Chitra

Full Stack developer · Learning AI & ML in public

[Chitra @0xchitra]

I’ve finalized my roadmap to AI/ML.
I will follow it strictly and document my journey publicly what I learn, build, break, and fix.
If you’re in AI & Machine Learning , I’d appreciate any guidance, feedback, or mentorship.

[Chitra @0xchitra]

I’ve decided to start learning AI & Machine Learning. Currently researching a beginner friendly roadmap and structuring my fundamentals properly.

Open to suggestions and guidance from those experienced in this space.

[Chitra @0xchitra]

@Ashutosh_7x7 🤣

[Chitra @0xchitra]

@nandantechtwts Boih! Telusko should be in good rank

[Chitra @0xchitra]

Day 87 of learning AI/ML

I studied Inference for regression slope

• Inference about slope (linear regression)
• Conditions for valid inference
• Confidence interval for slope
• t-statistic for slope
• Using p-value to conclude

#LearnInPublic #AI #ML

[Chitra @0xchitra]

Day 86 of learning AI/ML

I studied Chi-square tests (tables & association)

• Frequency & contingency tables
• Chi-square test for homogeneity
• Chi-square test for independence
• Testing relationships between variables

#LearnInPublic #AI #ML

[Chitra @0xchitra]

@Surendar__05 @isha_singh06 Finally someone who hates mongodb

[Chitra @0xchitra]

Day 85 of learning AI/ML

I studied Chi-square tests (categorical data)

• Inference for categorical data
• Chi-square distribution (intro)
• Goodness-of-fit test
• Chi-square statistic
• Interpreting results

#LearnInPublic #AI #ML

[Chitra @0xchitra]

Day 84 of learning AI/ML

I studied Comparing means

• Statistical significance (real example)
• Difference of sample means distribution
• Confidence interval for difference of means
• Hypothesis test for difference of means

#LearnInPublic #AI #ML

[Chitra @0xchitra]

Day 83 of learning AI/ML

I studied Comparing population proportions

• Comparing two population proportions
• Hypothesis testing for proportions
• Interpreting statistical significance
• Drawing conclusions from experiments

#LearnInPublic #AI #ML

[Chitra @0xchitra]

Day 82 of learning AI/ML

I studied Hypothesis testing (summary)

• Hypothesis testing & p-values
• One-tailed vs two-tailed tests
• z vs t statistics
• Small vs large sample tests
• Proportion hypothesis testing

#LearnInPublic #AI #ML

[Chitra @0xchitra]

Day 81 of learning AI/ML

I studied Hypothesis testing for a mean

• Writing hypotheses (mean)
• Conditions for t-test
• When to use z vs t
• Calculating t-statistic
• Finding & comparing p-values
• Making conclusions from test

#LearnInPublic #AI #ML

[Chitra @0xchitra]

Day 80 of learning AI/ML

I studied Hypothesis testing for proportions

• Constructing null & alternative hypotheses
• Conditions for z-test (proportion)
• Calculating p-value from z-score
• Making conclusions from test results

#LearnInPublic #AI #ML

[Chitra @0xchitra]

Day 79 of learning AI/ML

I studied Hypothesis testing (errors & power)

• Type I error, type II error (false negative)
• Power of a test (detecting true effect)
• Trade-off between errors & significance
• Real-world consequences of decisions

#LearnInPublic #AI #ML

[Chitra @0xchitra]

Day 78 of learning AI/ML

I studied Hypothesis testing
@khanacademy Unit 12

• Idea behind hypothesis testing
• Null vs alternative hypothesis
• p-values & significance levels
• Estimating p-values (simulation)
• Using p-values to draw conclusions

#LearnInPublic #AI #ML

[Chitra @0xchitra]

Day 76 of learning AI/ML

I studied t-intervals for mean

• Constructing t-interval for a mean
• Paired data confidence intervals
• Interpreting confidence intervals
• Sample size vs margin of error
• Small sample t-intervals

#LearnInPublic #AI #ML

[Chitra @0xchitra]

Day 75 of learning AI/ML

I studied t-distribution & inference
@khanacademy Unit 11

• Intro to t-statistics
• Why t is used (simulation insight)
• Conditions for valid t-intervals
• Inference on a mean
• Finding critical t-values

#LearnInPublic #AI #ML

[Chitra @0xchitra]

Day 74 of learning AI/ML

I studied Confidence intervals for proportions

• Margin of error & examples
• Conditions for valid CI (proportion)
• Critical value & confidence level
• Constructing & interpreting z-interval
• Sample size vs margin of error

#LearnInPublic #AI #ML

[Chitra @0xchitra]

Day 73 of learning AI/ML

I studied Confidence intervals
@khanacademy Unit 11

• Confidence intervals & margin of error
• Confidence interval simulation
• Interpreting confidence levels
• Understanding what confidence intervals mean

#LearnInPublic #AI #ML

[Chitra @0xchitra]

Day 72 of learning AI/ML

I studied
Sampling distribution of sample mean

• Inferring population mean from sample mean
• Central Limit Theorem (CLT)
• Standard error of the mean
• Mean & std of sample means
• Finding probabilities using sample means

#LearnInPublic #AI #ML

[Chitra @0xchitra]

Day 71 of learning AI/ML

I studied Sampling distribution of proportions

• Sampling distribution of sample proportion
• Conditions for normal approximation
• Mean & standard deviation of proportions
• Finding probabilities using sample proportions

#LearnInPublic #AI #ML

[Chitra @0xchitra]

Day 70 of learning AI/ML

I studied Sampling distributions
@khanacademy Unit 10

• Intro to sampling distributions
• Sampling statistic bias
• Biased vs unbiased estimators

#LearnInPublic #AI #ML