2025 Machine Learning & Data Science for Beginners in Python
Data Science Projects with Linear Regression, Logistic Regression, Random Forest, SVM, KNN, KMeans, XGBoost, PCA etc
4.38 (224 reviews)
13,145
students
32 hours
content
Apr 2025
last update
$64.99
regular price
What you will learn
The fundamental concepts and techniques of machine learning, including supervised and unsupervised learning
The implementation of various machine learning algorithms such as linear regression, logistic regression, k-nearest neighbors, decision trees, etc.
Techniques for building and evaluating machine learning models, such as feature selection, feature engineering, and model evaluation techniques.
The different types of model evaluation metrics, such as accuracy, precision, and recall and how to interpret them.
The use of machine learning libraries such as scikit-learn and pandas to build and evaluate models.
Hands-on experience working on real-world datasets and projects that will give students the opportunity to apply the concepts and techniques learned throughout.
The ability to analyze, interpret and present the results of machine learning models.
Understanding of the trade-offs between different machine learning algorithms, and their advantages and disadvantages.
Understanding of the best practices for developing, implementing, and interpreting machine learning models.
Skills in troubleshooting common machine learning problems and debugging machine learning models.
3435072
udemy ID
8/20/2020
course created date
1/29/2023
course indexed date
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