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

This course equips learners with practical data analysis and machine learning with pandas and other frameworks such as NumPy and TensorFlow.

Intermediate

87 Lessons

15h

Certificate of Completion

This course equips learners with practical data analysis and machine learning with pandas and other frameworks such as NumPy and TensorFlow.

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This course includes

165 Playgrounds

115 Challenges

8 Quizzes

Course Overview

Course Content

Apply Your Skills

Recommendations

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

1 Lessons

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

Overview

Data Manipulation with NumPy

11 Lessons

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

Data Analysis with pandas

14 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.

PCA

Data Modeling with scikit-learn

13 Lessons

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

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.

Hierarchical Clustering

Mean Shift Clustering

Gradient Boosting with XGBoost

10 Lessons

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

Hyperparameter Tuning

Model Persistence

Quiz

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

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Project

Implement the Decision Tree Classifier from Scratch

intermediate

2 hour

Project

Implement the Decision Tree Classifier from Scratch

Implement a decision tree classifier from scratch in Python using the ID3 algorithm, including training, testing, and visualization.

intermediate

2 hours

Project

Customer Segmentation with K-Means Clustering

intermediate

1 hour

Project

Customer Segmentation with K-Means Clustering

Learn customer segmentation using k-means clustering to improve marketing strategies and enhance customer experience with real datasets.

intermediate

1 hour

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