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

It lets us view data in terms of standard deviations from the average case (i.e. mean)

It lets us use much smaller data values, which makes computation much quicker

It makes it easier to calculate the mean of each column in the data

What is the main purpose of standardizing data?

<p>What is the main purpose of standardizing data?</p>


Combining multiple datasets into one large dataset

Subtracting the column-wise means from the values in a dataset

Replacing missing data in the dataset with substituted values

To increase the number of features in a dataset

To remove correlation from dataset features and perform dimensionality reduction

To remove repeated observations from the dataset

Plot a percentage of points from each class based on the class distribution

Use different colors and include a legend to distinguish data by class label

Put each class' points on separate plots, never on the same plot

What is a good practice for plotting data separated by class label?

<p>What is a good practice for plotting data separated by class label?</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

Quiz