Machine Learning FULL Course with Practical (10 HOURS) | Learn Free ML in 2025 | Part-1

Machine Learning Full Course for Beginners (2025) | Learn Machine Learning in 10 Hours (Part-1) 🔴 To learn Data Analytics Course online with regular LIVE CLASSES, enroll now: https://www.wscubetech.com/landing-pa... 00:00:00 - ML Course Introduction 00:01:03 - What is Machine learning (ML) 00:17:19 - Complete Roadmap To Learn Machine Learning 00:26:16 - Types of Variables in Machine Learning 00:38:24 - Data Cleaning in Machine Learning 00:45:15 - What is missing value and how to find it 01:05:50 - Handling Missing Values (Dropping) 01:15:48 - Handling Missing Values (Imputing category data) 01:27:24 - Handling Missing Values (Scikit-Learn) 01:34:40 - One Hot Encoding & Dummy Variables 01:49:43 - What is Label Encoding? 01:58:16 - What is Ordinal Encoding? 02:12:02 - What is an Outlier and How to Handle It? 02:24:42 - How to Remove Outliers using IQR? 02:37:19 - How to Remove Outliers using Z Score? 02:56:18 - What is Feature Scaling (Standardization)? 03:16:32 - What is Feature Scaling (Normalization)? 03:27:06 - How to Handle Duplicate Data? 03:38:39 - How to Replace and Change Data Types? 03:47:28 - Function Transformer 04:05:17 - Backward Elimination (using MLxtend) & Forward Elimination (using MLxtend) 04:20:26 - Train Test Split in Data Set 04:31:19 - Regression Analysis 04:35:55 - Linear Regression Algorithm (Simple Linear) 04:47:19 - Linear Regression Algorithm (Simple Linear) Practical 05:13:39 - Multiple Linear Regression 05:34:46 - Polynomial Regression 05:55:33 - What is a cost function? 06:02:35 - Regression Cost Function - R Squared score & Adjusted R Squared Regression Analysis 06:18:18 - How to find a Best fit line? 06:32:38 - L1 (Lasso Regularization), L2 (Ridge Regularization) Theory 06:44:32 - L1 (Lasso Regularization), L2 (Ridge Regularization) Practical 07:06:32 - Classification 07:19:42 - Logistic Regression (practical) (Binary Classification) 07:40:51 - Logistic Regression (practical) (Binary Classification) (Multiple input) 07:53:57 - Logistic Regression (practical) (Binary Classification) (Polynomial input) 08:07:23 - Logistic Regression (practical) (Multiclass Classification) 08:23:31 - Confusion Matrix 08:46:47 - Confusion Matrix (Sensitivity, Precision, Recall, F1 – Score) 09:11:16 - Imbalanced dataset 09:40:15 - Naive Bayes 10:02:32 - Naive Bayes (practical) WsCube Tech is a Vernacular Upskilling platform revolutionizing the way you learn and develop your career skills.🚀 WsCube Tech stands out as a leading EdTech platform, offering comprehensive education in Python, Machine Learning, and various Data Science skills. Our approach involves both online and classroom training, featuring hands-on projects delivered practically by seasoned industry experts. With WsCube Tech, you'll gain hands-on skills that make you globally competitive. Our courses are designed to prepare over 100 million career aspirants for the 'Bharat' of the future. 😊 👉 Want to learn and acquire skills in English? Visit WsCube Tech English channel: https://bit.ly/2M3oYOs 📌 𝗩𝗼𝘁𝗲 𝗳𝗼𝗿 𝗼𝘂𝗿 𝗻𝗲𝘅𝘁 𝘃𝗶𝗱𝗲𝗼: https://bit.ly/share-topic-ideas Watch Now our Trending App Development Playlist & Videos: 👉 Learn Python Programming in One Video -    • PYTHON Full Course for Beginners in 16 Hou...   👉 DSA Full Course -    • DSA Full Course with Practical in 9 Hours ...   👉 20 Python Projects in One Video -    • Top 20 Python Projects for Beginners to Ad...   👉 Excel Full Course for Data Analysis -    • Excel Full Course for Data Analysis with P...   For any queries, call us on: +91-7878985501 ✅ CONNECT WITH THE FOUNDER (Mr. Kushagra Bhatia) - 👉 Instagram -   / kushagrabhatiaofficial   👉 LinkedIn -   / kushagra-bhatia   👉 Facebook -   / kushagrawscubetech   Connect with WsCube Tech on social media for the latest offers, promos, job vacancies, and much more: 😄 Facebook:   / wscubetech.india   🐦 Twitter:   / wscubetechindia   📱 Instagram:   / wscubetechindia   👨🏻‍💻 LinkedIn:   / wscubetechindia   🔺 Youtube: http://bit.ly/wscubechannel 🌐 Website: http://wscubetech.com -------------------------------------| Thanks |-------------------------- #machinelearning #mlcourse #machinelearningfullcourse