CampusX Maths for Machine Learning: study plan and how long it takes
The CampusX playlist is 23 videos and 42.8 hours long. Here's how long it takes at different paces, a week-by-week schedule built from the real videos, and a planner that turns it into your own daily plan.
23 videos · 42.8 hours · CampusX
CampusX's Maths for Machine Learning collects long sessions in Hindi on the maths behind ML: descriptive statistics, distributions, the central limit theorem, hypothesis testing and chi-square tests, a probability crash course, linear algebra (matrices, eigenvectors, SVD) and differential calculus.
Most sessions are one to two and a half hours, so it's best planned a session per day.
Who it's for: Hindi-speaking learners heading into machine learning who need to fill gaps in statistics, probability and linear algebra.
How long does it take?
The full playlist is 42.8 hours across 23 videos. Here's roughly how many days that is at different daily watch times and speeds, counting video time only:
| Daily time | At 1x | At 1.5x | At 2x |
|---|---|---|---|
| 30 min a day | 86 days | 58 days | 43 days |
| 1 h a day | 43 days | 29 days | 22 days |
| 2 h a day | 22 days | 15 days | 11 days |
Make your own schedule
The playlist link is already filled in. Press Plan, set how much time you have each day and your speed, and you'll get a day-by-day schedule with a finish date and a calendar file. It reads the live playlist, so new videos are included.
Week-by-week schedule
A sample plan at 2 hours a day and normal speed, using whole videos so none is split across days. That's 4 weeks in total. For a different pace, start date or speed, use the planner above.
Week 1Videos 1–7 · 12h 20m
Day 1 · 13m
- 1Statistics Roadmap for Data Science and Data Analysis | Complete Guide | Full Resources | CampusX13m
Day 2 · 1h 52m
- 2Session 38 - Descriptive Statistics Part 1 | DSMP 20231h 52m
Day 3 · 1h 56m
- 3Session 39 - Descriptive Statistics Part 2 | DSMP 20231h 56m
Day 4 · 2h 10m
- 4Session 40 - Probability Distribution Functions - PDF, PMF & CDF | DSMP 20232h 10m
Day 5 · 2h 1m
- 5Session 41 - Normal Distribution | DSMP 20232h 1m
Day 6 · 2h 16m
- 6Session 42 - Non-Gaussian Probability Distributions | DSMP 20232h 16m
Day 7 · 1h 52m
- 7Session 43 - Central Limit Theorem | DSMP 20231h 52m
Week 2Videos 8–15 · 13h 45m
Day 8 · 2h 10m
- 8Session 44 - Confidence Intervals | DSMP 20232h 10m
Day 9 · 1h 59m
- 9Session 45 - Hypothesis Testing Part 1 | DSMP 20231h 59m
Day 10 · 2h 12m
- 10Session 46 - Hypothesis Testing Part 2 | p-values | t-tests | DSMP 20232h 12m
Day 11 · 2h 15m
- 11Session on Chi Square Tests2h 15m
Day 12 · 1h 58m
- 12Session on ANOVA | Paid Zoom Session1h 58m
Day 13 · 1h 34m
- 13Master Probability in Data Science: The Ultimate Crash Course! | Part 1 | CampusX1h 34m
Day 14 · 1h 36m
- 14Mastering Probability for ML: Joint | Marginal | Conditional Probability and Bayes' Theorem1h 20m
- 15Linear Algebra Roadmap for Machine Learning and Deep Learning | Complete Guide | CampusX16m
Week 3Videos 16–22 · 14h 19m
Day 15 · 1h 32m
- 16Supercharge Your ML Journey: Mastering Vectors in Linear Algebra - Part 11h 32m
Day 16 · 2h 27m
- 17Linear Algebra Part 2 | Matrices2h 27m
Day 17 · 2h 10m
- 18Linear Algebra Part 3 | Matrices Intuition2h 10m
Day 18 · 1h 41m
- 19Session on Eigen Vectors and Eigen Values | Week 29 | DSMP1h 41m
Day 19 · 2h 7m
- 20Session on Eigen Decomposition + PCA Variants | Week 29 | DSMP2h 7m
Day 20 · 2h 13m
- 21Session on Singular Value Decomposition2h 13m
Day 21 · 2h 9m
- 22Session on Optimization - The Big Picture2h 9m
Week 4Video 23 · 2h 26m
Day 22 · 2h 26m
- 23Session on Differential Calculus2h 26m
Tips for finishing
- Do only the sections you need: the playlist is organised as statistics, probability, linear algebra and calculus.
- Pair the linear algebra sessions with 3Blue1Brown's Essence of Linear Algebra for intuition.