Complete Machine Learning with Python: Hands-On Masterclass
Published 7/2026
Created by Tutorac Inc
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Intermediate | Genre: eLearning | Language: English | Duration: 60 Lectures ( 22h 49m ) | Size: 28.6 GB

Master Machine Learning fundamentals, Linear & Logistic Regression, Decision Trees, Random Forest, Naive Bayes, KNN, SVM

What you'll learn
⚡ Understand Machine Learning fundamentals and different types of Machine Learning.
⚡ Build Linear Regression and Logistic Regression models from scratch.
⚡ Learn Gradient Descent, Cost Functions, Curve Fitting, and Regularization techniques.
⚡ Evaluate models using Train-Test Split, R², ROC Curve, and AUC metrics.
⚡ Implement Decision Tree, Random Forest, Naive Bayes, KNN, and SVM algorithms.

Requirements
❗ Basic knowledge of Python programming is recommended.
❗ A computer with internet access.
❗ No prior Machine Learning experience is required.
❗ A willingness to learn mathematics and practice building Machine Learning models.

Description
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 Fitting

✨ Overfitting vs Underfitting

✨ Linear Regression Fundamentals

✨ Cost Function and Gradient Descent

✨ Train-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 Tree

✨ Random Forest

✨ Naive Bayes

✨ K-Nearest Neighbors (KNN)

✨ Support Vector Machine (SVM)

✨ Gradient Descent and Regularization

Throughout the course, you'll work through practical examples and real-world use cases that strengthen your understanding of machine learning concepts and model building.

By the end of this course, you'll have a solid understanding of the core machine learning algorithms, model evaluation techniques, and practical implementation strategies required to solve real-world prediction and classification problems. Join this course to build a strong Machine Learning foundation and take the first step toward a career in Data Science.

Who this course is for
⭐ Python Beginners who want to learn Machine Learning from scratch.
⭐ Students preparing for Machine Learning and Data Science careers.
⭐ Data Analysts who want to build predictive models.
⭐ Anyone interested in understanding core Machine Learning algorithms and real-world applications.

Homepage

https://anonymz.com/?https://www.udemy.com/course/complete-machine-learning-with-python

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