Name: AI Engineering 101: What You Need to Know Before You Start | Ostad
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যদি পাইথন প্রোগ্রামিং এর উপর ব্যাসিক নলেজ আপনার থাকে, 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>

AI Engineering: A Step by Step Roadmap

Shariful Islam Mubin

AI Engineering 101: What You Need to Know Before You Start

Objective: Enable students to collect and manipulate real-world data. Live Class 1: Reading CSV, JSON, File Operations Live Class 2: API Requests, Web Scraping Basics, Data Cleaning with Pandas

APIs, File Handling, and Data Manipulation

Objective: Build math foundations for machine learning models. Live Class 1: Linear Algebra - Vectors, Matrices, Dot Products Live Class 2: Probability, Statistics - Mean, Variance, Distributions

Math for Machine Learning

Objective: Introduce Git, Jupyter, Colab, and environment setup. Live Class 1: Git, GitHub, Project Structure, Version Control Live Class 2: Jupyter Notebook, Google Colab, Code Formatting, Markdown

Development Tools and Best Practices

Live Class 1: Linear, Ridge, Lasso Regression Live Class 2: Model Evaluation Metrics (MAE, RMSE, R^2)&nbsp; Project: House Price Predictor

Regression Models and Evaluation

Live Class 1: Logistic Regression, K-Nearest Neighbors, Decision Trees Live Class 2: Evaluation Metrics (Confusion Matrix, F1 Score, ROC-AUC)&nbsp; Project: Diabetes Detection System

Classification Models and Evaluation

Live Class 1: K-Means Clustering, PCA, Elbow Method Live Class 2: Customer Segmentation with Visualization&nbsp; Project: Customer Segmentation Engine

Unsupervised Learning and Dimensionality Reduction

Live Class 1: Neural Networks, Forward and Backpropagation Live Class 2: Activation Functions, Loss Functions, Implementing a Simple NN&nbsp; Project: MNIST Digit Classifier

Introduction to Deep Learning

Live Class 1: CNN Architecture, Convolution and Pooling Layers Live Class 2: Image Classification with CNN using Keras&nbsp; Project: Dog vs Cat Image Classifier

Convolutional Neural Networks (CNNs)

Live Class 1: RNN, LSTM, Sequential Data Processing Live Class 2: Time-Series Forecasting with LSTM&nbsp; Project: Sales Prediction Tool

Recurrent Neural Networks and Time-Series Forecasting

Live Class 1: Flask API for Model Serving Live Class 2: Docker Containerization, Postman Testing, Cloud Deployment Basics&nbsp; Project: Deployed Machine Learning API

Model Deployment and APIs

Live Class 1: Text Preprocessing - Tokenization, Lemmatization, Stopwords Live Class 2: Word Embeddings - TF-IDF, Word2Vec, GloVe&nbsp; Project: Sentiment Analysis on Product Reviews

Natural Language Processing Fundamentals

Live Class 1: Attention Mechanism, Transformer Architecture Overview Live Class 2: Hugging Face Transformers - Fine-tuning BERT/GPT&nbsp; Project: Text Classification Using Transformers

Transformers and Hugging Face Models

Live Class 1: Prompt Patterns, Few-Shot Learning, OpenAI API Overview Live Class 2: Function Calling and Structured Outputs with GPT&nbsp; Project: Resume Generator Using GPT

Prompt Engineering and GPT APIs

Live Class 1: Chains, Memory, Prompt Templates Live Class 2: CSV/PDF Q&amp;A Pipelines, Document Indexing&nbsp; Project: Chatbot for Document-Based QA

Introduction to LangChain

Live Class 1: LangChain Agent Executor, Custom Tools, Intermediate Memory Live Class 2: API Tools, File Tools, Error Handling&nbsp; Project: Business Assistant Chatbot

Building Intelligent Tool-Integrated Applications

Live Class 1: CrewAI, AutoGen Overview, Task Delegation Strategies Live Class 2: Multi-Agent Implementation Examples&nbsp; Project: Research Planning and Summarization Assistant

Multi-Agent Systems and Workflow Automation

Live Class 1: FAISS, Chroma, LangChain Retriever Live Class 2: Full RAG Pipeline: Ingestion, Embeddings, Retrieval&nbsp; Project: Knowledge Base Chatbot with Vector DB

Retrieval-Augmented Generation and Vector Databases

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