AI And Machine Learning Full Course | AI And Machine Learning Tutorial For Beginners | Simplilearn
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This video on AI and Machine Learning Full Course by Simplilearn will help you learn the fundamentals of Artificial Intelligence and Machine Learning from beginner to advanced level. The course covers Python programming, data preprocessing, supervised and unsupervised learning, deep learning, neural networks, natural language processing, computer vision, generative AI, and model deployment using industry-standard tools and frameworks. You will also learn how to build, train, evaluate, and optimize AI and machine learning models through hands-on projects and real-world use cases. By the end of this AI and Machine Learning tutorial, you will have a strong understanding of AI concepts and the practical skills needed to develop intelligent, data-driven applications.
Following are the topics covered in the Artificial Intelligence and Machine Learning Full Course 2026:
00:00:00 - Artificial Intelligence and Machine Learning Full Course 2026
00:03:48 - What is AI?
00:10:43 - What is Machine Learning?
01:16:37 - AI With Python Full Course 2026
01:20:01 - Fundamentals of Python For AI
02:04:20 - Why Python dominates AI
02:12:18 - Development environments overview
03:21:40 - Google Colab setup
03:46:34 - Python syntax fundamentals
04:10:04 - Print input and comments
04:57:17 - Variables and data types
05:42:47 - Operators and comparisons
06:33:23 - Lesson 2 introduction
06:40:18 - List indexing and access
07:01:25 - List slicing explained
07:22:46 - List methods and final Project
08:05:04 - Regression and Classification Basics
08:09:06 - Lesson Objectives and Business Scenario
09:02:07 - Environment Setup and Lesson Files
09:12:54 - Linear Regression Theory
10:20:32 - Polynomial Regression and Recap
10:29:36 - Parameters and Hyperparameters
11:01:59 - Data Leakage and Data Preprocessing
11:06:07 - Regularization for Overfitting Control
12:24:37 - Classification Foundations
12:52:13 - Logistic Regression Theory
13:15:25 - Logistic Regression Practical Example
13:43:56 - Naive Bayes Classifier
14:00:55 - K-Nearest Neighbors (KNN) Classifier
14:08:54 - Hyperparameter tuning
15:38:21 - bike rental regression Machine learning project
16:19:50 - Python Basics For Machine Learning
16:19:50 - Machine Learning With Python Full Course 2025
17:06:42 - Decision Tree
17:43:15 - Clustering
18:37:53 - Data and its types
19:49:12 - Probability
20:27:43 - Multiple Linear Regression
21:05:45 - Confusion Matrices
22:19:44 - KNN
22:43:30 - Support Vector Machine
23:34:30 - Principle Component Analysis
24:12:51 - Corona Virus Analysis
24:20:37 - Python For Machine Learning
25:03:23 - Semi-Supervised Learning Overview
25:10:04 - Reinforcement Learning Fundamentals
25:23:19 - Python Packages for Machine Learning
26:09:35 - Simple and Multiple Regression
26:23:57 - Preparing Data for Regression
27:04:27 - Overfitting and Underfitting
27:12:29 - Recap, Polynomial Regression, and Metrics
28:48:29 - Regularization Overview
28:56:43 - Lasso Regression Theory
29:10:44 - Lasso Regression in Code
30:04:23 - Ridge Regression Recap
30:32:45 - Hyperparameter Tuning Concepts
31:05:18 - Pipelines and Preprocessing Automation
31:59:51 - Supervised Vs Unsupervised vs Reinforced Learning
32:09:01 - Supervised & unsupervised
32:17:55 - Linear & Logistic Regression
34:30:30 - Naive Bayes and BVM
35:37:06 - K nearest Neighbors
36:03:31 - K Means Clustering
36:52:16 - PCA and regulation
37:38:10 - Probability And Statistics
38:48:19 - R Squared Error
39:14:26 - Deep Learning Interview Questions
40:19:13 - Mathematics for machine learning
47:13:34 - Machine Learning Interview Questions 2026
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