Artificial Intelligence Engineering (2024)

[center]Published 11/2024
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 2.57 GB | Duration: 4h 58m[/center]

Machine Learning | Artificial Intelligence Engineering: From Fundamentals to Deployment

What you'll learn
Understand the intuition of ML algorithms and performing hyperparameter optimization
Understanding of the ML pipeline and its components
Experience with ML and deep learning frameworks
Understanding of and experience in model training, deployment, and operational best practices
Requirements
This course is designed to accommodate learners with varying levels of experience, including beginners. While there are no strict prerequisites, having a basic understanding of programming concepts and familiarity with Python would be beneficial
Interest in Data Science, AI and Machine Learning
Desire to Learn and research
Description
This in-depth course is tailored for individuals aiming to become Machine Learning and AI Engineers. It encompasses the full ML pipeline, from basic principles to sophisticated deployment techniques. Participants will engage in hands-on projects and study real-world scenarios to acquire practical skills in creating, refining, and implementing AI technologies.The Udemy course for Machine Learning and AI Engineering is structured around the roles and responsibilities within the field. It provides a thorough exploration of all essential aspects, such as ML algorithms, the ML pipeline, deep learning frameworks, model training, deployment, and best practices for operations.Organized into 11 comprehensive sections, the course begins with the basics and gradually tackles more complex subjects. Each section is comprised of several lessons, practical projects, and quizzes to solidify the concepts learned.Here are some key features of the course:Comprehensive coverage: The course covers everything from basic math and Python skills to advanced topics like MLOps and large language models.Hands-on projects: Each major section includes a practical project to apply the learned concepts.Industry relevance: The course includes sections on MLOps, deployment, and current trends in AI, preparing students for real-world scenarios.Practical skills: There's a strong focus on practical skills like hyperparameter optimization, model deployment, and performance monitoring.Ethical considerations: The course includes a discussion on AI ethics, an important topic for AI engineers.Capstone project: The course concludes with a multi-week capstone project, allowing students to demonstrate their skills in a comprehensive manner.
Overview
Section 1: Introduction
Lecture 1 Promo video
Lecture 2 Machine Learning and AI Engineering: Course Outline
Lecture 3 The History of Machine Learning
Lecture 4 Introduction to Machine learning | AI Engineering
Lecture 5 The Machine Learning/Artificial Intelligence Pipeline
Lecture 6 Role of an ML/AI Engineer
Section 2: Mathematics for Machine Learning
Lecture 7 Section 1 Completed Wow
Lecture 8 Mathematics for Machine Learning
Lecture 9 Linear Algebra Essentials
Lecture 10 Probability and Statistics Essentials
Lecture 11 Calculus for Optimization
Lecture 12 Extra: Advisory Learning Method
Lecture 13 Extra: Breakdown of Essential Concepts
Section 3: Section 3: Python for Machine Learning
Lecture 14 Completed Section
Lecture 15 Python Basics and Data Structures
Lecture 16 Welcome to Pandas | NumPy | Matplotlib | Seaborn
Lecture 17 NumPy and Pandas for Data Manipulation
Lecture 18 Data Visualization with Matplotlib
Lecture 19 Data Visualization with Seaborn
Lecture 20 Project: Exploratory Data Analysis
Section 4: Machine Learning Algorithms
Lecture 21 Supervised Learning
Lecture 22 Unsupervised Learning
Lecture 23 Ensemble Methods (Random Forests, Gradient Boosting)
Lecture 24 Hyperparameter Optimization
Lecture 25 Extra: Entire End to End Python Project
Section 5: Deep Learning and Neural Networks
Lecture 26 Wow still here ?
Lecture 27 Neural Network Fundamentals
Lecture 28 Convolutional Neural Networks (CNNs)
Lecture 29 Recurrent Neural Networks (RNNs) and LSTMs
Lecture 30 Transfer Learning and Fine-tuning
Section 6: ML/AI Frameworks and Tools
Lecture 31 Section completed !
Lecture 32 Introduction to TensorFlow and Keras
Lecture 33 PyTorch Fundamentals
Lecture 34 Scikit-learn for Traditional ML
Lecture 35 Hugging Face Transformers for NLP
Section 7: The ML Pipeline
Lecture 36 Completed section!
Lecture 37 Data Collection and Preprocessing
Lecture 38 Feature Engineering and Selection
Lecture 39 Model Training and Evaluation
Lecture 40 Model Interpretation and Explainability
Lecture 41 Part 1: Building A Movie Recommendation Model
Section 8: MLOps and Deployment
Lecture 42 Well Done !!!!
Lecture 43 Introduction to MLOps
Lecture 44 Model Versioning and Experiment Tracking
Lecture 45 Containerization with Docker
Lecture 46 Deployment on Cloud Platforms (AWS, GCP, Azure)
Lecture 47 Monitoring and Maintaining ML Models in Production
Section 9: Large Language Models and Foundation Models
Lecture 48 Completed section!
Lecture 49 Introduction to LLMs and Foundation Models
Lecture 50 Fine-tuning Pre-trained Models
Lecture 51 Prompt Engineering and Few-shot Learning
Lecture 52 Extra: Transformers
Lecture 53 Ethical Considerations in AI
Lecture 54 Reinforcement Learning
Section 10: Section 10: Capstone Project
Lecture 55 Design, implement, and deploy an end-to-end AI solution
Lecture 56 Completed Course !!!
Students and Professionals,Beginners in AI, Machine learning and Data Science,Self-Learners and Lifelong Learners,Professionals Seeking Career Advancement

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