The Exact 7-Month AI/ML Roadmap I Would Follow If I Were Starting Today

Brivo — Tue, 03 Mar 2026 02:57:30 +0000

I see this question every single week in Discord servers,
Telegram groups, and college WhatsApp chats:

"Bhai kahan se start karu AI/ML?"
[Translation: "Where do I start with AI/ML?"]

And I see the same bad advice every time:
"Just start with Andrew Ng"
"No, do Krish Naik first"
"Just learn Python and figure it out"

Nobody gives a complete, opinionated, ordered answer.
This is mine.

PREREQUISITES (before Month 1)

Basic programming understanding (any language)
A laptop with 8GB RAM minimum
2 hours per day commitment
That's it. No Math degree needed. No CS background needed.

MONTH 1: Python Foundations

Topics:

Variables, data types, control flow
Functions, recursion, lambda
Object-Oriented Programming (classes, inheritance)
File I/O, exception handling
List comprehensions, generators
NumPy and Pandas basics

Project to build:
A data analysis script that reads a CSV,
cleans it, and outputs summary statistics.

MONTH 2: Mathematics for ML

Topics:

Vectors and matrices (Linear Algebra)
Derivatives and gradients (Calculus)
Probability distributions
Bayes theorem
Mean, variance, standard deviation, correlation (Statistics)

Don't overdo this. 3–4 weeks is enough.
You'll understand more math as you apply it
in algorithms — it clicks better that way.

MONTH 3–4: Machine Learning

Topics:

Linear & Logistic Regression
Decision Trees & Random Forests
Support Vector Machines
K-Nearest Neighbors
K-Means Clustering
PCA (Dimensionality Reduction)
Model evaluation: accuracy, precision, recall, F1, ROC-AUC
Scikit-learn end to end

Projects:

House price prediction (regression)
Email spam classifier (classification)
Customer segmentation (clustering)

MONTH 5: Deep Learning

Topics:

Perceptrons and multilayer neural networks
Activation functions, loss functions
Backpropagation (understand it, don't just use it)
CNNs for image classification
RNNs and LSTMs for sequential data
PyTorch framework (learn this, not TensorFlow)
Batch normalization, dropout, regularization

Projects:

Image classifier (CIFAR-10)
Sentiment analysis with LSTM

MONTH 6: NLP & Computer Vision

Topics:

Tokenization, stemming, lemmatization
TF-IDF, Word2Vec, GloVe
Transformer architecture (read the original paper)
BERT and fine-tuning pre-trained models
Object detection (YOLO)
Image segmentation (U-Net)
HuggingFace library

MONTH 7+: Generative AI

Topics:

Large Language Models (GPT architecture)
Prompt engineering techniques
Retrieval Augmented Generation (RAG)
Vector databases (Pinecone, ChromaDB)
Langchain framework
Building and deploying AI apps

This is the most employable skill in tech right now.
Don't skip it.

RESOURCES

Free:

YouTube: CodeWithHarry (Python), Krish Naik (ML/DL/GenAI), CampusX (Data Science), Apna College (Python/DSA)
Coursera: Andrew Ng ML Specialization (audit for free)
Fast.ai (Deep Learning)
Kaggle Learn (hands-on practice)

Organized Bundle (paid, ₹399):
If you don't want to spend weeks organizing all of
the above yourself → bundle.scrunchcreate.com
All the above educators, structured in this exact
order, one-time payment, lifetime access.

FINAL ADVICE

Don't collect resources. Build things.
For every 10 hours of watching → spend 10 hours coding.

The people who break into AI jobs aren't the ones
who watched the most tutorials.

They're the ones who built the most projects.

Now close this article and open your code editor.

DEV Community: Brivo

The Exact 7-Month AI/ML Roadmap I Would Follow If I Were Starting Today