Rank
Provider & Course
Duration
Effort Daily
Fee
“How Feasible for Beginner”
1
Logicmojo AI Course
20
6 h
₹ 28 750
1 : 15 live mentors, 5 projects from Scratch
2
Coursera / DeepLearning.AI — AI for Everyone
1
9 h
₹ 0 / 4 k cert
Non-technical, strategy first
3
Simplilearn × IBM with AI Basics with Watsonx
6
7 h
₹ 29 900
Cloud vouchers, labs Session
4
Udacity — AI Programming with Python
12
8 h
₹ 77 000*
Tutor feedback in 24 h
5
Google — ML Crash Course
—
15 h total
Free
GPU in browser, no install
6
DataCamp — Intro to Deep Learning
6
6 h
₹ 12 000 (annual sub)
All lessons in-browser
7
Microsoft Learn — Azure AI Fund. (AI-900)
4
4 h
₹ 4 700 exam
HR-friendly badge + Azure credit
8
Great Learning — Kick-Start AI
4
5 h
Free
UT-Austin faculty, live Slack
9
edX / Harvard — CS50 AI
10
8 h
₹ 0 / 13 k cert
Famous CS50 projects
10
Khan Academy — Intro to Machine Learning
—
8 h total
Free
Animated zero-math primer
Course Capsules
- Logicmojo AI Course ⭐ Our Top Pick Logicmojo AI Course is top in our list as it’s a very beginner friendly course for learning AI from scratch. No Prior coding experience required to join this course. Classes starts from very basic and gradually move to advance with project development on GenAI. Even someone from tech background can join this course to learn and developed projects to add in resume. Quick Overview What You Need to Know Details How Long It Takes 28 weeks (about 7 months) • 160 hours of live classes • 60 hours of self-study(Assignments) Time Per Week 6 hours total • 3 hours Live classes • 3 hours in live coding sessions How You Learn Mixed approach • Watch short videos at your own pace • Join live classes on Saturday & Sunday Cost ₹65,000 total • Can pay ₹4,395 per month for 12 months (0% interest) Teacher Support 1 teacher for every 15 students • Each group of 15 gets their own dedicated mentor Special Features • Everything works in your browser (JupyterHub) • Automatic checking of your code • "Run All" button tests your work instantly Certificate Logicmojo AI-Beginner badge Has QR code for verification
You can check for more detail : Logicmojo AI Course
Why This Course Is Great for Beginners
Logicmojo removes the common problems that make beginners give up:
- No Setup Headaches Start coding right away in your browser Everything is already set up and ready Don't waste time installing complicated software
- Builds Your Confidence Step by Step Course designed for small, regular victories Each short lesson ends with a "green checkmark" when your code works By week 5, you'll have a real app running online Feel the satisfaction of creating working code What You'll Learn - Week by Week The 20-week journey builds your skills gradually, with a real project at each stage:
Weeks
What You'll Learn
Theory You'll Understand
What You'll Actually Build
1-2
Python basics, VS Code tips
Difference between variables and tensors
"Hello Data" notebook uploaded to GitHub
3-4
Data wrangling with Pandas
How joins and groupby work (like SQL)
Milestone 1: Clean 1 million rows of data in under 45 seconds
5-7
Basic statistics + Logistic regression
Sigmoid curves and log-loss
Command-line classifier with confusion matrix image
8-10
Decision trees & ensembles
Bias-variance tradeoff (animated)
Report on optimal tree depth using cross-validation
11-14
Mini-CNN on MNIST dataset
How convolution kernels work
Milestone 3: Live visualization of neural network weights
15-18
Introduction to Generative AI
Embeddings and prompt tokens
Use Llama-3 on HuggingFace and measure quality with BLEU
19-20
Final Project Sprint
API Gateway vs Lambda cold-start
Telegram bot running live on AWS Lambda
Note: Milestones 2 & 4 match the goals for weeks 8 and 18
Tools You'll Use
Type
What You'll Use
Why It Matters
Coding Environment
JupyterHub (Logicmojo's cloud)
No installation needed; GPU available when required
Saving Your Work
GitHub Classroom
Automatic testing of your code; learn professional workflows
Making Apps Live
Replit (Week 5) → AWS Lambda (Week 20)
Experience both serverless and container deployment
AI Tools
HuggingFace SDK + Llama-3
Safe environment to practice with cutting-edge AI
Support You'll Get
· Mentors (1 for every 15 students)
Former junior engineers from MAANG companies
Weekly "bug-bash" Zoom sessions
Personal code review every two weeks
· Quick Help on Slack
Average response time: Less than 4 hours on weekdays
· Special Masterclass
Amit Kumar (Staff ML Engineer at Google)
2-hour session on fixing common AI problems (exploding gradients, NaN losses)
Real Student Success Story
"I came in with zero Git skills. By week 8, my logistic regression tool identified customer churn risk for our sales data. My boss approved my switch to the analytics team!" — Priya K., July 2024 batch
Results That Matter
92% of students complete the final project (January 2025 data)
Average first ML job salary: ₹7.8 LPA
Course pays for itself: About 2.7 times return within 12 months
Your Weekly Schedule
Day
What You'll Do
When
Monday-Wednesday
Watch 2-3 short videos (20 minutes each)
Evenings
Thursday
Take a 30-minute quiz + test your notebook code
After dinner
Saturday
Live coding lab #1
10:00 AM - 11:30 AM
Sunday
Live lab #2 (Q&A + mini-project)
10:00 AM - 11:30 AM
The Bottom Line
Logicmojo's Starter Track is the best launching pad for AI beginners in 2025. You get:
Multiple hands-on projects you can show employers
Personal mentorship from experienced engineers
Fair, transparent pricing
Skills and confidence to land your first analytics or ML job
Average starting salary of ₹17.8 LPA for graduates
More about the Best AI Courses, you can check here: Best AI Courses in India
- Coursera / DeepLearning.AI — AI for Everyone Quick Overview What You Need to Know Details Total Time • About 9 hours • 4 modules • 12 short quizzes Study Options • Finish in one week (~9 hours) • OR spread it out: 2-3 hours per week over a month How You Learn 100% at your own pace on Coursera • Works on computer, tablet, or phone Cost Free to watch • Optional certificate costs ₹4,000 What You Need to Know Nothing! No coding, no math required Certificate Coursera + DeepLearning.AI digital badge • Has QR code for verification
Why Beginners Love This Course
- No Coding Required Every concept explained through stories (like mail sorting, loan approvals) Pictures and diagrams instead of math equations
- "AI Project Canvas" Tool Downloadable PDF template Helps you plan AI projects using 6 simple categories (goal, data, KPI, etc.) Turns complex ideas into plain English
- Super Quick to Complete Entire course fits in a weekend Many people finish everything in just one day
What You'll Learn - Module by Module
Module
Time
What You'll Learn
What You'll Create
1 · What AI Can & Can't Do
2 hours
• Difference between Narrow and General AI
• Real examples of AI in use
List of 3 tasks you could automate
2 · Data Strategy
2 hours
Why good data matters (quality & quantity)
Map of where to find data (internal vs external)
3 · Project Lifecycle
3 hours
• How AI learns (train-test flow)
• How to improve AI over time
First draft of your AI Project Canvas
4 · Society & Ethics
2 hours
• Fairness in AI• Privacy concerns• Safety issues
200-word plan for preventing bias
Note: Other students will review and give feedback on your Canvas and ethics plan
Tools and Features
Feature
What It Is
Why It Helps Beginners
Video Player
Coursera HTML5 player
• Speed up or slow down (0.75x to 2x)• Subtitles in 11 languages
Study Materials
Downloadable transcripts & slides
• Great for reviewing later• Helpful for non-native English speakers
Peer Review
Built-in feedback system
Get helpful comments from students worldwide
Digital Badge
Shareable Coursera link + Credly export
• Easy to add to LinkedIn• HR departments can verify it
No coding tools needed - everything runs in your web browser
Your Teachers and Support
Main Instructor:
Andrew Ng - Co-founded Google Brain and Coursera
Co-Instructor:
Kian Katanforoosh - Head of Curriculum at DeepLearning.AI
Help Available:
Community Teaching Assistants - Volunteer mentors answer questions
Average response time in forums: about 12 hours
Monthly Live Q&A with DeepLearning.AI team (recorded if you miss it)
Weekend Study Schedule
Day & Time
What You'll Do
Time Needed
Saturday Morning
Watch Modules 1 & 2 + take quizzes
9:00 AM - 12:00 PM
Saturday Afternoon
Write your reflections for modules 1 & 2
4:00 PM - 5:00 PM
Sunday Morning
Watch Modules 3 & 4 + take quizzes
9:00 AM - 12:00 PM
Sunday Afternoon
Create your AI Project Canvas + ethics plan
12:30 PM - 2:00 PM
Sunday Evening
Review 2 other students' work
6:00 PM - 6:30 PM
Alternative: Spread this over 4 weeks, studying 2 hours each weekday evening
The Bottom Line
AI for Everyone is your mindset primer - it won't teach you programming, but it will help you:
Understand AI concepts
Talk confidently about data volume, success metrics, and fairness
Be ready for work discussions on Monday morning
Best approach: Take this course first for the big picture, then add a hands-on coding course (like Logicmojo or Udacity) to turn your new vocabulary into practical skills. This combination gives you a solid, hype-free foundation for your AI journey.
AI Course Options - Simplified
Simplilearn × IBM — AI Basics with Watsonx
Quick Course Facts
Duration: 6 weeks (about 36 hours with instructor + 20 hours practice)
Weekly time: 6-7 hours (2 live sessions of 3 hours each + quick practice labs)
Format: Live Zoom classes with cloud-based labs on IBM Cloud
Cost: ₹29,900 (can pay ₹2,750 per month for 12 months with 0% interest)
Bonus: $300 IBM Cloud credit to use Watsonx, AutoAI, and Monitoring tools
Projects: 10 guided mini-labs plus 3 major graded projects
Certificate: Two certificates - one from Purdue University and one from IBM (verified through Credly)
Why Beginners Complete This Course Successfully
Easy Start with Drag-and-Drop: You begin using AutoAI's simple point-and-click interface. By week 3, you can see the Python code that was created automatically, making it easier to learn coding gradually.
No Surprise Bills: The $300 credit covers about 200 hours of training on IBM's powerful computers, so you won't get unexpected charges.
Real-World Examples: All practice datasets (like product reviews, insurance claims, loan applications) come from actual IBM client work, so you understand why these models matter in business.
What You'll Learn Each Week
Week 1: Get familiar with IBM Cloud and Watsonx Studio. Connect a Jupyter notebook to AutoAI.
Week 2: Build a keyword extractor using AutoAI. Create a REST endpoint for keyword extraction.
Week 3: Learn model evaluation basics and ROC-AUC. Get an automatically generated ROC dashboard.
Week 4: Deploy a Watsonx pipeline. Build a live fraud detection API using logistic regression.
Week 5: Learn Docker and Cloud Pak for Data. Create a containerized model that runs locally.
Week 6: Build a sentiment analysis API with Docker and monitoring. Create an end-to-end service with Grafana alerts.
Tools and Technology You'll Use
Coding Environment: Watsonx JupyterLab (no need for a powerful laptop - GPUs are provided online)
AutoML Tool: AutoAI GUI (one-click model creation with explanation charts)
Deployment: Watsonx Pipelines, Docker, and Cloud Pak (learn both serverless and container methods)
Monitoring: Watson OpenScale and Grafana (drag-and-drop charts and alert webhooks)
Version Control: GitHub Classroom with private repositories and automatic grading
Instructors and Support
Main Instructor: J. Brown - IBM Master Instructor, formerly with Watson Health. Teaches both live sessions and hosts weekly Q&A on Slack.
Guest Speaker: Dr. D. Kulkarni - Adjunct Professor at Purdue University. Gives a talk on model fairness in week 3.
Lab Assistants: 6 certified IBM Cloud advocates available 24/7 on Slack with average response time under 2 hours.
Weekly Schedule
Monday Evening: Live lecture and demo (theory plus hands-on demonstration) - 7:00-8:30 PM
Tuesday: Self-paced reading and 30-minute quiz - flexible timing
Wednesday: TA office hours chat - 7:00-8:00 PM
Thursday Evening: Hands-on lab with pair coding - 7:00-8:30 PM
Friday-Saturday: Complete notebook tasks and upload to GitHub - about 1 hour
Sunday: Weekly reflection and peer comments - 30 minutes
Summary
This 6-week bootcamp gives beginners three important achievements:
A model built using a simple interface that you can understand
Python code automatically generated from that interface that you can modify
A containerized API that you can deploy - all using IBM's free credit system
With certificates from both Purdue and IBM, graduates often get junior ML or automation jobs earning around ₹9 LPA, which is about double the course fee within a year.Udacity — AI Programming with Python Nanodegree
Quick Course Facts
Duration: 12 weeks (recommended pace)
Weekly time: 8-10 hours, self-scheduled
Format: Completely online with videos and auto-graded workspaces
Cost: List price ₹77,000, but promotional discounts often reduce it to about ₹31,000
Support: On-demand Slack mentors with 24-hour code review guarantee
Projects: 5 graded projects, each reviewed line-by-line
Certificate: Udacity Nanodegree certificate (PDF plus LinkedIn badge)
Why Beginners Complete This Course Successfully
Detailed Code Reviews: Every project gets line-by-line feedback within 24 hours. Bad coding practices are flagged and improvement tips are given.
Browser-Based Workspace: No need to install Python locally. NumPy, PyTorch, and GPU access are pre-configured.
Flexible Timeline: You can pause for a week without penalty and simply extend your subscription if needed.
What You'll Learn Each Week
Weeks 1-2: Python foundations, object-oriented programming, virtual environments. Build a "Explore-US-Bikeshare" command-line data explorer.
Weeks 3-4: NumPy vector mathematics. Create a notebook showing how matrix multiplication is faster than loops.
Weeks 5-6: Pandas data analysis and visualization. Write a report summarizing Airbnb NYC dataset.
Weeks 7-8: Basic calculus and first neural network in PyTorch. Build a feed-forward network that fits a sine curve.
Weeks 9-10: Word embeddings and RNN introduction. Create an IMDb sentiment classifier with at least 85% accuracy.
Weeks 11-12: Deployment and inference basics. Package your classifier as a Flask app and upload to Heroku.
Tools and Technology You'll Use
Notebook Environment: Udacity Workspaces (no setup required, GPU toggle for deep learning sections)
Version Control: Git and GitHub Classroom (mandatory pull requests trigger automated tests)
Deep Learning Library: PyTorch 2.x (clear, easy-to-understand syntax compared to TensorFlow)
Deployment: Flask and Heroku or Render (learn 12-factor app principles and Procfile basics)
Instructors and Support
Content Lead: Mat Leonard, PhD (formerly at Google Brain) - appears in high-level concept videos
Code Reviewers: Over 150 freelancers (formerly at Amazon, Meta) - provide written reviews within 24 hours with unlimited resubmission
Mentors: Slack channel with average 8-hour response time, weekly live Q&A calls (recorded)
Weekly Schedule
Monday/Tuesday: Watch concept videos (about 1 hour each) - 2 hours total
Wednesday: Mini-quiz and small workspace exercise - 1 hour
Thursday/Friday: Build project features and submit pull request - 3 hours
Saturday: Receive code review and apply fixes - 1 hour
Sunday: Optional mentor call replay - 30 minutes
Summary
Udacity's Nanodegree costs more than most beginner courses, but the detailed human code review and required Git workflow simulate a real development environment. Graduates finish with five polished code repositories and access to a recruiter-friendly alumni network, making the higher cost worthwhile for learners who want detailed feedback and a portfolio that impresses hiring managers.Google : Machine Learning Crash Course (MLCC)
Quick Overview Table
Metric
Detail
Total length
About 15 hours of interactive lessons, mini-lectures, and coding labs
Weekly load
Finish in one intensive weekend or spread over 3 evenings × 5 hours
Delivery
100% browser-based; TensorFlow Playgrounds + Colab notebooks
Tuition
Free , no hidden paywall, no credit-card gate
Credential
None (downloadable completion letter); GitHub repo serves as proof
Ideal device
Any laptop with Chrome; heavy sections spin up Google-hosted GPU
Why This Course Works Well for Beginners
Instant feedback: When you adjust the learning-rate slider, the loss curve updates immediately; concepts feel real and tangible
Nothing to install: Colab notebooks come with TensorFlow already installed; Google Cloud GPU access is included
Small steps: 25 "lessons" that average 20 minutes each; every lesson ends with a green "Correct!" checkmark
Course Content and Learning Goals
Segment
Goal
Milestone Deliverable
Lesson 1-3
Linear & logistic regression
Fit straight-line model to housing data; explain weight sign
Lesson 4-6
Feature crosses & buckets
Build wide model predicting baby-weight from birth metrics
Lesson 7-10
Loss functions & SGD
Plot squared-error vs log-loss; animate gradient descent
Lesson 11-15
DNN on MNIST
30-line Colab trains 98% accurate digit classifier
Lesson 16-18
Over-/under-fitting
Visualise training vs validation curves; add L2 regularisation
Lesson 19-21
Hyper-parameter tuning
Grid-search learning rate + batch size; compare AUC
Lesson 22-25
ML engineering best practices
Convert notebook to Reusable Python module, push to GitHub
Tools and Environment
Layer
Tool
Beginner Benefit
Concept visual
TensorFlow Playground
Real-time graph of weights & activations
Coding lab
Google Colab (GPU)
One-click to run; 12 GB RAM VM
Dataset hub
tf.keras.datasets
Pre-loaded MNIST, California housing
Version control exercise
GitHub gist + Colab "Save a Copy"
Teaches commit basics without CLI
Teachers and Support
Role
Contributor
Interaction
Lead author
Cassie Kozyrkov (Chief Decision Scientist, Google)
Narrates key "What-to-watch-for" videos
Engineering authors
Google Brain ML Education Team
Write inline Colab comments
Community
MLCC Google Group + StackOverflow tag
Crowd-sourced Q&A; median answer ~24 hours
Sample Weekend Schedule
Day
Activity
Time
Friday Eve
Lessons 1-6 (linear → buckets)
18:00–21:00
Saturday AM
Lessons 7-10 + DNN lab
09:00–12:30
Saturday PM
Over-fit visual & regularisation
14:00–16:00
Sunday AM
Hyper-param search + best-practice notes
09:00–11:30
Sunday PM
Refactor notebook → GitHub repo, share on LinkedIn
16:00–17:30
Summary
Google's ML Crash Course transforms complex gradient concepts into easy-to-understand visual tools, all within a free, GPU-powered browser environment. While it doesn't provide a formal certificate, the GitHub notebook you create serves as proof of your work and prepares you for more advanced programs like Logicmojo or Udacity. For value and learning quality, MLCC is an excellent second step after getting familiar with AI basics.
- DataCamp — Intro to Deep Learning Quick Overview Table Metric Detail Total length About 40 hours (10 interactive chapters) Weekly load 5 hours × 8 weeks or complete in one holiday week Delivery Fully in-browser IDE with GPU; no installs Tuition ₹12,000 — annual "all-access" DataCamp subscription (covers 400+ other courses) Capstone Train & deploy Keras image classifier on flower dataset Credential DataCamp "Statement of Accomplishment" (PDF + profile badge)
Why This Course Works Well for Beginners
Interactive coding: You complete missing code segments; instant grader tells you if you're correct
Built-in GPU: Keras models run on DataCamp's servers; no need to manage Colab
Game-like progress: XP points, streak badges, and chapter quizzes keep you motivated
Course Content and Projects
Chapter
Theme
Milestone Lab
1
Neural-net anatomy + Keras Sequential
Forward-pass demo on XOR
2
Optimisers & learning rate
Tune SGD vs Adam on Boston Housing
3
Over-/under-fitting & dropout
Add dropout, plot val-loss vs epochs
4
CNN fundamentals
ConvNet reaches 94% on CIFAR-10 subset
5
RNN & sequence data
LSTM predicts Shakespearean next word
6
Transfer Learning
Fine-tune MobileNet on 200 custom images
7
Model interpretability
Grad-CAM heat-maps for a dog-vs-cat model
8
Deployment basics
Export Keras .h5; test Flask inference locally
9
Project setup
Start flower-classifier capstone in guided repo
10
Capstone wrap-up
Achieve ≥ 90% accuracy, push to GitHub Pages
Tools and Environment
Layer
Tool
Beginner Perk
IDE
DataCamp Workspace
Preloaded GPU, auto-save, dark-mode
Library
TensorFlow/Keras 2.15
Latest stable, no pip needed
Grader
Auto-unit tests
Instant feedback, retry unlimited
Version control
Built-in Git push to GitHub
One-click repo creation
Deployment
Flask micro-demo + GitHub Pages
Teaches REST and static hosting basics
Teachers and Support
Content author: Isaiah Hull, PhD (former ECB data scientist) — explains math concepts using animations
In-app chat bot for hints; connects to human tutor if you're stuck for more than 30 minutes
Live events: weekly "Office Hours" webinar; Q&A with DataCamp instructors
Sample Weekly Schedule
Day
Task
Time
Mon / Tue
Watch micro-lessons + mini-quiz
1 hour
Wed
Hands-on chapter lab
2 hours
Thu
Graded challenge
1 hour
Sat
Capstone coding sprint
2 hours
Sun
Review streak dashboard, plan next week
15 min
Summary
DataCamp's Intro to Deep Learning offers a smooth learning experience: GPU-powered browser IDE, small coding exercises, and automatic grading that encourages practice. While the certificate won't guarantee jobs at major tech companies, startups and hiring managers value a polished GitHub project and consistent daily coding habits—making this an excellent, low-risk starting point for beginners who prefer hands-on learning over complex setup.
- Microsoft Learn — Azure AI Fundamentals (AI-900)
Fast-Facts Dashboard
Metric
Detail
Total length
About 4 weeks recommended-pace (18–22 study hours)
Weekly load
4–6 hours self-paced micro-lessons + sandbox labs
Delivery
Microsoft Learn interactive docs, quizzes, and Azure sandboxes
Tuition
Learning content free · Certification exam ₹4,700
Cloud credit
USD 200 Azure credit voucher (activated after first lab)
Credential
Microsoft Certified: Azure AI Fundamentals (AI-900)—adds to official MS transcript
Why Beginners Finish (a.k.a. "Beginner Wins")
No credit-card cloud access — lab steps auto-provision a temporary Azure subscription with USD 200 credit; you can train models without billing anxiety
Exam-ready structure — every unit is mapped to an AI-900 objective; built-in "Knowledge Check" quizzes mirror exam wording
HR-recognised badge — the certification posts directly to your Microsoft transcript and Credly profile, signalling vendor-verified skills even for non-developers
Curriculum Roadmap & Milestones
Week
Theme
Milestone Lab
1
AI workloads & considerations
Identify bias scenarios via MS Responsible AI checklist
2
Computer Vision with Custom Vision
Detect objects—upload 50 images, train and test mAP ≥ 0.75
3
NLP with Azure AI Studio
Build text-analytics pipeline for sentiment & key phrases
4
AI integration & deployment
Deploy vision model to Logic Apps; trigger on blob upload and email JSON response
Passing the built-in practice test with ≥ 85% strongly predicts exam success.
Tool-Chain & Lab Environment
Layer
Service
Beginner Benefit
Vision
Azure Custom Vision
Drag-and-drop GUI, auto-labels small datasets
NLP
Azure Cognitive Services – Text Analytics
REST endpoint created in four clicks
Automation
Logic Apps
No-code workflow; event-driven triggers
DevOps
Azure AI Studio Notebooks
Pre-installed SDK, free GPU tier
Learning
Microsoft Learn Sandbox
Temporary subscription auto-deletes after lab
Support & Faculty Line-up
Role
Contributor
Interaction
Content architects
Microsoft Cloud Advocates team
Author interactive modules; update every quarter
Featured voices
Jen Looper, Seth Juarez
Short "Key Concept" videos clarifying exam topics
Community
AI-900 Study Group (Discord + TechCommunity)
Live "Exam Cram" sessions every fortnight
Q&A
Microsoft Learn forums, tag azure-ai-fundamentals
Median peer reply < 12 hours
Typical Week-on-Week Calendar
Day
Task
Time
Mon / Tue
Read two learning paths; finish mini-quizzes
1.5 hours
Wed
Hands-on sandbox lab (e.g., Custom Vision)
2 hours
Thu
Review flashcards, take Knowledge Check
1 hour
Sat
Practice exam (40 Q) + note weak areas
2 hours
Sun
Community Q&A or Exam Cram replay
30 min
Total ≈ 5–6 hours; four cycles complete the syllabus and practice bank.
Bottom Line
AI-900 is the quickest, lowest-cost enterprise badge a beginner can earn: free coursework, a sub-₹5,000 proctored exam, and a built-in USD 200 Azure sandbox. You finish with a no-code object-detection workflow running in Logic Apps, plus a résumé-visible certification recognised by hiring managers and enterprise HR systems worldwide—an ideal launchpad before deeper Python-centric training.
- Great Learning — Kick-Start AI Fast-Facts Dashboard
Metric
Detail
Total length
4 weeks (8 live evening sessions + self-study)
Weekly load
5 h — 2 × 90-min live classes plus ~2 h practice
Delivery
Zoom classrooms, Slack community, browser notebooks
Tuition
₹ 0 — fully sponsored teaser boot camp
Capstone
Build a Naïve Bayes e-mail spam filter
Credential
“Kick-Start AI” digital badge + GL alumni Slack access
Why Beginners Finish (a.k.a. “Beginner Wins”)
Zero-rupee risk — great for testing your appetite before investing in paid programmes.
Live UT-Austin adjunct — every lecture ends with open-mic Q&A; no guessing in silence.
9 a.m.–9 p.m. Slack TAs — questions answered the same day, even outside class hours.
Curriculum Roadmap & Milestones
Week
Theme
Hands-On Milestone
1
Python crash + Colab primer
Write loops, lists, and a simple CSV parser
2
Exploratory Data Analysis
Complete an EDA challenge on a retail dataset; submit visual report
3
Probability & text pre-processing
Tokenise e-mails, compute TF counts, split train/test
4
Naïve Bayes & model metrics
Achieve ≥ 90 % accuracy spam filter; export to .pkl, share on GitHub
Tool-Chain & Lab Environment
Layer
Tool
Beginner Perk
Notebook
Google Colab (GPU free tier)
No local installs; runs on phone if needed
Data viz
seaborn, matplotlib presets
Templates provided; just tweak
Version control
GitHub Classroom
Auto-checks notebook executes end-to-end
Deployment (optional)
Streamlit share link
One-click app for spam demo
Support & Faculty Line-up
Role
Name & Affiliation
Interaction
Lead instructor
Dr. Daniel Mitchell, UT-Austin adjunct
Teaches live; runs Q&A “office hour” after each class
Slack TAs (4)
Great Learning alumni now in ML roles
Respond 09:00–21:00; tag resolves in < 3 h median
Guest talk
GL PGP-AIML graduate now at Amazon
“How my boot camp capstone became my interview story”
Typical Week-on-Week Calendar
Day
Task
Time
Mon Eve
Live class (lecture + demo)
19:00–20:30
Tue
Review slides, tiny quiz
30 min
Thu Eve
Live coding lab
19:00–20:30
Fri/Sat
Finish worksheet, post to Slack for TA check
1 h
Sun
Optional peer code review session
30 min
Bottom line
Kick-Start AI is a risk-free “taster menu”: four weeks, zero cost, and two concrete deliverables—an EDA notebook and a working spam-filter model. It’s perfect if you want to gauge your interest (and discipline) before committing cash to a longer boot camp such as Logicmojo or Udacity. Plus, the Great Learning alumni Slack remains open after completion, giving you a built-in peer group for the next steps of your AI journey.
9. edX / Harvard — CS50’s Introduction to AI with Python
Fast-Facts Dashboard
Metric
Detail
Total length
10 weeks (~ 60 lecture/lab hours, plus project time)
Weekly load
6–8 h (2 h video, 1 h short quiz, 3–5 h project)
Delivery
Asynchronous on edX; downloadable problem sets
Tuition
Free to audit · Verified certificate ₹ 13 000
Projects
7 graded problem sets + optional final capstone
Credential
Harvard-issued, edX-verified certificate (if paid)
Why Beginners Finish (a.k.a. “Beginner Wins”)
Project-centric pedagogy Every concept immediately becomes code—no “watch only” weeks.
Step-by-step staff solution videos After deadline, watch Brian Yu code the entire project live.
Self-paced grace Deadlines are advisory; you decide when to submit.
Beginner Caveat You must install Python 3, pip, and a text editor locally—ideal practice for real-world dev, but heavier than browser-only courses.
Curriculum Roadmap & Milestones
Week
Core Topic
Project Milestone
1
Search
A* path-finder solves 15-puzzle in seconds
2
Knowledge
Logical inference to solve Knights & Knaves riddles
3
Uncertainty
Heredity: probability of genes & traits via Bayes nets
4
Optimization
Tic-Tac-Toe AI using Minimax with alpha–beta pruning
5
Learning
Shopping — Naïve Bayes predicts purchase intent
6
Language
N-grams text generator writes Shakespeare-ish sentences
7
Network Science
PageRank scores Harvard Gazette hyperlinks
8–10
Personal Capstone
Build & present a project of your choice (optional)
Tool-Chain & Lab Environment
Layer
Tool / Library
Beginner Benefit
Dev env
VS Code / IDE of choice
Staff setup video for Windows/macOS/Linux
Package mgmt
pip, venv
Real-world dependency practice
Libraries
NumPy, scikit-learn, NLTK
Light intro to mainstream stacks
Autograder
CS50 submit + check50
Instant CLI feedback on tests
Version control
GitHub template repo
Teaches commit discipline early
Support & Faculty Line-up
Role
Name
Interaction
Lead lecturer
David J. Malan
High-energy concept videos
Head TF
Brian Yu
Live-coding walkthroughs after deadlines
Community
EdX & Discord forums
Staff + alumni; median peer reply < 24 h
Office Hours
Weekly livestream
Open Q&A; recordings archived
Typical Week-on-Week Calendar
Day
Task
Time
Mon
Watch lecture segment (≈ 45 min)
19:00–19:45
Tue
Short quiz & reading
30 min
Wed/Thu
Start project; outline algorithm
1.5 h
Sat
Finish coding, run check50 tests
3 h
Sun
Submit, watch solution video, reflect
1 h
Bottom Line
CS50 AI gives beginners seven resume-grade repos—from A* search to a Minimax-powered game agent—plus bragging rights of a Harvard course. Setup is heavier than browser-only boot camps, but the payoff is a disciplined real-developer workflow and projects that hiring managers can clone and run. Pair it with a lighter GUI-based intro (e.g., Google MLCC) if you want conceptual intuition before diving into local Python installs.
10. Khan Academy — Intro to Machine Learning
Fast-Facts Dashboard
Metric
Detail
Total length
6 – 8 hours of micro-lessons and quick-checks
Weekly load
2 h × 3–4 evenings, or one Sunday binge
Delivery
100 % in-browser “chalk-talk” videos + interactive widgets
Tuition
Free — no ads, no upsell
Prerequisites
High-school algebra; zero coding required
Credential
In-platform “Course Mastered” badge (non-shareable PDF)
Why Beginners Finish (a.k.a. “Beginner Wins”)
Zero install — lessons play in any browser, even on a phone; sliders and widgets run client-side JavaScript.
Chalkboard story-telling — Sal Khan’s handwriting + gentle voice demystify regression and clustering without intimidating notation.
Gamified progress — every quiz adds energy points; streaks unlock avatars, keeping younger or motivation-sensitive learners engaged.
Curriculum Roadmap & Milestones
Segment
Runtime
Key Concept
Milestone Widget
Unit 1
60 min
What is Machine Learning?
Drag-and-drop “supervised vs unsupervised” sorting game
Unit 2
90 min
Linear Regression
Interactive slider adjusts slope; hit R² ≥ 0.9 on synthetic data
Unit 3
75 min
Classification & decision boundaries
Click-to-add data points, watch boundary update
Unit 4
60 min
k-Means Clustering
Cluster planets by mass & distance; achieve correct grouping
Unit 5
45 min
Bias–variance intuition
Flip a “training size” dial, observe error curves
Final Challenge
45 min
Build a mini spam filter logic tree in pseudo-code
Pass 7/7 test e-mails correctly
Tool-Chain & Lab Environment
Layer
Tool
Beginner Benefit
Video
HTML5 chalk-talk player
Pause & replay frame-by-frame
Widgets
Custom JS + D3 visualisers
Immediate visual feedback; no Python needed
Quizzes
Auto-graded MCQs & drag-tasks
Hints unlocked after one wrong answer
Sandbox
“Spin-off” editor (p5.js)
Optional step for learners ready to code
Support & Faculty Line-up
Role
Instructor
Interaction
Lead narrator
Sal Khan
Voice-over for each chalkboard lesson
Content writers
Khan Academy Computing Team
Answer common questions in thread comments
Community
Discussion below every video
Peer replies typically < 12 h; volunteer moderators filter spam
Typical Week-on-Week Calendar
Day
Task
Time
Mon
Units 1 & 2 videos + quizzes
120 min
Wed
Unit 3 + interactive boundary widget
75 min
Fri
Unit 4 clustering lab
60 min
Sun
Bias–variance lesson + Final spam-filter challenge
90 min
Bottom line
Khan Academy’s Intro to Machine Learning is the “vitamin pill” before heavier boot camps: crystal-clear visuals, zero technical setup, and enough interactive play to cement concepts. You won’t leave with a corporate-ready certificate, but you will grasp the intuition behind regression, classification, and clustering—making the next leap to Python or cloud labs far less daunting.
Extra Value: 30-Day Kick-Off Blueprint
Week 0 Block two evening slots + one weekend slot on your calendar,treat them as immutable meetings with future-you.
Week 1 Finish Coursera AI for Everyone for big-picture context (9 h).
Week 2–3 Do Khan Academy for intuition + Google MLCC’s first five lessons.
Week 4 Choose your paid track (Logicmojo if you need live help; Simplilearn-IBM if you want a brand badge).
Deliverable Push your first model repo—no matter how tiny—to GitHub and share on LinkedIn. Momentum beats perfection.
FAQ for Newcomers
Q: Do I need calculus before starting?
A: No. The courses above teach gradients via code first; you can layer calculus later.
Q: Laptop specs?
A: Any i5/8 GB machine is fine for beginner datasets. Heavier CNN labs use free cloud GPUs (Colab, IBM, Azure).
Q: How soon can I apply for an ML job?
A: Typical graduates land junior DS/ML analyst roles 4–8 months after starting, once they can demo two end-to-end projects.
Closing Thought
The biggest hurdle isn’t eigenvectors; it’s inertia. Pick one path, schedule study blocks like doctor appointments, and deploy something public in the first month. Every bug you squash after that is proof you belong in the AI conversation.
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