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

The number of identical feature values between two data observations

The similarity in a particular feature column for two data observations

The similarity between two feature values for a particular data observation

The proportional similarity in feature values between two data observations

What does cosine similarity measure (in terms of a dataset)?

What does cosine similarity measure (in terms of a dataset)?

The number of clusters specified prior to starting the algorithm

The number of clusters automatically determined by the algorithm

The average number of data points in each cluster

The minimum number of data points in each cluster

What does the K represent in K-means clustering?

What does the K represent in K-means clustering?

Which of the following algorithms automatically determines the number of clusters?

Which of the following algorithms automatically determines the number of clusters?

It is not affected by permutations in the labeling

The ARI function in scikit-learn is symmetric (i.e. can swap argument order)

It is corrected-for-chance: random labelings always get a score of -1

Which of the following is inaccurate about adjusted Rand index?

Which of the following is inaccurate about adjusted Rand index?

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