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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 ✅Subscribe to our Channel to learn more about the top Technologies: https://bit.ly/2VT4WtH ⏩ Check out More Videos In This Category By Simplilearn: https://www.youtube.com/playlist?list=PLEiEAq2VkUULyr_ftxpHB6DumOq1Zz2hq #aiandmachinelearningengineerfullcourse #ai #aiml #aicourse #machinelearningcourse #learnai #mltutorial #aiforbeginners #datascience #deeplearning #aiandml #mltraining #aicareer #simplilearn #2026 👉 Learn More At: https://www.simplilearn.com/professional-aiml-program?utm_campaign=tsQuNWeuiAQ&utm_medium=Description&utm_source=Youtube Key Highlights & Overview: Learning <strong>Ai Ml Course Tutorial</strong> requires clear step-by-step guidance and practical hands-on visual demonstrations. 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