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

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.

Intermediate

87 Lessons

15h

Certificate of Completion

AI-POWERED

Explanations

Adaptive Learning

AI-POWERED

Explanations

Adaptive Learning

This course includes

165 Playgrounds

115 Challenges

8 Quizzes

This course includes

165 Playgrounds

115 Challenges

8 Quizzes

Course Overview

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, sup...Show More

Course Content

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.

Introduction

NumPy Arrays NumPy Basics Math Random Indexing Filtering Statistics Aggregation Saving Data Quiz

Data Analysis with pandas

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

Introduction

Series DataFrame Combining Indexing File I/O Grouping Features Filtering Sorting Metrics Plotting To NumPy Quiz

Data Preprocessing with scikit-learn

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

Introduction

Standardizing Data Data Range Robust Scaling Normalizing Data Data Imputation PCA Labeled Data Quiz

Data Modeling with scikit-learn

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

Introduction Linear Regression Ridge Regression LASSO Regression Bayesian Regression Logistic Regression Decision Trees Training and Testing Cross-Validation Applying CV to Decision Trees Evaluating Models Exhaustive Tuning Quiz

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.

Course Author

Show License and Attributions

Part of the Machine Learning Path

Become a Machine Learning Engineer

This Skill Path covers Python fundamentals, machine learning techniques and algorithms through practical exercises, preparing you as a machine learning engineer for real-world applications.

12 Modules

Trusted by 1.4 million developers working at companies

Anthony Walker

@_webarchitect_

Emma Bostian 🐞

@EmmaBostian

Evan Dunbar

ML Engineer

Carlos Matias La Borde

Software Developer

Souvik Kundu

Front-end Developer

Vinay Krishnaiah

Software Developer

Eric Downs

Musician/Entrepeneur

Kenan Eyvazov

DevOps Engineer

Anthony Walker

@_webarchitect_

Emma Bostian 🐞

@EmmaBostian

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Evaluate and debug your code with the click of a button. Get real-time feedback on test cases, including time and space complexity of your solutions.

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Adaptive Learning

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AI Code Mentor

AI Code Mentor helps you quickly identify errors in your code, learn from your mistakes, and nudge you in the right direction — just like a 1:1 tutor!