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Advanced Machine Learning with Python
Updated on Sep, 2026
Programming, Data Science, Machine Learning
Duration - 5.5 hours
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Welcome to "Advanced Machine Learning with Python," a comprehensive course designed to equip you with the advanced knowledge and skills needed to excel in machine learning. Throughout this program, you will explore various advanced machine learning concepts, algorithms, and practical applications.
This course covers crucial topics, including feature selection, data preprocessing, bagging and boosting techniques, and model tuning with grid search. You will also delve into specific algorithms such as linear regression, random forest, and Naive Bayes theorem, gaining hands-on experience with Python to build and optimize machine learning models.
Bagging and Boosting: Explore ensemble learning techniques such as bagging and boosting to enhance model accuracy and robustness.
Model Tuning and Grid Search: Understand the process of model tuning using grid search to find the optimal hyperparameters for your models.
Linear Regression: Dive deep into linear regression, understanding its principles and applications.
This course is tailored for data scientists, machine learning enthusiasts, and AI practitioners.
It aims to enhance your proficiency in applying advanced machine learning techniques effectively.
You will learn to implement advanced algorithms, optimize models, and utilize feature engineering strategies to extract meaningful insights from data
Basic Understanding of Python Programming: Including familiarity with libraries such as NumPy, Pandas, and Scikit-learn.
Knowledge of Machine Learning Fundamentals: Understanding of basic machine learning concepts and algorithms.
Familiarity with Statistics and Probability: Basic knowledge of statistics and probability is beneficial.
Check out the detailed breakdown of what’s inside the course
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