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

Returns a 1-D array of numbers based on a a given range and interval

What does the <code>arange</code> function do?

What does the <code>arange</code> function do?

The data is saved to the file without the .npy extension

The function automatically adds the .npy extension

What happens if we try to use <code>np.save</code> on a file without a .npy extension?

What happens if we try to use <code>np.save</code> on a file without a .npy extension?

The former is computationally more expensive than the latter

The former is significantly slower than the latter

The former produces the overall sum while the latter calculates cumulative sums

What is the difference between <code>np.sum</code> and <code>np.cumsum</code>?

What is the difference between <code>np.sum</code> and <code>np.cumsum</code>?

A 1-D array containing the column sums of <code>arr</code>

A 1-D array containing the row sums of <code>arr</code>

A 2-D array containing the cumulative column sums of <code>arr</code>

A 1-D array containing the cumulative row sums of <code>arr</code>

Consider the 2-D array, <code>arr</code>. What is the output of <code>np.sum(arr, axis=1)</code>?

Consider the 2-D array, <code>arr</code>. What is the output of <code>np.sum(arr, axis=1)</code>?

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