Complete Machine Learning with Python

About this course

This course contains the use of artificial intelligence.

The "Complete Machine Learning with Python" course is a comprehensive program designed to build a strong foundation in machine learning concepts, algorithms, and predictive modeling using Python. Whether you're a beginner or an aspiring data scientist, this course helps you understand both the theory and practical implementation of core machine learning techniques.

You'll begin by learning the fundamentals of Machine Learning, Data Science, and Deep Learning, followed by an introduction to different types of machine learning tasks, including supervised, unsupervised, and reinforcement learning. As you progress, you'll understand data patterns, curve fitting, overfitting, underfitting, and the mathematical intuition behind machine learning models.

Course Highlights:

Introduction to Machine Learning

Types of Machine Learning

Data Patterns and Curve FittingOverfitting vs Underfitting

Linear Regression Fundamentals

Cost Function and Gradient DescentTrain-Test Split and Model Evaluation

The course then explores some of the most widely used machine learning algorithms. You'll learn how Linear Regression and Logistic Regression work, understand ROC and AUC evaluation metrics, and implement practical use cases using real-world datasets. You'll also gain hands-on experience with Decision Trees, Random Forest, Naive Bayes, K-Nearest Neighbors (KNN), and Support Vector Machines (SVM), understanding when and why each algorithm is used.

Machine Learning Algorithms Covered:

Linear Regression

Logistic Regression

Decision TreeRandom ForestNaive BayesK-Nearest Neighbors (KNN)Support Vector Machine (SVM)Gradient Descent and Regularization

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