Скачать или смотреть Machine Learning Full Course For Beginners 2025 Part 1| Free Machine Learning Course For Beginners

Machine Learning Full Course For Beginners 2025 Part 1| Free Machine Learning Course For Beginners
✅Datasets and Source Code: https://forms.gle/UnGdxYMp8K6d7GabA
🔥Liverpool John Moore University MS In Data Science: https://www.upgrad.com/data-science-m...
🔥IIIT Bangalore Post Graduate Program in Data Science & AI: https://www.upgrad.com/data-science-p...
🔥Master of Science in Machine Learning & AI: https://www.upgrad.com/masters-in-ml-...
🔥Post Graduate Certificate in Machine Learning and Deep Learning: https://www.upgrad.com/machine-learni...

Hello and welcome back to upGrad's YouTube Channel! We're excited to introduce our free machine learning full course designed specifically for beginners in 2025. In this comprehensive machine learning tutorial video, our experienced mentor will provide in-depth insights into machine learning topics, including:
Fundamentals of machine learning
Applications and key concepts
Foundational models

This online machine learning tutorial was originally conducted for upGrad students and has been refurbished exclusively for upGrad YouTube subscribers and a wider audience.

Topics Covered:
00:00:50 - Introduction to Machine Learning Full Course For Beginners
00:02:00 - Agenda For Machine Learning Full Course
00:02:53 - What is Data Analysis?
00:04:40 - Why go beyond Data Analysis
00:07:12 - Learning Concept on Machine Learning
00:10:50 - Example on Learning Concept
00:12:24 - The Aim of Machine Learning
00:13:15 - Why Machine Learning?
00:15:05 - Comparison Between Human Brain and Computer
00:16:00 - What is Motivations?
00:19:20 - What is Automation?
00:26:10 - What is Precision?
00:28:22 - What is Current Landscape?
00:29:00 - Difference b/w DS,ML,AI
00:35:59 - What is Machine Learning Paradigms
00:36:20 - The Machine Learning Pipeline
00:39:12 - What is ML Paradigms ?
00:51:08 - What is Supervised Learning
01:00:00 - Types of ML Tasks
01:14:47 - Why Consider Linesr Regression?
01:26:02 - Why Not use Correlation?
01:26:50 - What is Linear Regression Model?
01:27:55 - Simple Linear Regression Explained
01:42:51 - Dataset and Proposed Model Equation
01:44:27 - What is Best Fit Line
01:52:00 - Which Model Is Better?
02:01:41 - Optimization Methods
02:32:40 - What is Model Evaluation (Goodness of Fit)
02:31:10 - What is Model Evaluation (Predictive Power)
02:36:19 - MSE and RMSE (RED Model)
02:39:52 - Examples on Basic Data Processing
03:01:00 - Example on Exploratory Data Analysis
03:23:08 - Example on Numerical Predictors -Sklearn
03:44:10 - Multiple Linear Regression Model
04:10:20 - Introduction to Multiple Variables
04:21:29 - What is Variance Inflation Factor?
04:35:40 - Hands on Projects on ML Model
05:36:56 - Regularization and Hyperparameter Tuning
05:43:20 - What is ML Model Inference?
05:47:16 - How to Improve model performance
05:48:20 - What is Bias and Variance
05:58:34 - Solving Models using Linear Combination
05:59:44 - What is Feature Scaling
06:00:40 - What is Multiple Linear Regression Model
06:04:09 - Feature Scaling Methods
06:04:50 - What is Data Leakage?
06:13:16 - What is Regularization?
06:19:36 - What is Model Inference?
06:22:42 - What is Effect of Scaling?
06:36:35 - What is Hyperparameter Tuning?
06:38:12 - Parameters Vs Hyperparameters
06:46:00 - Hyperparameters Tuning Methods
06:50:00 - Example on Setup and Data Preparation
07:09:30 - Example on Multiple Linear Regression Model
07:20:00 - Example on Ridge Regression Model
07:23:13 - Example on LASSO Regression Model
07:25:03 - Example on Hyperparameter Tuning: Ridge Model
07:30:02 - Example on Hyperparameter Tuning: LASSO Model
07:35:40 - Example on Optimal Models

#machinelearning #machinelearningfullcourse #machinelearningtutorialforbeginners

If you're feeling a little overwhelmed, don't worry! Re-watch this video, experiment with the concepts, and join me in the comments with any questions.

The dataset and code script used in this tutorial are added in the description for you to check out.

Let’s keep up our progress and build our knowledge step-by-step!

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