AI Engineering Bootcamp for Programmers

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! 

Objective: Enable students to collect and manipulate real-world data.

Live Class 1: What is a CSV file | Reading CSV using csv.reader() | Reading rows as dictionary using csv.DictReader() | What is JSON | Reading JSON file using json.load() | Converting JSON string using json.loads() | File modes: r, w, a | Reading text file using open() | Writing text file using write() | Using with statement for safe file handling

Live Class 2: API Requests with requests module | Making GET and POST requests | Handling API response data | Basics of web scraping | Using requests and BeautifulSoup | Extracting data from HTML elements | Introduction to data cleaning | Handling missing values with Pandas | Removing duplicates and irrelevant columns | Formatting and transforming data types

Objective: Build math foundations for machine learning models.

Live Class 1: Introduction to vectors | Vector addition and scalar multiplication | Understanding matrices | Matrix dimensions and structure | Matrix operations: addition and multiplication | Dot product between vectors | Geometric interpretation of dot product | Applications of dot product in data science

Live Class 2: Introduction to probability concepts | Basic rules of probability | What is statistics and its role in data analysis | Calculating mean and median | Understanding variance and standard deviation | Introduction to data distributions | Normal distribution and its properties | Other common distributions: binomial, Poisson

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

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

Project: House Price Predictor