Prompt Engineering & Generative AI for AI Engineers

Set up a clean Python development environment by installing Python 3.14.2, configuring path, and installing Visual Studio Code with the Python extension to prevent environment errors.

Master Python basics by learning what a program does and how variables store data, with data types like int, float, string, and boolean guiding operations.

Learn how arithmetic, comparison, and logical (including not) operators in Python help calculate values, compare data, and make decisions, with practical demos of marks and attendance in Visual Studio Code.

Discover how conditional statements guide program decisions with if and else in Python, emphasizing condition evaluation, indentation, and printing outcomes such as you pass or you fail.

Master Elif logic, learn the range function, and implement for and while loops with conditions. See how comments clarify code and how loops power AI workflows.

Learn how break and continue control Python loops by exiting and skipping iterations, with practical examples from for loops using range.

Master how Python lists store multiple values, are mutable, use indexing and slicing to access 1D, 2D, and 3D structures, with append, pop, and sort to manage data.

Explore tuples in Python, contrast them with lists, and master creation with round brackets, immutability, indexing, slicing, and essential methods like count and index.

Explore python sets, collections of unique, unordered items created with curly braces, including add, remove, union, and intersection; note they are unindexed, remove duplicates automatically, and empty set uses set().

Learn how Python dictionaries store data as key‑value pairs, access values by key or get, and modify with add, update, remove, and keys, values, items methods, noting mutability and order.

Learn how to define and reuse code with Python functions using def, parameters, and return values; master syntax, indentation, calls, and default parameters through practical examples.

Explore function arguments and lambda functions in Python, distinguish parameters from arguments, and apply one-line lambda expressions with map and filter for short, modular code.

discover how modules and packages organize Python code, with modules as files and packages as folders, and learn to import built-in and own libraries to reuse code.

Understand how libraries, modules, and packages differ, why built-in, third-party, and custom libraries matter, and how to install and import numpy with pip for artificial intelligence projects.

Learn how Python uses classes as blueprints to create objects with data and behaviors, and how init, self, and dot notation manage instances.

Explore how attributes store data inside objects and how methods enable actions, using Python and Visual Studio Code examples, and learn why object oriented programming underpins AI models and training.

Explore inheritance and polymorphism in Python, where a parent class provides shared behavior to child classes, and method overriding yields different outputs in vehicle and shape examples for ai frameworks.

Encapsulation protects data by bundling it with methods and exposing access via safe methods. Abstraction hides complexity by showing only what is necessary, with shape implementing the area interface.

Learn how Python reads, writes, and appends txt, csv, and json files to persist data, with safe file handling in VS Code.

Learn how to read and write CSV files with Python using the CSV module, including using CSV reader and CSV writer to manage student records.

Learn to read and write JSON files with Python's json module, loading data from data.json and dumping updates with indentation. Explore JSON's role in AI APIs and LMS configurations.

Learn how Python communicates with external apis using the requests library to perform a get request to GitHub's api and handle endpoints, http responses, and json data.

Master posting data via post requests, using headers and authentication, with json payloads and api keys, and handle responses and errors in an api-driven prompt engineering workflow.

Master prompt engineering to generate Python code faster and cleaner by using structured prompts with persona, context, task, constraints, and output format, and applying prompts for debugging and refactoring.

Use prompts to debug code with AI as an around-the-clock debugging assistant. Identify syntax, runtime, and logical errors, and craft prompts that explain and fix issues.

AI enhances code documentation by explaining code, generating a doc string, and adding inline comments. It creates readme files with prompts like explain this code for beginner to improve clarity.

Explore data cleaning essentials, from identifying missing values and inconsistent formats to cleaning a messy employee dataset with Python, while using prompt engineering to automate clean data for reliable ai.

Build a python data cleaning script for a messy csv, using prompt engineering to generate cleaning logic, clean age, salary, country, and email; remove duplicates; save the clean csv.

Learn how NumPy provides a fast, memory-efficient math engine for AI. Apply vectorized operations on one-dimensional to three-dimensional arrays and understand the bridge between data and Python.

Explore broadcasting and vectorization in NumPy, showing how operations on entire arrays replace loops. Learn scalar broadcasting, shape rules, and image normalization to 0–1 values for faster AI learning.

Explore how pandas data frames resemble labeled, excel-like tables in python, and learn indexing with .loc and .iloc to select data by label or position.

Detect missing data with isNull and count it with sum to assess the dataset, then drop or fill with mean, median, or most common value.

Explore histograms to understand data distribution and scatterplots to reveal relationships, with practical visualization in matplotlib for AI data analysis and debugging.

Explore Seaborn for advanced visualizations built on Matplotlib, using count, bar, box, and scatter plots with Pandas dataframes to explore department trends, salaries, and data distribution.

Learn how prompt engineering accelerates data cleaning and visualization using pandas, matplotlib, and seaborn. Apply role, context, and task prompts to plan cleaning, choose the right chart, and generate code.

Load and explore a dataset in Python using pandas, apply cleaning if needed, and use prompt engineering to ask ai for insights and suggest charts before visualizing, exploratory data analysis.

Prompt AI to select charts for age, salary, department. Use histogram for age, histogram or boxplot for salary, boxplot for salary by department, plus scatterplot for age versus salary.

Master machine learning by contrasting supervised and unsupervised learning. Learn how supervised learning uses labeled data to classify or predict, while unsupervised learning discovers structure in raw data.

Apply train-test split to evaluate models on unseen data and prevent memorization and overfitting. Use 80/20 training and testing split with features like hours studied and attendance to predict pass/fail.

Evaluate machine learning models using accuracy and F1 for classification and RMSE for regression, training on training data and evaluating on testing data.

Learn how RMSE, or root mean squared error, measures prediction errors for continuous targets like salary, house price, and temperature, by squaring errors, averaging, and taking the square root.

Explore underfitting and overfitting, showing how a too-simple model fails training data while overfitting memorizes data, and see RMSE and train/test splits in a linear regression example.

Explore overfitting, where a model memorizes training data and fails to generalize to unseen data. Learn how more data, simplifying models, removing unnecessary features, and cross-validation prevent overfitting.

Explore the bias-variance tradeoff to balance model simplicity and complexity, improve generalization, and prevent underfitting and overfitting through regularization, cross-validation, and thoughtful data use.

Master feature engineering basics to turn raw data into meaningful signals by creating, transforming, and selecting features, including scaling, one-hot encoding, and log transforms to handle skewed data.

Discover practical feature engineering by transforming raw data into smarter inputs for a house price model, including new features, scaling, and improved predictions with linear regression.

Explore prompt-driven auto-generation of features, dataset cleaning, and EDA summaries to speed data analysis, identify issues, and derive actionable insights with AI as a data assistant.