AI Engineering with Generative AI (Batch-01)

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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.

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Course Content

Introductory Class

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

Class 01 – Introduction to the AI Engineering | Overview of Certification | Installation of Anaconda IDE | Basics of Python (PRINT Statement, Comments) | Introduction to Variables and Data Types

Class 02 – Data Types in Python | Escape Sequences in Python | Built-in Functions for Numbers, Floats, and Strings | String Indexing and Slicing | Discussion: Key Skills of a Data Engineer

Class 03 – Data Types in Python | Built-in Functions: Lists, Sets, Tuples, Dictionaries, and Booleans | Comparison and Chained Comparison Operators | Conditional Statements (IF, ELIF, ELSE)

Class 04 – Loops in Python: FOR LOOPS with Range() | Mini Projects: Even/Odd Checker, Number Guessing Game (with/without attempts) | WHILE LOOPS in Python | Loop Control Statements (Continue, Break, Pass)

Class 05 – Loops and Conditionals (e.g., Pyramids, Largest Number in a List) | User-Defined Functions in Python (Reverse a String, Factorial using reduce(), Count Data Types in a String, etc) | LAMBDA Functions with FILTER() and MAP() | Introduction to Artificial Intelligence (AI)

Class 06 – Introduction to Python Libraries | Exploring NumPy (Creating 1D, 2D, 3D Arrays, and using functions like RAND(), ZEROS(), ONES(), RESHAPE(), etc.) | Indexing and Slicing in 2D and 3D Arrays | Introduction to Pandas

Class 07 – Exploring Pandas | Exploratory Data Analysis (EDA) and Visualization | Visuals: Bar, Histogram, KDE, Scatter, Box Plot | Project 1: Roller Coaster Dataset | Finding Missing Values, Correlation, Heatmap, etc. | Connecting SQL Server to Python using pyodbc

Class 08 – Project 2: EDA on Laptop Price Dataset | Introduction to Automated EDA Libraries: ydata_profiling, autoviz, sweetviz, dtale | Working with PDF files using pdfplumber

Class 09 – Real-World Applications in Python | PDF File Operations with pdfplumber | Sample Real-World Projects | Introduction to Machine Learning Types (Supervised, Unsupervised, Reinforcement Learning)

Class 10 – Real-World Applications in Python | PDF Handling: Reading, Merging, Extracting Images, Adding Watermarks | QR Code Generator | Email Sender App (Basic to Advanced) – Complete Workflow

Class 11 – Real-World Applications in Python | Nearby Location Finder (within 2–3 km radius) | Voice-based To-Do List (Add, Read, Exit Tasks) | Audio Transcriber: Speech to Text (English to Urdu Translation) | Understanding Features (Independent vs Dependent Variables) | Basic Encoding

Class 12 – Overview of Machine Learning Algorithms (Types of ML: Clustering, Association) | Supervised Learning Algorithms (Linear Regression, Logistic Regression)

Class 13 – Support Vector Machines (SVM): Understanding and Implementation | Feature Scaling with StandardScaler | Model Evaluation Metrics (Confusion Matrix, Accuracy, Precision)

Class 14 – Confusion Matrix – Detailed Explanation and Interpretation | Precision vs Recall – Definitions and Use Cases | F1 Score – Balancing Precision and Recall | Theoretical Foundations of Decision Tree Model: Entropy and Information Gain | Introduction to Random Forest – Concept and Advantages Over Single Trees

Class 15 – Implementation of Decision Tree and Random Forest Algorithms | Understanding Tree-Based Models for Classification and Regression | Addressing Overfitting and Underfitting in Machine Learning Models | Bias-Variance Tradeoff – Concept, Visualization, and Impact on Model Performance | Techniques to Improve Generalization in Tree-Based Models

Class 16 – Machine Learning Flowchart Discussion for Algorithm Selection | K-Nearest Neighbors (KNN) – Concept, Distance Metrics, and Use Cases | Implementation of KNN for Classification Tasks | Introduction to K-Means Clustering | Understanding the Working of K-Means Algorithm | Choosing Optimal K Using Elbow Method | Implementation of K-Means for Unsupervised Pattern Detection

Class 17 – Association Rule Learning: Concept and Real-World Applications | Apriori Algorithm – Step-by-Step Explanation | Key Metrics: Support | Confidence | Lift | Implementation of Apriori for Market Basket Analysis | Identifying Strong Association Rules in Transaction Data | Advantages and Limitations of Apriori Algorithm

Class 18 – Unsupervised Learning Techniques | Association Rule Learning Using FP-Growth Algorithm – Concept, Use Cases, and Implementation | Market Basket Analysis with FP-Growth | Introduction to Clustering Algorithms: DBSCAN and HDBSCAN | Understanding Density-Based Clustering | K-Distance Graph for Optimal Epsilon Selection | Implementation and Evaluation of DBSCAN and HDBSCAN Clusters

Class 19 – Introduction to Databases and Their Role in Data Analysis | SQL Basics: Using IN, WHERE, LIKE, and BETWEEN Clauses for Filtering Data | Data Manipulation Language (DML) Commands: INSERT | UPDATE | DELETE | SQL Joins: Understanding INNER JOIN, LEFT JOIN, RIGHT JOIN with Practical Examples | Importance of Joins in Relational Data Retrieval

Class 20 – SQL Joins – Advanced Concepts (Part 2) | Understanding and Creating SQL Views for Data Abstraction | Use Cases of Views in Data Analysis and Reporting | Open Discussion on Real-World Data Science Applications and Career Pathways | Naive Bayes Algorithm – Concept, Assumptions, and Working | Types of Naive Bayes (Gaussian | Multinomial | Bernoulli) | Practical Examples and Use Cases of Naive Bayes in Classification Tasks

Class 21 – Ensemble Learning: Concept and Types | Introduction to Bagging, Boosting, and Stacking | Understanding the Need for Ensemble Methods in Machine Learning | Detailed Explanation of Each Technique with Real-Life Examples | Advantages of Combining Multiple Models to Improve Performance | Comparison of Ensemble Methods with Single Learners

Class 22 – Handling Class Imbalance Using SMOTE and Undersampling Techniques | Importance of Addressing Class Imbalance in Classification Problems | Comparison Between Over-Sampling and Under-Sampling Methods | Implementation of SMOTE for Minority Class Enhancement | ROC Curve – Concept, Interpretation, and Use Cases | AUC (Area Under the Curve) – Evaluating Model Performance Through True Positive and False Positive Rates

Class 23 – Implementation of Class Imbalance Techniques: SMOTE | Tomek Links | ADASYN | Understanding the Need for Balancing Imbalanced Datasets | Applying Resampling Techniques to Improve Model Performance | Implementation of Naive Bayes Algorithm | Types of Naive Bayes (Gaussian | Multinomial | Bernoulli) | Real-Life Application Scenarios of Naive Bayes | Model Evaluation Using Confusion Matrix and Accuracy Metrics

Class 24 – Implementation of Ensemble Techniques (Bagging | Boosting | Stacking | Voting Classifier) | Comparative Analysis of Ensemble Models and Base Learners | Use Cases and Advantages of Ensemble Learning in Real-World Scenarios | Introduction to Natural Language Processing (NLP) | How NLP Works – From Text to Insights | Text Processing Framework: Tokenization | Stopword Removal | Lemmatization | Sentiment Analysis | Preparing Text Data for Machine Learning Models

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