The Complete AI Engineering Roadmap: From Python to Production-Ready Generative AI & MLOps

What you will learn:

Master advanced Python programming from fundamental data types to complex file operations, building a robust foundation essential for all AI and machine learning endeavors.
Gain expertise in data science methodologies, utilizing powerful libraries like NumPy, Pandas, Matplotlib, and Seaborn for comprehensive data cleaning, insightful visualization, and in-depth analysis to extract valuable insights.
Develop, train, and rigorously evaluate a wide array of machine learning models using Scikit-learn, encompassing diverse regression, classification, and sophisticated ensemble methods, with a strong focus on model optimization techniques.
Architect and implement state-of-the-art deep learning models with TensorFlow and PyTorch, including advanced Convolutional Neural Networks (CNNs) for image processing and Recurrent Neural Networks (RNNs) and LSTMs for complex sequence data tasks.
Construct end-to-end MLOps pipelines incorporating Git, DVC, Docker, MLflow, and CI/CD strategies to streamline model versioning, packaging, deployment, and monitoring across major cloud platforms like AWS, GCP, and Azure.
Engineer cutting-edge Generative AI and Large Language Model (LLM) applications using leading APIs such as OpenAI GPT, Claude, and Gemini, integrating Retrieval-Augmented Generation (RAG) pipelines and custom fine-tuning for specialized tasks.

Description

Embark on an unparalleled journey into the world of Artificial Intelligence with our most comprehensive program, “The Complete AI Engineering Roadmap.” This immersive curriculum is meticulously crafted to transform aspiring professionals into accomplished, production-ready AI Engineers, equipped to tackle the demands of the modern tech landscape. Delve deep into every critical layer of the AI engineering ecosystem, starting from foundational Python programming and advanced data science principles, through cutting-edge machine learning and deep learning methodologies, robust MLOps practices, and the revolutionary field of Generative AI with Large Language Models (LLMs).

This program serves as your definitive guide to securing a prominent role as a Full-Stack AI Engineer. You will acquire the expertise to ideate, develop, train, seamlessly deploy, and efficiently scale intricate AI models across diverse real-world scenarios. Our hands-on approach ensures practical mastery, utilizing industry-standard tools and frameworks such as NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch, Docker, Git, MLflow, LangChain, and FastAPI. By engaging with these essential AI technologies, you'll gain practical experience mirroring the workflows of leading technology firms.

Your educational voyage commences with an in-depth exploration of Python for Data Science. You'll solidify your understanding of essential programming constructs including control flow, functions, sophisticated data structures, and efficient file handling. Subsequently, the course transitions into comprehensive data analysis and compelling data visualization using powerful libraries like Matplotlib, Seaborn, and Pandas. Here, you will cultivate robust data skills encompassing advanced data cleaning, intricate feature engineering, and rigorous statistical modeling, enabling you to adeptly manipulate vast datasets and prepare them for complex machine learning pipelines.

The subsequent core phase of the curriculum is dedicated to Machine Learning (ML). Here, you will thoroughly investigate both supervised and unsupervised learning paradigms, mastering classification, regression, and sophisticated ensemble techniques. Crucially, you’ll gain proficiency in model evaluation strategies to ensure robust performance. Practical implementation includes a wide array of algorithms such as linear and logistic regression, decision trees, random forests, and advanced boosting methods like XGBoost, LightGBM, and CatBoost. Each theoretical concept is reinforced through immersive, hands-on ML projects, bridging the gap between academic understanding and practical application.

Following your mastery of ML, you will transition to advanced Deep Learning (DL). This module focuses on constructing and training sophisticated neural networks using leading frameworks, TensorFlow and PyTorch. You'll gain a profound understanding of fundamental concepts such as forward propagation, backpropagation, diverse activation functions, various loss functions, and advanced gradient descent optimization algorithms. The curriculum guides you through building Convolutional Neural Networks (CNNs) for high-accuracy image classification and Recurrent Neural Networks (RNNs), LSTMs, and GRUs for complex sequence modeling tasks. By the culmination of this module, you will have developed and deployed multiple deep learning models on authentic datasets.

The course then delves into the critical domain of MLOps (Machine Learning Operations), an indispensable skill set for deploying and managing robust AI systems in live production environments. You will learn best practices for version control with Git and DVC, efficient model packaging using ONNX and TorchScript, robust API serving with Flask and FastAPI, and scalable cloud deployment strategies across AWS, GCP, and Azure. Furthermore, you’ll gain expertise in automating model pipelines through Continuous Integration/Continuous Deployment (CI/CD) tools, guaranteeing that your AI models are consistently reliable, infinitely scalable, and optimized for enterprise-level deployment.

Finally, immerse yourself in the cutting-edge fields of Generative AI (GenAI) and Large Language Models (LLMs). This module covers advanced prompt engineering, tokenization techniques, fine-tuning custom models, implementing retrieval-augmented generation (RAG) pipelines, and exploring sophisticated AI agent frameworks such as LangChain and CrewAI. You’ll develop practical LLM applications utilizing popular APIs like OpenAI GPT, Claude, and Gemini, culminating in a significant capstone project where you design and implement your own intelligent AI chatbot or advanced content generation system.

Upon the successful completion of this rigorous course, you will command a comprehensive technical skill set, enabling you to thrive as a Full-Stack AI Engineer. You will possess an end-to-end understanding and practical proficiency across data science, machine learning, deep learning, MLOps, and Generative AI. Whether you are initiating your career in AI or aspiring to elevate into advanced engineering leadership roles, this program furnishes you with the essential skills, powerful tools, and an impressive portfolio to actively shape the future of Artificial Intelligence.

Curriculum

Introduction to the Course

This introductory section provides a comprehensive overview of the Full-Stack AI Engineer program. It begins with an 'Introduction to Full-Stack AI Engineer: Python, ML, Deep Learning & GenAI' to set the stage, followed by '50 Essential AI Concepts: A Comprehensive Journey' to build foundational knowledge. A 'Quick Quiz with Answers' helps consolidate learning, and 'Resources for the Course - Slides and Code Files' ensures students have all necessary materials.

Week 1: Python Programming Basics

Week 1 is dedicated to establishing a strong foundation in Python programming for AI. It starts with an 'Introduction to Week 1 Python Programming Basics' and covers 'Day 1: Introduction to Python and Development Setup'. Subsequent days delve into core concepts like 'Control Flow in Python' (Day 2), 'Functions and Modules' (Day 3), and mastering 'Data Structures (Lists, Tuples, Dictionaries, Sets)' on Day 4. 'Working with Strings' (Day 5) and 'File Handling' (Day 6) are also covered, culminating in 'Pythonic Code and Project Work' on Day 7, where learners apply their new skills.

Week 2: Data Science Essentials

This week focuses on the essential tools and techniques for data science. After an 'Introduction to Week 2 Data Science Essentials', learners will dive into 'Day 1: Introduction to NumPy for Numerical Computing' and 'Day 2: Advanced NumPy Operations'. 'Day 3: Introduction to Pandas for Data Manipulation' is followed by practical skills in 'Data Cleaning and Preparation with Pandas' (Day 4) and 'Data Aggregation and Grouping in Pandas' (Day 5). The week concludes with 'Data Visualization with Matplotlib and Seaborn' (Day 6) and an 'Exploratory Data Analysis (EDA) Project' (Day 7) to apply these skills.

Week 3: Mathematics for Machine Learning

Week 3 builds the crucial mathematical backbone for machine learning. Following an 'Introduction to Week 3 Mathematics for Machine Learning', the curriculum covers 'Day 1: Linear Algebra Fundamentals' and 'Day 2: Advanced Linear Algebra Concepts'. Learners then explore 'Day 3: Calculus for Machine Learning (Derivatives)' and 'Day 4: Calculus for Machine Learning (Integrals and Optimization)'. The week also includes 'Day 5: Probability Theory and Distributions' and 'Day 6: Statistics Fundamentals', culminating in a 'Math-Driven Mini Project – Linear Regression from Scratch' on Day 7 to solidify understanding.

Week 4: Probability and Statistics for Machine Learning

Building on the mathematical foundations, Week 4 specifically targets probability and statistics for ML. It begins with an 'Introduction to Week 4 Probability and Statistics for Machine Learning', then moves to 'Day 1: Probability Theory and Random Variables' and 'Day 2: Probability Distributions in Machine Learning'. 'Day 3: Statistical Inference - Estimation and Confidence Intervals' and 'Day 4: Hypothesis Testing and P-Values' provide core statistical concepts. 'Day 5: Types of Hypothesis Tests' and 'Day 6: Correlation and Regression Analysis' deepen the understanding, concluding with a 'Statistical Analysis Project – Analyzing Real-World Data' on Day 7.

Week 5: Introduction to Machine Learning

This week provides a foundational introduction to machine learning. After an 'Introduction to Week 5 Introduction to Machine Learning', students learn 'Day 1: Machine Learning Basics and Terminology'. 'Day 2: Introduction to Supervised Learning and Regression Models' is followed by 'Day 3: Advanced Regression Models – Polynomial Regression and Regularization'. The curriculum then covers 'Day 4: Introduction to Classification and Logistic Regression', 'Day 5: Model Evaluation and Cross-Validation', and 'Day 6: k-Nearest Neighbors (k-NN) Algorithm', finishing with a practical 'Supervised Learning Mini Project' on Day 7.

Week 6: Feature Engineering and Model Evaluation

Week 6 hones in on crucial pre-processing and evaluation techniques. Starting with an 'Introduction to Week 6 Feature Engineering and Model Evaluation', it explores 'Day 1: Introduction to Feature Engineering'. Subsequent lectures cover 'Day 2: Data Scaling and Normalization', 'Day 3: Encoding Categorical Variables', and 'Day 4: Feature Selection Techniques'. Learners then practice 'Creating and Transforming Features' (Day 5), followed by advanced 'Model Evaluation Techniques' (Day 6) and 'Cross-Validation and Hyperparameter Tuning' (Day 7) to build robust models.

Week 7: Advanced Machine Learning Algorithms

This section delves into more complex and powerful machine learning algorithms. Following an 'Introduction to Week 7 Advanced Machine Learning Algorithms', students explore 'Day 1: Introduction to Ensemble Learning'. 'Day 2: Bagging and Random Forests' and 'Day 3: Boosting and Gradient Boosting' introduce key ensemble methods. Days 4 and 5 focus on specific algorithms: 'Introduction to XGBoost' and 'LightGBM and CatBoost'. 'Day 6: Handling Imbalanced Data' addresses practical challenges, concluding with an 'Ensemble Learning Project – Comparing Models on a Real Dataset' on Day 7.

Week 8: Model Tuning and Optimization

Week 8 is dedicated to optimizing machine learning models for peak performance. After an 'Introduction to Week 8 Model Tuning and Optimization', the curriculum covers 'Day 1: Introduction to Hyperparameter Tuning', 'Day 2: Grid Search and Random Search', and 'Day 3: Advanced Hyperparameter Tuning with Bayesian Optimization'. Learners also study 'Day 4: Regularization Techniques for Model Optimization' and 'Day 5: Cross-Validation and Model Evaluation Techniques'. The week culminates with 'Day 6: Automated Hyperparameter Tuning with GridSearchCV and RandomizedSearchCV' and an 'Optimization Project – Building and Tuning a Final Model' on Day 7.

Week 9: Neural Networks and Deep Learning Fundamentals

This week introduces the exciting world of deep learning. It begins with an 'Introduction to Week 9 Neural Networks and Deep Learning Fundamentals' and 'Day 1: Introduction to Deep Learning and Neural Networks'. Key concepts like 'Forward Propagation and Activation Functions' (Day 2) and 'Loss Functions and Backpropagation' (Day 3) are explored, alongside 'Gradient Descent and Optimization Techniques' (Day 4). Practical sessions include 'Building Neural Networks with TensorFlow and Keras' (Day 5) and 'Building Neural Networks with PyTorch' (Day 6), ending with a 'Neural Network Project – Image Classification on CIFAR-10' on Day 7.

Week 10: Convolutional Neural Networks (CNNs)

Week 10 focuses specifically on Convolutional Neural Networks, vital for computer vision. After an 'Introduction to Week 10 Convolutional Neural Networks (CNNs)', the curriculum covers 'Day 1: Introduction to Convolutional Neural Networks', 'Day 2: Convolutional Layers and Filters', and 'Day 3: Pooling Layers and Dimensionality Reduction'. Hands-on experience includes 'Building CNN Architectures with Keras and TensorFlow' (Day 4) and 'Building CNN Architectures with PyTorch' (Day 5). 'Day 6: Regularization and Data Augmentation for CNNs' addresses optimization, concluding with a 'CNN Project – Image Classification on Fashion MNIST or CIFAR-10' on Day 7.

Week 11: Recurrent Neural Networks (RNNs) and Sequence Modeling

This week is dedicated to understanding and applying Recurrent Neural Networks for sequential data. It starts with an 'Introduction to Week 11 Recurrent Neural Networks (RNNs) and Sequence Modeling' and 'Day 1: Introduction to Sequence Modeling and RNNs'. Learners then dive into 'Day 2: Understanding RNN Architecture and Backpropagation Through Time (BPTT)', 'Day 3: Long Short-Term Memory (LSTM) Networks', and 'Day 4: Gated Recurrent Units (GRUs)'. 'Day 5: Text Preprocessing and Word Embeddings for RNNs' prepares for natural language tasks, followed by 'Day 6: Sequence-to-Sequence Models and Applications'. The week ends with an 'RNN Project – Text Generation or Sentiment Analysis' on Day 7.

Week 12: Transformers and Attention Mechanisms

Week 12 explores the revolutionary Transformer architecture. Following an 'Introduction to Week 12 Transformers and Attention Mechanisms', the curriculum covers 'Day 1: Introduction to Attention Mechanisms' and 'Day 2: Introduction to Transformers Architecture'. Deep dives into 'Day 3: Self-Attention and Multi-Head Attention in Transformers' and 'Day 4: Positional Encoding and Feed-Forward Networks' are provided. Practical lessons include 'Day 5: Hands-On with Pre-Trained Transformers – BERT and GPT' and 'Day 6: Advanced Transformers – BERT Variants and GPT-3'. The week culminates in a 'Transformer Project – Text Summarization or Translation' on Day 7.

Week 13: Transfer Learning and Fine-Tuning

This week focuses on advanced techniques for leveraging pre-trained models. After an 'Introduction to Week 13 Transfer Learning and Fine-Tuning', students learn 'Day 1: Introduction to Transfer Learning'. Applications in computer vision are covered in 'Day 2: Transfer Learning in Computer Vision' and 'Day 3: Fine-Tuning Techniques in Computer Vision'. Similarly, NLP applications are addressed in 'Day 4: Transfer Learning in NLP' and 'Day 5: Fine-Tuning Techniques in NLP'. 'Day 6: Domain Adaptation and Transfer Learning Challenges' discusses advanced topics, leading to a 'Transfer Learning Project – Fine-Tuning for a Custom Task' on Day 7.

Week 14: MLOps and Model Deployment

Week 14 introduces the crucial field of MLOps for production-ready AI. Following an 'Introduction to Week 14 MLOps and Model Deployment', learners explore 'Day 1: Introduction to MLOps – Why It Matters'. Practical skills include 'Day 2: Version Control for Data and Models (Git, DVC)', 'Day 3: Model Packaging with Pickle, ONNX, and TorchScript', and 'Day 4: Serving Models with Flask, FastAPI, and Streamlit'. Cloud deployment on 'Day 5: Deploying ML Models on AWS, GCP, and Azure' is covered, alongside 'Day 6: CI/CD Pipelines for Machine Learning Projects'. The week concludes with an 'MLOps Project – Build and Deploy a Model End-to-End' on Day 7.

Week 15: Generative AI and Large Language Model Applications

The final week delves into the cutting-edge of Generative AI and Large Language Models. After an 'Introduction to Week 15 – Generative AI and Large Language Model Applications', students learn 'Day 1: Introduction to Generative AI and LLMs' and 'Day 2: Understanding Prompt Engineering and Tokenization'. Practical development includes 'Day 3: Building Applications with OpenAI, Gemini, and Claude APIs' and 'Day 4: Fine-Tuning and Custom Instruction Models'. 'Day 5: Building RAG (Retrieval-Augmented Generation) Pipelines' and 'Day 6: AI Agents and Autonomous Systems Overview' explore advanced topics, culminating in a 'Generative AI Project – Build Your Own Chatbot or Content Generator' on Day 7.

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