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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

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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This course includes

165 Playgrounds
115 Challenges
8 Quizzes
Course Overview
Course Content
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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
If you're a software engineer looking to add machine learning to your skillset, this is the place to start. This course will t...Show More

Course Content

1.

What you'll learn from this course

1 Lessons

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

2.

Data Manipulation with NumPy

11 Lessons

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

3.

Data Analysis with pandas

14 Lessons

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

4.

Data Preprocessing with scikit-learn

9 Lessons

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

5.

Data Modeling with scikit-learn

13 Lessons

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

6.

Clustering with scikit-learn

10 Lessons

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

7.

Gradient Boosting with XGBoost

10 Lessons

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

8.

Deep Learning with TensorFlow

12 Lessons

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

9.

Deep Learning with Keras

7 Lessons

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

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