chapter Goals:

A. Training