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 handle file input/output using pandas</li>
  <li>Write code for processing data files</li>
</ul>

<h2>A. Reading data</h2>
<p>One of the most important features in pandas is the ability to read from data files. pandas accepts a variety of file formats, ranging from CSV and Excel spreadsheets to SQL and even HTML. A full list of the available file formats for pandas can be found <a href="http://pandas.pydata.org/pandas-docs/stable/io.html" target="_blank">here</a>.</p>
<p>In this chapter we'll focus on three of the most common file types: <a href="https://en.wikipedia.org/wiki/Comma-separated_values" target="_blank">CSV</a>, <a href="https://en.wikipedia.org/wiki/Microsoft_Excel" target="_blank">XLSX (Microsoft Excel)</a>, and <a href="https://en.wikipedia.org/wiki/JSON" target="_blank">JSON</a>. For reading data from a file, we use either the <code><a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_csv.html" target="_blank">read_csv</a></code>, <code><a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_excel.html" target="_blank">read_excel</a></code>, or <code><a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_json.html" target="_blank">read_json</code></a> function, depending on the file type.</p>
<p>Each of the file reading functions takes in a file path as the only required argument. Each function has numerous keyword arguments, so we won't get into most of them. However, we'll still discuss a couple of the more commonly used keyword arguments.</p>

Read from and write to different types of files in pandas.

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

File I/O