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

Use the <code>loc</code> and <code>iloc</code> functions

Directly index columns similar to a Python dictionary

Which of the following are methods for indexing into a DataFrame?

<p>Which of the following are methods for indexing into a DataFrame?</p>


The DataFrame is the replacement of the deprecated Series

A DataFrame is faster but has less functionality

They are both aliases for the same object

What is the difference between a Series and a DataFrame?

<p>What is the difference between a Series and a DataFrame?</p>


A data feature that can be placed into one of several different categories

A list-like data feature that has many different categories of sub-data

A data feature that categorizes other data features

A data feature that is the accumulation of many other data features

A feature that indicates whether or not the data has been pre-processed

A feature that represents categorical data, using 1's and 0's to denote which categories are present

An indicator of whether or not a category is quantitative or categorical

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

Quiz