The Ultimate Supervised Learning For Data Science Course
Published 6/2025
Created by Nash J
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All | Genre: eLearning | Language: English | Duration: 231 Lectures ( 15h 42m ) | Size: 8.4 GB
Master the most popular supervised learning algorithms through hands-on Kaggle case studies solving real-world problems!
What you'll learn
Logical and practical knowledge of popular Machine Learning algorithms.
Learn to tackle real-world problems with Classification and Regression tasks. This course prepares you for top-tier performance in Machine Learning.
Hands-on case studies, handpicked to guide you from basics to advanced, with every line of code explained in detail.
This course aligns with industry practices, ensuring what you learn remains relevant for real-world implementation.
Requirements
Basic knowledge of Python is helpful, but we have included dedicated optional section to cover all the essentials you need.
Description
Master Supervised Machine Learning with Real-World Case Studies!This hands-on course is your complete guide to Supervised Machine Learning, designed to take you from beginner to confident practitioner. Learn and implement popular algorithms including Linear Regression, Logistic Regression, Linear Discriminant Analysis, Decision Trees, Random Forest, K-Nearest Neighbors (KNN), Naive Bayes, Support Vector Machines (SVM), and powerful Boosting algorithms like AdaBoost, Gradient Boosting, and XGBoost.Each algorithm is thoroughly explained using clear, impressive visualizations that bring concepts to life, along with a deep dive into the mathematical intuition and working logic behind the models-ensuring both visual clarity and theoretical depth for a complete, well-rounded understanding.What sets this course apart? Real case studies from Kaggle-not just toy datasets! You'll gain practical experience solving actual classification and regression problems using Python and essential libraries like NumPy, Pandas, Scikit-learn, and Seaborn.You'll also learn critical ML concepts like bias-variance tradeoff, model tuning, overfitting vs underfitting, confusion matrix, ROC-AUC, and cross-validation, helping you build models that are both accurate and robust.Whether you're aiming for a career in Data Science, AI, or Analytics, this course equips you with the skills employers look for. No prior ML experience required-just curiosity and basic Python.Enroll now and take your machine learning journey from theory to real-world mastery!
Who this course is for
Individuals with little to no prior knowledge of supervised learning who want a structured learning road-map.
Data Enthusiasts: Aspiring data scientists and analysts eager to master classification and regression techniques.
Working Professionals: Professionals looking to upskill or transition into data science roles by gaining a strong foundation in supervised learning concepts and applications.
Students and Academics: Learners in academic settings seeking practical insights and hands-on experience to complement their theoretical knowledge.
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