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 save and load data in NumPy</li>
  <li>Write code to save NumPy data to a file</li>
</ul>

<h2>A. Saving</h2>
<p>After performing data manipulation with NumPy, it's a good idea to save the data in a file for future use. To do this, we use the <code><a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.save.html" target="_blank">np.save</a></code> function.</p>
<p>The first argument for the function is the name/path of the file we want to save our data to. The file name/path should have a ".npy" extension. If it does not, then <code>np.save</code> will append the ".npy" extension to it.</p>
<p>The second argument for <code>np.save</code> is the NumPy data we want to save. The function has no return value. Also, the format of the ".npy" files when viewed with a text editor is largely gibberish when viewed with a text editor.</p>

If `np.save` is called with the name of a file that already exists, it will overwrite the previous file.

<p>The code below shows examples of saving NumPy data.</p>



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

Saving Data

Chapter Goals:

A. Saving