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Machine Learning with NumPy, pandas, scikit-learn, and More

Learn practical machine learning with NumPy, pandas, scikit-learn, and more. Learn data analysis, feature engineering, and deep learning using industry-standard frameworks. Basic Python required.

4.5

88 Lessons

15h

Updated 1 month ago

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Learning Roadmap

88 Lessons8 Quizzes115 Challenges

What you'll learn from this course

Get familiar with machine learning processes, types, and model building essentials.

Overview

Data Manipulation with NumPy

Grasp the fundamentals of data manipulation with NumPy arrays, arithmetic operations, and statistical analysis.

Data Analysis with pandas

15 Lessons

Master the steps to utilize pandas for MLB data analysis, including processing, manipulation, and visualization.

Data Preprocessing with scikit-learn

9 Lessons

Grasp the fundamentals of scikit-learn's data preprocessing techniques for scaling, normalizing, imputing, and dimensional reduction.

Data Modeling with scikit-learn

13 Lessons

Dig into scikit-learn's data modeling techniques, including linear regression, classification, and hyperparameter tuning.

Clustering with scikit-learn

10 Lessons

Follow the process of clustering algorithms, evaluating their performance, and feature clustering in scikit-learn.

Gradient Boosting with XGBoost

10 Lessons

Build on XGBoost for efficient, high-performance gradient-boosted decision trees in data science.

Deep Learning with TensorFlow

12 Lessons

Learn how to use TensorFlow for neural networks, from MLPs to multiclass classifications.

Deep Learning with Keras

7 Lessons

Get started with creating, configuring, and evaluating neural network models using Keras.

Machine Learning Fundamentals

Mock Interview

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Certificate of Completion

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Developed by MAANG Engineers

ABOUT THIS COURSE

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.

ABOUT THE AUTHOR

Adaptilab

AdaptiLab is a Seattle-based SaaS startup helping companies grow their machine learning teams from hiring to productivity.

Learn more about Adaptilab

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