AI Engineering with Generative AI (Batch-01)

Wishlist Share
Share Course
Page Link
Share On Social Media

About Course

AI Engineering Specialist – Comprehensive Course

 

The AI Engineering Specialist course is a meticulously designed, in-depth program that equips learners with the essential skills and knowledge required to master Artificial Intelligence, Machine Learning, and Deep Learning. This course takes a structured approach, starting from Python programming fundamentals and advancing through data science, mathematical foundations, machine learning algorithms, deep learning frameworks, and real-world AI applications.

 

The journey begins with Python programming basics, where students gain proficiency in writing efficient Python code, handling data structures, working with files, and structuring their code using functions and modules. This foundational knowledge paves the way for Data Science Essentials, covering NumPy, Pandas, data cleaning, aggregation, and visualization techniques to prepare learners for real-world data manipulation and exploration. The curriculum also includes Mathematics for Machine Learning, ensuring a solid grasp of linear algebra, calculus, probability, and statistics, which are crucial for building AI models.

 

As students progress, they delve into Probability and Statistics for Machine Learning, covering probability theory, hypothesis testing, statistical inference, and regression analysis. The course then transitions into Machine Learning, introducing supervised learning, regression models, classification techniques, model evaluation, and advanced algorithms like k-Nearest Neighbors (k-NN). Feature engineering, data preprocessing, and hyperparameter tuning are also explored in-depth to enhance model performance.

 

A significant portion of the program is dedicated to Advanced Machine Learning and Model Optimization, where learners explore ensemble learning techniques like bagging, boosting, XGBoost, and LightGBM. Additionally, they will master cross-validation, hyperparameter tuning, and automated optimization methods. The course then shifts focus to Neural Networks and Deep Learning, covering essential concepts such as forward propagation, activation functions, backpropagation, and optimization techniques. Learners gain hands-on experience with frameworks like TensorFlow, Keras, and PyTorch, enabling them to build deep learning models for various applications.

 

Specialized topics such as Convolutional Neural Networks (CNNs) for image recognition, Recurrent Neural Networks (RNNs) for sequence modeling, and Transformers and Attention Mechanisms for natural language processing are also covered. Students will work on projects involving text generation, image classification, sentiment analysis, and language modeling. Additionally, the course includes Transfer Learning and Fine-Tuning, where learners leverage pre-trained models to enhance AI applications across domains.

 

The curriculum further expands into AI Model Deployment and Production, including TensorFlow Serving, TFX pipelines, model validation, and scaling AI solutions using Kubernetes. Learners will also explore LangChain for Language Models, implementing projects like chatbots, sentiment analysis tools, and document retrieval systems. The course concludes with hands-on projects in Computer Vision, Natural Language Processing, Recommender Systems, Generative Adversarial Networks (GANs), and Reinforcement Learning, ensuring that students are industry-ready with real-world AI expertise.

 

By the end of this program, participants will possess a comprehensive understanding of AI Engineering, from fundamental programming to advanced deep learning architectures and model deployment strategies. This course is ideal for aspiring AI engineers, data scientists, and professionals seeking to gain cutting-edge skills in artificial intelligence and machine learning. Whether you are a beginner or an experienced practitioner, this course will provide the tools, techniques, and hands-on experience needed to thrive in the AI-driven world.

Show More

Course Content

Introductory Class

  • AI Engineering (Batch-01) – (Introductory Class)
    01:53:09

Class 01 – AI Engineering Overview | Anaconda IDE Installation | Python Basics (Print, Comments, Variables, Data Types)

Class 02 – Python Data Types | Escape Sequences | Built-in Functions | String Indexing & Slicing | Data Engineer Skills Discussion

Class 03 – Python Data Types | Built-in Functions (Lists, Sets, Tuples, Dicts, Booleans) | Comparison Operators | Conditional Statements

Class 04 – Python Loops | For & While Loops | Loop Controls (Break, Continue, Pass) | Mini Projects (Even/Odd, Guessing Game)

Class 05 – Loops & Conditionals (Mini Projects) | User-Defined Functions (Reverse, Factorial, Count Types) | Lambda with filter(), map() | Intro to AI

Class 06 – Python Libraries Intro | NumPy (Arrays, rand(), zeros(), ones(), reshape()) | Indexing & Slicing (2D/3D) | Intro to Pandas

Class 07 – Pandas & EDA | Visuals: Bar, Histogram, KDE, Scatter, Box | Project 1: Roller Coaster Dataset | Missing Values, Correlation, Heatmap | SQL Server Connection (pyodbc)

Class 08 – Project 2: Laptop Price EDA | Automated EDA Tools: ydata_profiling, autoviz, sweetviz, dtale | PDF Extraction with pdfplumber

Class 09 – Real-World Python Projects | PDF Handling | ML Types Overview

Class 10 – Real-World Python Projects: PDF Tasks | QR Code Generator | Email Sender App

Class 11 – Python Projects: Location Finder | Voice To-Do App | Audio Transcriber (EN–UR) | Features & Encoding Basics

Class 12 – ML Overview: Clustering | Association | Supervised Learning (Linear & Logistic Regression)

Class 13 – Support Vector Machines (SVM) Basics & Implementation | Feature Scaling (StandardScaler) | Model Evaluation Metrics

Class 14 – Confusion Matrix Explained | Precision vs Recall | F1 Score | Decision Tree Theory (Entropy & Info Gain) | Intro to Random Forest

Class 15 – Decision Tree & Random Forest | Classification & Regression | Overfitting vs Underfitting | Bias-Variance Tradeoff | Generalization Techniques

Class 16 – ML Algorithm Selection Flowchart | KNN: Concept, Metrics & Implementation | K-Means Clustering: Concept, Elbow Method & Implementation

Class 17 – Association Rule Learning & Apriori Algorithm | Support, Confidence, Lift | Market Basket Analysis | Pros & Cons of Apriori

Class 18 – FP-Growth for Association Rules | Market Basket Analysis | Intro to DBSCAN & HDBSCAN | Density-Based Clustering & Epsilon Selection | Implementation & Evaluation

Class 19 – SQL Basics: IN | WHERE | LIKE | BETWEEN | DML (INSERT, UPDATE, DELETE) | Joins

Class 20 – SQL Joins (Advanced) | SQL Views: Concept & Use Cases | Data Science Career Talk | Naive Bayes: Concept | Types (Gaussian | Multinomial | Bernoulli) | Practical Use Cases

Class 21 – Ensemble Learning: Bagging | Boosting | Stacking | Why Ensembles? | Real-Life Examples | Benefits vs Single Models

Class 22 – Handling Class Imbalance: SMOTE & Undersampling | Oversampling vs Undersampling | ROC Curve & AUC – Concept and Evaluation

Class 23 – Class Imbalance Techniques: SMOTE, Tomek Links, ADASYN | Naive Bayes (Gaussian, Multinomial, Bernoulli) – Implementation & Use Cases | Model Evaluation with Confusion Matrix & Accuracy

Class 24 – Ensemble Techniques (Bagging, Boosting, Stacking, Voting) | Comparative Analysis with Base Learners | Real-World Use Cases | Intro to NLP: Tokenization, Stopword Removal, Lemmatization, Sentiment Analysis | Text Preprocessing for ML

Class 25 – Deep Learning Intro Deep Learning vs Machine Learning | Basics of ANN and Its Working

Class 26 – Data Science Foundations | Career Roadmap | Virtual Environments | Intro to Cloud & Docker

Class 27 – Intro to Deep Learning & CNNs | Convolution Concepts: Filters, Stride, Padding, Feature Maps

Class 28 – CNNs (Part 2): Pooling Layers, Activation Functions | Intro to Perceptrons (Single & Multi-Layer)

Class 29 – RNNs (Part 1): Intro, Architecture, Working Mechanism | Sequential Data Use Cases

Class 30 – RNNs (Part 2) | LSTM (Long Short-Term Memory) | Bidirectional LSTM | Applications

Class 31 – Deep Learning Applications | Hand Gesture Detection with CNN & MediaPipe | Glasses Detection Model with CNN

Class 32 – CNN Implementation | Face Mask Detection Model | Custom Annotation Tool for Image Labeling

Class 33 – RNN Implementation – Built RNN Model from Scratch for Next Word Prediction (e.g., Messaging App) | Introduction to Transformer Neural Networks (Part 1)

Class 34 – Transformers Neural Network (Part 2) – Encoding & Decoding | Self-Attention Mechanism | Generative AI Overview

Class 35 – Prompt Engineering – Maximizing Output from ChatGPT & Other AI Tools | Frameworks: RACE, CREATE, TAG, etc.

Class 36 – Generative AI with TensorFlow – Implemented FLAN-T5 & MiniLM-L6-V2 Embeddings | Built First PDF QnA Chatbot | How LLMs Work Explained

Class 37 – Python UI – Streamlit Intro | System Setup | First GUI App Development

Class 38 – Python UI – Chatbot Using Static Q&A and Wikipedia | Streamlit Integration | Class Activity & Presentation

Class 39 – Generative AI with Hugging Face Overview | Mistral AI Model | Conversational App Development with Inference Client & Streamlit

Class 40 – Generative AI: Text-to-Image & API Models | Text-to-Image with Hugging Face | Groq App for Marketing & Job Posters | Conversational Bot with DeepSeek API | API-Based vs Offline Models

Class 41 – Generative AI – RAG (Retrieval-Augmented Generation) Overview | Building RAG-Based Chatbot Using GROQ API & FAISS | Vector Embeddings & Database Integration | Use Cases Discussion

Class 42 – Complete Recap of 41 Classes | Technology & Project Highlights | MLOps Overview & Real-World Application Insights

Class 43 –

Class 44 –