৭ মাসের স্টাডিপ্ল্যান

৪৮ টি লাইভ ক্লাস

১৩ টি ইন্ডাস্ট্রি স্ট্যান্ডার্ড প্রজেক্ট

প্রোগ্রেস ট্র্যাকিং

সপ্তাহে ৩ দিন সাপোর্ট ক্লাস

কমিউনিটি সাপোর্ট

লাইফটাইম এক্সেস

ইন্টারভিউ হ্যান্ডবুক

সার্টিফিকেট

ল্যাপটপ/ডেস্কটপ (৮ জিবি র‍্যাম)

ভালো ইন্টারনেট কানেকশন

লেগে থাকার মানসিকতা

থাকছে ৫ টির বেশি রিয়েল লাইফ প্রজেক্ট, যেগুলো করে হয়ে যাবে মোস্ট ডিমানডিং প্রোডাক্ট বিল্ড করার অভিজ্ঞতা।

Run you first code!

এই কোর্সের প্রিরিকুইজিট হলো পাইথন। তাই পাইথনের উপর এই ভিডিওগুলো দেখে নিলে আপনার প্রিপারেশন হবে সলিড। 

বেসিক ঝালাই করুন এখান থেকে

Python

Pandas

NumPys

Matplotlib

scikit-learn

TensorFlow

Keras

PyTorch

Jupyter Notebook

Google Colab

GitHub

FastAPI

Docker

MLflow

SpaCy

OpenAI

LangChain

Postman

Swagger (OpenAPI)

টিম প্রোজেক্ট

সিভি মেকিং এবং পোর্ট ফোলিও বিল্ডিং

জব পাওয়া পর্যন্ত সাপোর্ট

হ্যান্ডবুক ডাউনলোড করুন

গল্পে গল্পে মেশিন লার্নিং

MD Mazharul Islam Nabil

Wasee Ahsan

M Parvej Alam

Zobayer Al Mahmud

Tasnia Washim

Md.Sakibul Hasan Chanchal

Md Arman Ahmed Jony

Rohima Khanom

Md. Manwarul Alam

Methila Afrin Meem

Nargees Jahan

Thongya Murong

Nazmul Hossain

Zawad Ishmam

Toukir Ahamed Pigeon

Md.Sakibul Hasan

Mojibur Rahaman

Md Mochaddec Hossain (Maruf)

ব্যাচে ভর্তি হোন

আপনি যদি পাইথন প্রোগ্রামিং খুব ভালো ভাবে&nbsp;জানেন, তাহলে AI Engineering Bootcamp for Programmers লাইভ কোর্সটি আপনার জন্যই।<h3>এই কোর্সটা কাদের জন্য?</h3><ul><li>যদি আপনি Python প্রোগ্রামিংয়ের বেসিকটা জানেন, আর ভাবছেন – “AI নিয়ে কিছু শিখলে ভালো হতো!”</li><li>আপনি যদি বিশ্ববিদ্যালয়ের স্টুডেন্ট হন, কিংবা ফ্রেশ গ্র্যাজুয়েট, অথবা এমন কেউ যিনি প্রোগ্রামিং জানেন কিন্তু AI নিয়ে কাজ শুরু করতে পারছেন না&nbsp;</li><li>অলরেডি প্রোগ্রামার হিসেবে কাজ করছেন এবং চাচ্ছেন AI ইঞ্জিনিয়ার হিসেবে ক্যারিয়ার স্টার্ট করতে</li></ul><h3>কোর্স থেকে কী কী শিখতে পারবেন?&nbsp;</h3><ul><li>&nbsp;Python প্রোগ্রামিং — AI এর জন্য যেটা সবচেয়ে জরুরি</li><li>&nbsp;প্রয়োজনীয় ম্যাথমেটিক্স ও স্ট্যাটিস্টিক্স — ব্যাকএন্ডে যা সব কিছুর বেস</li><li>&nbsp;NLP (Natural Language Processing) — ল্যাংগুয়েজ বোঝা ও আন্ডারস্ট্যান্ড করা</li><li>&nbsp;LLM (Large Language Models) — যেমন GPT-এর মতো মডেল কিভাবে কাজ করে</li><li>&nbsp;LangChain — LLM-কে কাজে লাগিয়ে প্রজেক্ট বানানো শিখবেন</li><li>&nbsp;Vector Database — AI মডেল ট্রেইনিংয়ের জন্য দরকারি ডেটা ম্যানেজমেন্ট</li><li>&nbsp;Speech Recognition — ভয়েস কমান্ড আর অডিও প্রসেসিং শেখা</li><li>&nbsp;Real-world Projects — পুরো কোর্সে ১২টা রিয়েল প্রজেক্ট বানাবেন নিজের হাতে! </li></ul><h3>কী কী প্রোজেক্ট করানো হবে কোর্সে?&nbsp;</h3><ol><li>House Price Predictor (Regression)</li><li>Diabetes Detection System (Classification)</li><li>Customer Segmentation Engine (Unsupervised)</li><li>MNIST Digit Classifier (Deep Learning)</li><li>Dog vs Cat Classifier (CNN)</li><li>Sales Predictor (Time-Series)</li><li>Sentiment Analyzer (NLP)</li><li>Resume Generator (LLM)</li><li>Business Assistant Chatbot (LangChain Tools)</li><li>Document QA Chatbot (LangChain + PDF)</li><li>Research Assistant (Multi-Agent)</li><li>Knowledge Base Chatbot (RAG)</li><li>Final Capstone: End-to-End AI Application with Deployment</li></ol>

একদম শূন্য থেকে কীভাবে হবেন AI Engineer

সাপোর্ট ক্লাস

Sazzad Hossain

গল্পে গল্পে Machine Learning: সহজভাবে বুঝে ফেলুন কঠিন ধারণাগুলো

Michael: An AI data analyst for actionable insights.

Leo: An AI tutor and automation agent with tool use.

Custu: Customer support AI with database integration.

Askly:  A context-aware chatbot for natural conversations.

House Price Predictor

Diabetes Detection System

Customer Segmentation Engine

MNIST Digit Classifier

Dog vs Cat Image Classifier

Sales Prediction Tool

Deployed Machine Learning API

Text Classification Using Transformers

Resume Generator Using GPT

Chatbot for Document-Based QA

Business Assistant Chatbot

Research Planning and Summarization Assistant

Knowledge Base Chatbot with Vector DB

ব্যাচের কার্যক্রম - মেইন লাইভ ক্লাস শুরুর আগেই যারা ইনরোল করেছেন, তারা কি বসে থাকবেন ? না! এই মডিউলে ব্যাসিক বেশ কিছু ভিডিও দেওয়া আছে। শুরু হয়ে যাক তবে শেখার জার্নি!&nbsp;

শুরুর আগে শুরু

1.1 Python Env...ent Setup

1.2 Run Your First ever Python Code

2.1 Introduction to String

2.2 Indexing in String

2.3 String Immutability

Environment Setup & Python Foundations

Python Basics & Control Flow

Goal: Manage and transform data effectively using Python’s built-in collections. Live Class 1: Built in Data Structures (Part 2) Tuples | Tuple Methods | Sets | Set Methods | Dictionaries | Dictionary Methods Live Class 2: Advanced Looping with Data Structures Iterating over Lists, Tuples, Sets, Dictionaries | Zip and Enumerate | Nested Dictionary/Lists | Mini Practice Problems Mathematical Concepts Covered:Data Organization and Representation (Tuples, Sets, Dictionaries) | Understanding Uniqueness and Immutability | Mapping and Key-Value Relationships | Iteration Logic over Complex Structures | Sequence Pairing (Zip, Enumerate) | Nested Data Handling | Logical Structuring and Abstraction | Problem Solving with Data Patterns

Core Data Structures Mastery

Goal: Write reusable code, work with files, and learn OOP basics to prepare for real projects. Live Class 1: Functions and Modules Defining Functions | Parameters &amp; Arguments | Return Values | Default &amp; Keyword Arguments | Variable Scope | Try &amp; Except | Using Built-in Modules Live Class 2: File Handling and OOP Basics Reading &amp; Writing Files | File Modes | With Statement | Introduction to OOP | Classes &amp; Objects | Attributes &amp; Methods | Mini Project (Student Grade Calculator or Contact Book) Mathematical Concepts Covered: Functional Abstraction and Modularity | Parameters, Arguments, and Return Logic | Variable Scope and Data Flow | Error Handling and Logical Debugging | File Input/Output Operations | Data Persistence and Representation | Object-Oriented Thinking (Classes, Objects, Attributes, Methods) | Applied Logic through Mini Project (Computation &amp; Data Management)

Functions, Files & OOP Basics

মডিউল ১ থেকে মডিউল ৪ পর্যন্ত যা যা আলোচনা হয়েছে, সেটার উপর ভিত্তি করে থাকবে একটা বড়সড় এসাইনমেন্ট ও লাইভ টেস্ট। ঝালিয়ে নিতে পারবেন নিজেকে।

Exam week 1

পাইথন প্রোগ্রামিং দিয়ে শুরু (Module 0-5)

Synensis IT

Apurba Technologies Limited

North South University

Objective: Understand the full course and get a breif knowledge about ML Live Class 1: AI Engineer Full Roadmap | Intro to ML Live Class 2: Types of Machine Learning | Intro to EDA Mathematical Concepts Covered: Foundations of Artificial Intelligence and Machine Learning | Data Representation and Abstraction | Introduction to Statistical Thinking | Types of Machine Learning (Supervised, Unsupervised, Reinforcement) | Exploratory Data Analysis (EDA) Concepts | Data Patterns, Distributions, and Correlations | Logical and Analytical Reasoning for Model Understanding

Intro to Machine Learning

<blockquote>Objective: Understand the Full ML Pipeline and Get ready to deep dive into the world of ML Live Class 1: Master Pandas, Numpy , Vector and its Importance in ML Live Class 2: Machine Learning Pipeline , Best Practices | Underfit , Overfit , Bais , Variance Mathematical Concepts Covered: Vectors and Linear Algebra Foundations | Numerical Computation with NumPy | Data Manipulation and Analysis using Pandas | Understanding Dimensions and Data Structures | Machine Learning Pipeline Concepts | Model Evaluation and Optimization | Underfitting vs Overfitting | Bias-Variance Tradeoff | Statistical Reasoning and Data Preprocessing Best Practices </blockquote>

Machine Learning Pipeline

Objective: Introduce Git, Jupyter, Colab, and environment setup. Live Class 1: What is Git and why use it | Basic Git commands: init, add, commit | Understanding version control | What is GitHub and how it works | Creating and managing repositories on GitHub | Cloning and pushing code | Branching and merging basics | Typical project folder structure | Best practices for organizing codebase Live Class 2: What is Jupyter Notebook | Running and managing code cells | Introduction to Google Colab | Uploading and sharing notebooks | Writing clean and readable code | Using comments and proper indentation | Writing Markdown in notebooks | Formatting text, lists, and code blocks with Markdown | Creating headings and notes for better understanding Mathematical Concepts Covered: Version Control Logic and Change Tracking | Workflow Structuring and Project Organization | Logical Sequencing of Code Operations (Init, Add, Commit, Merge) | Systematic Thinking for Code Management | Documentation and Presentation through Markdown | Structured Data Representation in Notebooks | Readability, Clarity, and Code Abstraction | Analytical Organization of Code and Notes for Reproducibility

Development Tools and Best Practices

Live Class 1: Introduction to linear regression | Fitting a line to data using least squares | Understanding slope and intercept | What is overfitting in regression | Introduction to regularization | Ridge regression and L2 penalty | Lasso regression and L1 penalty | Comparing ridge and lasso | When to use ridge vs lasso Live Class 2: Introduction to model evaluation | What is Mean Absolute Error (MAE) | Interpreting MAE in regression models | What is Root Mean Squared Error (RMSE) | Difference between MAE and RMSE | What is R-squared (R²) | Interpreting R² as model accuracy | Choosing the right metric for evaluation Mathematical Concepts Covered: Linear Regression and Line Fitting using Least Squares | Understanding Slope, Intercept, and Line Equation | Overfitting and Model Complexity | Regularization Techniques (L1 and L2 Penalties) | Ridge and Lasso Regression Comparison | Statistical Model Evaluation Metrics (MAE, RMSE, R²) | Error Measurement and Interpretation | Model Accuracy and Performance Assessment | Analytical Decision-Making for Metric Selection Project: House Price Predictor

Regression Models and Evaluation

Live Class 1: Introduction to logistic regression | Understanding sigmoid function and probabilities | Binary classification with logistic regression | Introduction to K-Nearest Neighbors (KNN) | How KNN makes predictions based on distance | Choosing the right value of K | Introduction to decision trees | Splitting criteria: Gini and Entropy | Overfitting and pruning in decision trees | Comparing logistic, KNN, and decision trees Live Class 2: What is a confusion matrix | Understanding TP, FP, TN, FN | Calculating precision and recall | What is F1 score and why it matters | Balancing precision and recall | Introduction to ROC curve | Interpreting ROC-AUC score | Choosing metrics based on problem type Project: Titanic Dataset Mathematical Concepts Covered: Logistic Regression and Sigmoid Function | Probability and Binary Classification | K-Nearest Neighbors Algorithm and Distance Metrics | Model Parameter Tuning (Choosing K) | Decision Trees and Splitting Criteria (Gini, Entropy) | Overfitting and Pruning Techniques | Comparative Analysis of Classification Models | Confusion Matrix and Classification Outcomes (TP, FP, TN, FN) | Precision, Recall, and F1 Score Calculations | Balancing Evaluation Metrics | ROC Curve and AUC Interpretation | Metric Selection Based on Problem Context

Classification Models and Evaluation

মডিউল ৬ থেকে মডিউল ১০ পর্যন্ত যা যা আলোচনা হয়েছে, সেটার উপর ভিত্তি করে থাকবে একটা বড়সড় এসাইনমেন্ট ও লাইভ টেস্ট। ঝালিয়ে নিতে পারবেন নিজেকে।

Exam Week 2

Live Class 1: Introduction to K-Means clustering | How K-Means groups similar data points | Choosing number of clusters | What is the Elbow Method | Using Elbow Method to find optimal K | Introduction to PCA (Principal Component Analysis) | Reducing dimensionality with PCA | Interpreting principal components | PCA vs clustering: when to use what Live Class 2: Hierarchical Clustering &amp; DBSCAN Project: Clustering Assignment: Iris Dataset Mathematical Concepts Covered: K-Means Clustering and Data Grouping | Cluster Count Selection and Optimization | Elbow Method for Determining Optimal Clusters | Dimensionality Reduction with Principal Component Analysis (PCA) | Interpretation of Principal Components | Comparative Analysis of PCA and Clustering Techniques | Hierarchical Clustering Methods | Density-Based Spatial Clustering of Applications with Noise (DBSCAN)

Unsupervised Learning and Dimensionality Reduction

মেশিন লার্নিং এ হাতেখড়ি | Basic To Advanced (Module 6-12)

Live Class 1: Introduction to neural networks | Structure of a neural network: neurons, layers, activation functions | Forward propagation process | Calculating outputs in a neural network | What is backpropagation | Adjusting weights during backpropagation | Gradient descent and learning rate | Role of loss function in training | Training a neural network through epochs Live Class 2: Deep Neural Networks, Vanishing/Exploding Gradients, and Batch Normalization (with TensorFlow) Project: MNIST Digit Classifier Mathematical Concepts Covered: Neural Network Architecture (Neurons, Layers, Activation Functions) | Forward Propagation and Output Calculation | Backpropagation Algorithm and Weight Adjustment | Gradient Descent Optimization and Learning Rate | Loss Function and Model Training | Iterative Training over Epochs | Deep Neural Networks and Layer Stacking | Challenges of Vanishing and Exploding Gradients | Batch Normalization Techniques | Practical Implementation with TensorFlow

Introduction to Deep Learning

Live Class 1: Introduction to Convolutional Neural Networks (CNN) | What is computer vision? Use cases in industry | Basics of CNNs: convolution, pooling, flatten, FC | CNN architecture overview: input, convolution, pooling, fully connected layers | What is a convolution layer and its purpose | Applying filters to input images | Understanding kernel size and stride | Max pooling and average pooling layers | Reducing spatial dimensions with pooling | Role of convolution and pooling in feature extraction Live Class 2: Introduction to image classification | Overview of CNN for image classification | Using Keras for building CNN models | Loading and preprocessing image data | Defining the CNN architecture in Keras | Adding convolution, activation, and pooling layers | Flattening and adding fully connected layers | Compiling the model with optimizer, loss, and metrics | Training the model with image data | Evaluating model performance on test data | Image preprocessing techniques: resizing, normalization | Data augmentation for Image Data Project: Dog vs Cat Image Classifier Mathematical Concepts Covered: Convolutional Neural Networks (CNN) Fundamentals | Computer Vision Concepts and Industry Applications | Convolution Operations and Filter Application | Kernel Size, Stride, and Feature Extraction | Pooling Layers (Max and Average) and Spatial Dimension Reduction | CNN Architecture Components (Input, Convolution, Pooling, Fully Connected Layers) | Image Classification Basics with CNN | Model Building with Keras Framework | Image Data Loading and Preprocessing | Network Architecture Design and Layer Stacking | Model Compilation and Training | Performance Evaluation Metrics | Image Preprocessing Techniques (Resizing, Normalization) | Data Augmentation for Robustness

Computer Vision with CNNs

মডিউল ১১ থেকে মডিউল ১৪ পর্যন্ত যা যা আলোচনা হয়েছে, সেটার উপর ভিত্তি করে থাকবে একটা বড়সড় এসাইনমেন্ট ও লাইভ টেস্ট। ঝালিয়ে নিতে পারবেন নিজেকে।

Exam Week 3

ডীপ লার্নিং & কম্পিউটার ভিশন (Module 13-15)

Live Class 1: Object Detection Model Finetuning (Roboflow Journey) | Yolo Model Finetune Live Class 2: Transformers : The core of Modern AI Project: Sales Object Tracking &amp; Heatmap Visualization Mathematical Concepts Covered: Object Detection and Model Fine-Tuning Techniques | YOLO Model Architecture and Adaptation | Transfer Learning and Model Optimization | Fundamentals of Transformers Architecture | Attention Mechanisms and Self-Attention | Sequential Data Processing | Core Concepts Driving Modern AI Advances

Model Finetuning & Intro to Transformers

Live Class 1: Building Scalable ML APIs with FastAPI What is FastAPI and why it's ideal for ML deployment | Setting up a FastAPI app for serving predictions | Creating robust endpoints with Pydantic for validation | Serving ML models using joblib or pickle | Structuring APIs: /predict, /health, /info endpoints | Testing APIs using Swagger UI, ReDoc, and Postman | Intro to async handling and real-time performance advantages Live Class 2: Dockerizing Your ML Service &amp; Intro to MLflow Docker Focus: | What is Docker and why use it for ML APIs | Writing a Dockerfile for FastAPI apps | Building Docker images and running containers | Exposing containerized APIs over localhost/port | Testing containerized predictions with curl/Postman | Analyzing API responses and troubleshooting | Basics of cloud deployment | Deploying Dockerized applications on cloud platforms | Using services like AWS, Heroku, or Google Cloud for deployment Project: Production Ready ML Model Pipeline with FastAPI,&nbsp;Docker&nbsp;&amp;&nbsp;MLflow Mathematical Concepts Covered: API Design and Deployment for Machine Learning Models | Data Validation with Pydantic | Model Serialization and Serving Techniques | Endpoint Structuring and Asynchronous Handling | Performance Optimization in Real-Time Systems | Containerization with Docker for Reproducible Environments | Building and Managing Docker Images and Containers | Networking and API Exposure | Testing and Troubleshooting API Endpoints | Cloud Deployment Fundamentals | Scalable ML Service Management

Model Deployment & Monitoring w/ FastAPI, Docker and MLflow

মডেল ফাইনটিউনিং & ডেপ্লয়মেন্ট (Module 16-17)

iBusiness Formula

Brain Station 23

Tahmid Rahman

Live Class 1: Introduction to text preprocessing | What is tokenization and how it works | Breaking text into words or sentences | Introduction to lemmatization | Reducing words to their base form using lemmatization | What are stopwords and why remove them | Identifying and filtering stopwords in text | Using libraries like NLTK or SpaCy for preprocessing | Combining tokenization, lemmatization, and stopwords removal for clean text data Live Class 2: Introduction to word embeddings | What is TF-IDF (Term Frequency-Inverse Document Frequency) | How TF-IDF captures word importance in a corpus | Introduction to Word2Vec and its two models: Continuous Bag of Words (CBOW) and Skip-gram | Training Word2Vec on text data to create word embeddings | What is GloVe (Global Vectors for Word Representation) | How GloVe differs from Word2Vec and captures global word relationships | Using pre-trained embeddings for NLP tasks | Comparing TF-IDF, Word2Vec, and GloVe for various applications Project: Sentiment Analysis on Product Reviews Mathematical Concepts Covered: Text Preprocessing Techniques | Tokenization: Word and Sentence Segmentation | Lemmatization and Morphological Normalization | Stopwords Identification and Removal | Natural Language Processing (NLP) Libraries Usage (NLTK, SpaCy) | Term Frequency-Inverse Document Frequency (TF-IDF) for Word Importance | Word Embedding Models: Word2Vec (CBOW, Skip-gram) | Global Vectors for Word Representation (GloVe) | Training and Using Word Embeddings | Comparative Analysis of TF-IDF, Word2Vec, and GloVe in NLP Tasks

Natural Language Processing Fundamentals

Live Class 1: Introduction to the attention mechanism | How attention allows models to focus on important parts of the input | Types of attention: hard and soft attention | Self-attention and its role in sequence processing | Introduction to Transformer architecture | Key components of Transformers: Encoder and Decoder | Multi-head attention mechanism | Positional encoding to preserve sequence order | Advantages of Transformers over RNNs and LSTMs | Applications of Transformers in NLP tasks like translation and summarization Live Class 2: Introduction to Hugging Face Transformers | Overview of BERT and GPT models | Understanding pre-trained models and transfer learning | Fine-tuning BERT for text classification tasks | Preparing the dataset for fine-tuning | Training BERT with custom data using Hugging Face’s Trainer API | Fine-tuning GPT for text generation tasks | Adjusting hyperparameters during fine-tuning | Evaluating model performance on downstream tasks | Using pre-trained Hugging Face models for various NLP applications Project: Text Classification Using Transformers Mathematical Concepts Covered: Attention Mechanisms and Focused Processing | Hard and Soft Attention Types | Self-Attention in Sequence Modeling | Transformer Architecture Fundamentals (Encoder, Decoder) | Multi-Head Attention and Parallelization | Positional Encoding for Sequence Order Preservation | Comparative Advantages Over RNNs and LSTMs | Applications in NLP (Translation, Summarization) | Pre-Trained Transformer Models (BERT, GPT) | Transfer Learning and Fine-Tuning Techniques | Dataset Preparation and Model Training | Hyperparameter Optimization | Model Evaluation and Deployment in NLP Tasks

Transformers and Hugging Face Models

Live Class 1: Introduction to prompt patterns in NLP | Designing effective prompts for different tasks | Few-shot learning and how it reduces data requirements | Understanding few-shot, one-shot, and zero-shot learning | Introduction to OpenAI API | Overview of GPT models and their capabilities | Making API requests to OpenAI for text generation and understanding tasks | Prompt engineering for better results | Fine-tuning models with custom prompts for specific tasks | Using OpenAI API in real-world applications like chatbots, summarization, and translation Live Class 2: Introduction to function calling with GPT models | Using GPT for structured outputs like JSON, tables, or lists | Designing prompts for structured responses | Calling external functions or APIs from within a prompt | Handling structured data in response formats | Parsing and formatting structured outputs from GPT | Integrating GPT with external systems for dynamic function calls | Use cases of function calling in automation, data extraction, and decision-making tasks | Best practices for optimizing structured output quality Project: Linkedin Post Generator Mathematical Concepts Covered: Prompt Engineering and Pattern Design in NLP | Few-Shot, One-Shot, and Zero-Shot Learning Paradigms | Data Efficiency in Model Training | OpenAI API Usage and GPT Model Capabilities | Structured Output Generation (JSON, Tables, Lists) | Function Calling and External API Integration | Designing and Parsing Structured Prompts | Automation and Dynamic Data Extraction | Real-World Application Development with GPT | Optimization Techniques for Output Quality

Prompt Engineering and GPT APIs

NLP, ট্রান্সফর্মারস (Module 18-20)

Live Class 1: Introduction to chains in NLP workflows | Combining multiple tasks or steps using chains | Using memory in AI models for context retention | Implementing memory to maintain state across interactions | Creating prompt templates for consistent input formatting | Benefits of reusable prompt templates in large-scale applications | How chains and memory work together in task automation | Example use cases of chains, memory, and templates in complex tasks like conversation bots and multi-step reasoning | Optimizing performance with chains and templates Live Class 2: Introduction to CSV/PDF Q&amp;A pipelines | Extracting data from CSV files for Q&amp;A tasks | Parsing PDF files for text-based question answering | Using OCR for extracting text from scanned PDFs | Building a pipeline to answer questions from structured or unstructured documents | Document indexing for fast retrieval | How to index documents for efficient search and retrieval Project: Chatbot for Document-Based QA Mathematical Concepts Covered: NLP Workflow Automation with Task Chaining | Context Retention Using Memory in AI Models | State Management Across Interactions | Prompt Template Design and Reusability | Task Automation Through Chains and Memory Integration | Multi-Step Reasoning and Complex Task Handling | Data Extraction from Structured (CSV) and Unstructured (PDF) Formats | Optical Character Recognition (OCR) Techniques | Pipeline Construction for Document-Based Q&amp;A | Document Indexing and Efficient Retrieval Methods

Introduction to LangChain

Live Class 1: Introduction to LangChain Agent Executor | How the Agent Executor manages complex tasks | Using LangChain to build intelligent agents | Creating and integrating custom tools into LangChain | Leveraging external APIs and services as custom tools | Implementing intermediate memory for maintaining context across tasks | Storing temporary information to guide agent decision-making | Optimizing agent performance with efficient memory management | Example use cases of LangChain in conversational agents and workflow automation Live Class 2: Introduction to API tools in LangChain | Using built-in API tools for integrating external services | Handling API requests and responses with LangChain | Introduction to file tools for managing document-based inputs | Reading, writing, and processing files in pipelines | Using LangChain to process and analyze file data (CSV, JSON, text) | Error handling strategies in LangChain workflows | Catching and managing exceptions in API and file tool interactions | Debugging and logging in LangChain | Best practices for building robust workflows with error handling Project: Business Assistant Chatbot Mathematical Concepts Covered: Task Management and Execution with LangChain Agent Executor | Intelligent Agent Design and Custom Tool Integration | External API Utilization and Service Orchestration | Context Preservation through Intermediate Memory | Memory Optimization for Efficient Decision-Making | Workflow Automation in Conversational Agents | API Integration and Request-Response Handling | File Management and Data Processing Pipelines (CSV, JSON, Text) | Error Detection, Handling, and Debugging Strategies | Robust Workflow Construction and Logging Best Practices

Building Intelligent Tool-Integrated Applications

Live Class 1: Introduction to CrewAI | How CrewAI enables automation and collaboration across tasks | Overview of AutoGen and its role in automating task generation | Using AutoGen for dynamic task creation and assignment | Task delegation strategies for optimizing workflow | Assigning tasks based on skills, priorities, and timelines | Managing task dependencies and ensuring smooth handoffs | Best practices for delegating tasks effectively within automated systems Live Class 2: Introduction to multi-agent systems | How multiple agents interact and collaborate to solve problems | Example 1: Multi-agent system for recommendation engines | Agents working together to provide personalized suggestions | Example 2: Collaborative robots in manufacturing | Coordinating tasks between robots to improve efficiency | Example 3: Autonomous vehicle fleet management | Multiple agents managing traffic flow and navigation Project: Career Mate MulltiAgent Mathematical Concepts Covered: Automation and Collaboration Frameworks in AI Systems | Dynamic Task Generation and Assignment Algorithms | Optimization of Task Delegation Based on Skills and Priorities | Managing Task Dependencies and Workflow Sequencing | Multi-Agent System Architecture and Interaction Models | Cooperative Problem Solving and Distributed Decision Making | Application of Multi-Agent Systems in Recommendation Engines, Robotics, and Autonomous Vehicle Coordination

Multi-Agent Systems and Workflow Automation

Live Class 1: Introduction to FAISS (Facebook AI Similarity Search) | Using FAISS for efficient similarity search and nearest neighbor search | How FAISS works with high-dimensional data | Introduction to Chroma for scalable vector storage | Using Chroma for real-time, fast retrieval of vector-based data | Overview of LangChain retriever | Integrating LangChain with FAISS and Chroma for document retrieval | How LangChain handles retrieval-augmented generation (RAG) tasks | Benefits of combining FAISS, Chroma, and LangChain for high-performance information retrieval | Use cases in document search, semantic search, and knowledge extraction Live Class 2: Introduction to the full Retrieval-Augmented Generation (RAG) pipeline | Ingestion of data for RAG systems: collecting and processing raw data | Preprocessing data for embedding generation | Using embeddings to represent data in vector space | Introduction to embeddings: how they capture semantic meaning | Generating embeddings with models like BERT or Sentence Transformers | Storing and indexing embeddings for efficient retrieval | Retrieval process: searching for relevant documents or data based on query Project: Knowledge Base Chatbot with Vector DB Mathematical Concepts Covered: Similarity Search and Nearest Neighbor Algorithms (FAISS) | High-Dimensional Data Handling and Vector Search | Scalable Vector Storage with Chroma | Integration of LangChain Retriever for Document Retrieval | Retrieval-Augmented Generation (RAG) Framework | Combining FAISS, Chroma, and LangChain for Efficient Information Retrieval | Semantic Search and Knowledge Extraction Use Cases | Data Ingestion and Preprocessing for RAG Pipelines | Embedding Generation and Vector Space Representation | Use of Pre-Trained Models (BERT, Sentence Transformers) for Embeddings | Embedding Storage, Indexing, and Retrieval Processes

Retrieval-Augmented Generation and Vector Databases

মডিউল ১৬ থেকে মডিউল ২৪ পর্যন্ত যা যা আলোচনা হয়েছে, সেটার উপর ভিত্তি করে থাকবে একটা বড়সড় এসাইনমেন্ট ও লাইভ টেস্ট। ঝালিয়ে নিতে পারবেন নিজেকে।

Exam Week 4

AI Agent Development (Module 21-25)

Capstone Project: Students will propose and build a complete real-world AI application integrating skills from previous modules. The project must include:<ul><li>A problem statement with measurable outcomes (e.g., customer support automation, smart report generation, intelligent search)</li><li>Data ingestion (structured/unstructured)</li><li>Application of an LLM using prompt engineering, LangChain, or Hugging Face</li><li>Integration with at least one external API or tool</li><li>UI or interface demonstration (CLI, chatbot, or web UI)</li><li>Deployment and version control through GitHub</li></ul> Examples:<ul><li>An AI assistant for internal HR document search</li><li>A chatbot that answers customer product questions using uploaded manuals</li><li>A multi-agent research and report generator tool</li></ul> Live Class 1: Capstone Demo, Peer Review, Instructor Feedback&nbsp; Live Class 2: Resume &amp; GitHub Review, LinkedIn Optimization, Mock Interviews, Career Roadmap

Capstone Presentation and Career Support

Live Class 1: Getting Remote Jobs with AI SkillsTopics: Understanding the Global AI Job Market | High-Demand Remote Roles: AI Engineer, No-Code AI Builder, Prompt Engineer | Platforms for Remote Work (LinkedIn, We Work Remotely, AngelList, Turing) | Creating a Job-Ready AI Portfolio | Writing AI-focused CVs and Cover Letters | How to Apply &amp; Follow Up | Interview Questions for Remote AI Roles | Soft Skills &amp; Time Zone Management for Remote Work Assignment: Create a remote-job-ready CV highlighting your AI skills + Build or update a LinkedIn profile showcasing your projects. Live Class 2: Freelancing with AI: Strategy to ExecutionTopics: Introduction to AI Freelancing | Marketplace Breakdown: Upwork, Freelancer, PeoplePerHour, Contra (excluding Fiverr for this module) | In-demand AI Freelancing Gigs: Chatbot setup, automation, API integration, AI workflow design | How to Create an Optimized Freelance Profile | Portfolio Best Practices | Bidding Strategies &amp; Client Communication | Building a Long-Term Freelancing Career Assignment: Create a freelance profile draft with 1 AI service gig + Submit a sample proposal based on a real job post.

Income Pathways with AI Skills: Remote Jobs & Freelancing

Live Class 1: CV, Portfolio &amp; LinkedIn Profile BuildingCraft an AI Engineering CV showcasing ML/AI skills, projects, frameworks, and tools | Build a portfolio with AI/ML projects, GitHub links, and deployed models | Optimize LinkedIn profile with AI-focused keywords, experience, and skills Live Class 2: Local &amp; Remote Job Search StrategiesResearch and apply to local AI engineering jobs | Learn tips for landing remote AI/ML job opportunities | Discover best practices for international roles and stand out in global AI job applications Assignment: Write a tailored cover letter for a remote AI engineering job position and submit it.

Job Assistance Program

ক্যাপস্টোন প্রোজেক্ট | ফ্রিল্যান্সিং, লোকাল ও রিমোট জব এসিস্ট্যান্স প্রোগ্রাম (Module 26-28)

Md Mobashir Hasan

Nazib Riasat

MD. Shahriar Imtiaz

AI Engineering Bootcamp