AI Engineer Roadmap — AI School of India

Phase 01 — Python Foundations · Weeks 1–3

Start from absolute zero. Learn Python the way an AI engineer uses it — variables, data structures, control flow, functions, and exception handling. No fluff, all practical.

PythonData StructuresFunctionsOOP

1.1 Introduction to Programming & Python Setup Video Ready

Watch on YouTube · What is Programming? — Python from Zero Topics covered

What programming is and why Python is the AI language
Installing Python, VS Code, and Jupyter Notebook
Writing and running your first Python program
How AI engineers use Python differently from web developers

1.2 Variables, Data Types & Operators Video Ready

Watch on YouTube · Variables in Python — Explained in Simple Telugu Topics covered

Integers, floats, strings, and booleans
Variable naming rules and best practices
Arithmetic, comparison, and logical operators
Type conversion and type checking

1.3 Lists — Python's Most Used Data Structure Video Ready

Watch on YouTube · List Data Structures — Explained from Scratch Topics covered

Creating, indexing, and slicing lists
append, insert, remove, pop, sort, reverse
List comprehensions for cleaner code
Nested lists and 2D data representation

1.4 Tuples, Sets & Dictionaries Video Ready

Watch on YouTube · Tuples — Data Structures Made Easy Watch on YouTube · Dictionaries — Explained in Simple Telugu Watch on YouTube · Sets — Explained in Simple Telugu Topics covered

Tuples — immutable sequences and when to use them
Sets — unique values, union, intersection, difference
Dictionaries — key-value pairs, real-world use cases
When to choose which data structure

1.5 Control Flow — If-Else & Loops Video Ready

Watch on YouTube · If-Else — Decision Making in Python Watch on YouTube · Loops — For & While Loops Explained Topics covered

if, elif, else — conditional logic with real examples
for loops — iterating over lists, ranges, dictionaries
while loops — when and how to avoid infinite loops
break, continue, pass keywords

1.6 Functions & Exception Handling Video Ready

Watch on YouTube · Functions — Write Reusable Python Code Watch on YouTube · Exception Handling — Handle Errors Gracefully Topics covered

Defining functions, parameters, return values
*args and **kwargs for flexible functions
Lambda functions and when to use them
try, except, finally — handling errors gracefully in AI code

Phase 02 — Data Analysis · Weeks 4–6

Real datasets, real insights. Master Pandas for data wrangling and Matplotlib & Seaborn for visualisation — with projects using IPL data, Indian datasets, and business scenarios.

PandasMatplotlibSeabornEDA

2.1 Pandas Essentials — Loading & Exploring Data Video Ready

Watch on YouTube · Pandas Part 1 — DataFrames & Reading Data Topics covered

What is Pandas and why every AI engineer needs it
Reading CSV, Excel, JSON files into DataFrames
head(), tail(), info(), describe() — understanding your data fast
Selecting columns, filtering rows, conditional queries

2.2 Data Cleaning — Handling Messy Real-World Data Video Ready

Watch on YouTube · Pandas Part 2 — Data Cleaning & Missing Values Topics covered

Finding and handling missing values (dropna, fillna)
Removing duplicates and fixing data types
Renaming columns and reshaping DataFrames
String operations on text columns

2.3 Data Aggregation & Grouping Patterns Video Ready

Watch on YouTube · Pandas Part 3 — GroupBy, Merge & Pivot Tables Topics covered

groupby() — summarising data by category
merge() and join() — combining multiple datasets
pivot_table() — Excel-style summaries in Python
Project: IPL season-wise performance analysis

2.4 Advanced Pandas — Apply, Lambda & Performance Tips Video Ready

Watch on YouTube · Pandas Part 4 — Apply, Lambda & Feature Engineering Topics covered

apply() and map() — transforming columns efficiently
Creating new features from existing columns
Working with datetime columns
Exporting cleaned data for ML pipelines

2.5 Data Visualisation — Charts That Tell a Story Video Ready

Watch on YouTube · Matplotlib & Seaborn — Full Visualisation Course Topics covered

Line, bar, scatter, histogram, pie charts with Matplotlib
Seaborn heatmaps, pairplots, boxplots for EDA
Customising colours, labels, titles, and figure sizes
Project: Visual EDA on a real Indian business dataset

Phase 03 — Machine Learning · Weeks 7–10

From theory to working models. Learn every core ML algorithm — with the maths explained simply and projects using real Indian datasets like student placement, airline satisfaction, and loan defaults.

Scikit-learnRegressionClassificationSVMRandom Forest

3.1 How Machines Actually Learn — ML Concepts Demystified Video Ready

Watch on YouTube · Machine Learning Clearly Explained Topics covered

AI vs ML vs Deep Learning — clear distinctions with examples
Supervised, unsupervised, and reinforcement learning
The ML workflow: data → model → prediction
Real-world ML use cases in Indian companies

3.2 Supervised Learning & the Training Pipeline Video Ready

Watch on YouTube · Supervised Learning — Training Pipeline Topics covered

Train/test/validation split — why it matters
Features (X) vs target (y) — thinking like an ML engineer
Cross-validation to avoid lucky splits
Evaluation metrics: accuracy, MAE, RMSE, R²

3.3 Linear Regression — Predicting Continuous Values Video Ready

Watch on YouTube · Linear Regression — Theory & Use Cases Watch on YouTube · Linear Regression — Practical Implementation Topics covered

The equation of a line — from maths to ML
Cost function and how the model learns
Simple vs multiple linear regression
Practical: implementing with Scikit-learn from scratch

3.4 Gradient Descent — The Engine Behind Every ML Model Video Ready

Watch on YouTube · Gradient Descent — Learning Rate & Intuition Topics covered

What gradient descent is — explained with a visual analogy
Learning rate — too high, too low, just right
Batch vs stochastic vs mini-batch gradient descent
Why this concept applies to LLMs and deep learning too

3.5 Overfitting & Underfitting — Models That Generalise Video Ready

Watch on YouTube · Overfitting & Underfitting — Bias-Variance Tradeoff Topics covered

Overfitting — when your model memorises instead of learning
Underfitting — when your model is too simple
Bias-variance tradeoff — the core ML balancing act
Regularisation (Ridge & Lasso) to fix overfitting

3.6 Data Preprocessing — Building a Clean ML Pipeline Video Ready

Watch on YouTube · Data Preprocessing — Clean ML Pipeline Watch on YouTube · Encoding — Label Encoding vs One-Hot Encoding Watch on YouTube · Feature Scaling — Importance & Practical Demo Topics covered

Encoding categorical variables — Label vs One-Hot
Feature scaling — StandardScaler vs MinMaxScaler
Handling imbalanced datasets
Building a reusable Scikit-learn Pipeline object

3.7 Logistic Regression — Classification from the Ground Up Video Ready

Watch on YouTube · Logistic Regression — Theory + Practical Topics covered

Sigmoid function and probability outputs
Decision boundary and threshold tuning
Confusion matrix, precision, recall, F1-score, ROC-AUC
Project: Predicting student pass/fail outcomes

3.8 KNN Algorithm — Classify by Similarity Video Ready

Watch on YouTube · KNN Algorithm — Theory Watch on YouTube · KNN Practical — Student Placement Prediction Topics covered

How KNN works — distance-based intuition
Choosing the right value of K
Feature scaling is critical for KNN — why
Project: Predicting student campus placements

3.9 SVM — Finding the Perfect Decision Boundary Video Ready

Watch on YouTube · SVM — Complete Theory Watch on YouTube · SVM — Practical Implementation Topics covered

Support vectors, hyperplane, and margin — visually explained
Kernel trick — handling non-linearly separable data
C and Gamma hyperparameters — what they control
When to prefer SVM over other classifiers

3.10 Decision Trees & Random Forests — Ensemble Power Video Ready

Watch on YouTube · Decision Trees Part 1 — Entropy & Information Gain Watch on YouTube · Decision Trees Part 2 — Gini Index Watch on YouTube · Random Forest — Ensemble Algorithm Watch on YouTube · Decision Tree & Random Forest Practical — Airline Project Topics covered

Decision Trees — entropy, information gain, Gini index
Random Forest — bagging multiple trees for better accuracy
Feature importance — understanding what drives predictions
Project: Airline passenger satisfaction prediction

3.11 Hyperparameter Tuning — Squeezing the Best from Your Model Video Ready

Watch on YouTube · Hyperparameter Tuning — Grid Search & Random Search Topics covered

Parameters vs hyperparameters — the key distinction
GridSearchCV — exhaustive search over a parameter grid
RandomizedSearchCV — faster tuning for large search spaces
Cross-validation inside tuning to avoid data leakage

3.12 Complete ML Course — End-to-End in 7 Hours Video Ready

Watch on YouTube · Machine Learning Playlist — Continue the Complete ML Path Topics covered

Full recap of all ML algorithms in one sitting
End-to-end project walkthrough from raw data to deployed model
Interview preparation — most asked ML questions
Best resource to revise before applying for AI/ML roles

Phase 04 — Deep Learning · Weeks 11–13

Neural networks are the foundation of everything from image recognition to ChatGPT. These videos are coming soon — subscribe to get notified when each one drops.

Neural NetworksCNNsTransfer LearningPyTorch

4.1 Neural Networks — How the Brain of AI Works Coming Soon