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AI/ML/NLP Research Track

A complete practical path from Python and Kaggle fundamentals to machine learning, NLP, transformers, multilingual assistants, and beginner research workflow.

4.9rating1,320 students14h 30m total16 lessons
3 Hands-on Labs 1 Challenges 4 Projects
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What you'll learn

Build strong Python and data-stack foundations with real examples
Apply supervised ML with scikit-learn for classification and regression tasks
Complete three must-have beginner ML projects and report metrics correctly
Understand NLP foundations: tokenization, embeddings, transformers, and BERT
Use Hugging Face pipelines and fine-tuning workflows in practical projects
Build a multilingual Norwegian + Urdu assistant as a research-style capstone
Develop paper-reading, experiment evaluation, and portfolio publishing habits

Final Project

Build and evaluate a Norwegian + Urdu AI assistant with multilingual NLP modules and publish the full research portfolio

Curriculum

16 lessons · 14h 30m
1

Track Orientation: Full Guide and Study Plan

20 min
2

Kaggle Python Course: Practical Fast Track

35 min
3

Python Beginner to Expert: Complete Guide

90 min
4

NumPy Detailed Tutorial

Lab
45 min
5

Pandas Detailed Tutorial

Lab
50 min
6

Matplotlib Detailed Tutorial

Lab
40 min
7

Jupyter Notebook Workflow Mastery

35 min
8

ML Basics: Supervised Learning and Metrics

50 min
9

Project 1: Spam Detection

Project
45 min
10

Project 2: Movie Sentiment Analysis

Project
50 min
11

Project 3: Tabular Prediction Model

Project
45 min
12

NLP Foundation: Transformers + Hugging Face

55 min
13

Research Project: Norwegian + Urdu AI Assistant

Milestone
60 min
14

How to Read AI Papers as a Beginner

25 min
15

Build Your AI Research Portfolio

Challenge
20 min
16

AI Engineering Roadmap and Next Steps

30 min

Course Info

Lessons16 lessons
Total time14h 30m
LevelBeginner → Advanced
Students1,320
Rating4.9 / 5.0
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