Artificial Intelligence Engineering

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