Machine Learning Pro End to End™
With Case Study This comprehensive course offers an in-depth journey into Machine Learning and Data Science
7
students
2 hours
content
Jan 2025
last update
$44.99
regular price
What you will learn
Introduction to Machine Learning:- Understand the basics and types of Machine Learning.
ML Unsupervised Learning:- Learn the concepts and techniques of Unsupervised Learning.
Supervised Learning - Regression:- Master regression models for predicting continuous outcomes.
Evaluation Metrics for Regression Model:- Evaluate regression models using metrics like MSE, RMSE, and R-squared.
Supervised Learning - Classification in Machine Learning:- Learn classification algorithms for categorical predictions.
Supervised Learning - Decision Trees:- Understand how Decision Trees work for classification and regression.
Unsupervised Learning - Clustering:- Explore clustering techniques to group data points.
Unsupervised Learning - DBSCAN Clustering: Apply the DBSCAN algorithm for density-based clustering.
Unsupervised Learning - Dimensionality Reduction:- Learn techniques to reduce data dimensions while retaining key information.
Unsupervised Learning - Dimensionality Reduction with t-SNE:- Use t-SNE for visualizing high-dimensional data in a reduced form.
Model Evaluation and Validation Techniques:- Understand model validation methods like cross-validation.
Model Evaluation - Bias-Variance Tradeoffs:- Learn to balance bias and variance for improved model performance.
Introduction to Python Libraries for Data Science:- Get familiar with key Python libraries such as NumPy, Pandas, and Scikit-learn.
Introduction to Python Libraries for Data Science:- Explore advanced Python libraries used in data analysis and machine learning.
Introduction to R Libraries for Data Science:- Learn essential R libraries for data manipulation and modeling.
Introduction to R Libraries for Data Science Statistical Modeling:- Apply statistical modeling using R's powerful libraries.
Screenshots
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udemy ID
9/10/2024
course created date
4/9/2025
course indexed date
adedayo0001
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