Machine Learning & Artificial Intelligence | Data Science Free Courses

✅ Statistics & Probability Cheatsheet 📚🧠 📌 Descriptive Statistics: ⦁  Mean = (Σx) / n ⦁  Median = Middle value ⦁  Mode = Most frequent value ⦁  Variance (σ²) = Σ(x - μ)² / n ⦁  Std Dev (σ) = √Variance ⦁  Range = Max - Min ⦁  IQR = Q3 - Q1 📌 Probability Basics: ⦁  P(A) = Outcomes A / Total Outcomes ⦁  P(A ∩ B) = P(A) × P(B) (if independent) ⦁  P(A ∪ B) = P(A) + P(B) - P(A ∩ B) ⦁  Conditional: P(A|B) = P(A ∩ B) / P(B) ⦁  Bayes’ Theorem: P(A|B) = [P(B|A) × P(A)] / P(B) 📌 Common Distributions: ⦁  Binomial (fixed trials) ⦁  Normal (bell curve) ⦁  Poisson (rare events over time) ⦁  Uniform (equal probability) 📌 Inferential Stats: ⦁  Z-score = (x - μ) / σ ⦁  Central Limit Theorem: sampling dist ≈ Normal ⦁  Confidence Interval: CI = x‌ ± z*(σ/√n) 📌 Hypothesis Testing: ⦁  H₀ = No effect; H₁ = Effect present ⦁  p-value < α → Reject H₀ ⦁  Tests: t-test (small samples), z-test (known σ), chi-square (categorical data) 📌 Correlation: ⦁  Pearson: linear relation (–1 to 1) ⦁  Spearman: rank-based correlation 🧪 Tools to Practice:  Python packages: scipy.stats, statsmodels, pandas  Visualization: seaborn, matplotlib 💡 Quick tip: Use these formulas to crush interviews and build solid ML foundations! 💬 Tap ❤️ for more

👑 Types of Machine Learning