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Statistics and Probability | Incognition
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Statistics and Probability
Statistics, probability, linear algebra, and calculus — the bedrock every ML practitioner needs.
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79 lessons
Chapter 1: Introduction to Statistics
0 / 5 lessons
1
What is Statistics?
2
Descriptive vs Inferential Statistics
3
Population vs Sample
4
Parameter vs Statistic
5
Types of Data
Chapter 2: Data Collection & Sampling
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1
Random Sampling
2
Stratified Sampling
3
Cluster Sampling
4
Sampling Bias
5
Experimental vs Observational Studies
Chapter 3: Measures of Central Tendency
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1
Mean
2
Median
3
Mode
4
Weighted Mean
Chapter 4: Measures of Dispersion
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1
Range
2
Variance
3
Standard Deviation
4
Interquartile Range (IQR)
Chapter 5: Data Distribution & Shape
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Frequency & Histograms
2
Distribution Shapes
3
Symmetry & Skewness
4
Percentiles and Quantiles
Chapter 6: Introduction to Probability
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1
Probability Intuition
2
Sample Spaces & Outcomes
3
Event Operations & Rules
4
Conditional Probability & Tables
5
Independence & Multiplication
Chapter 7: Random Variables
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1
Random Variables
2
Discrete Random Variables
3
Continuous Random Variables
4
PMF
5
Probability Density Function (PDF)
6
Cumulative Distribution Function (CDF)
Chapter 8: Probability Distributions
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1
Bernoulli Distribution
2
Binomial Distribution
3
Poisson Distribution
4
Normal Distribution
5
Exponential Distribution
6
Beta Distribution
Chapter 9: Statistical Inference
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Introduction to Statistical Inference
2
Sampling Distributions
3
Standard Error
4
Central Limit Theorem
5
Confidence Intervals
Chapter 10: Hypothesis Testing
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1
Foundations of Hypothesis Testing
2
P-Values & Statistical Significance
3
Type I, Type II Errors & Power
4
Z-Tests & T-Tests
5
ANOVA
Chapter 11: Correlation & Regression
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1
Understanding Correlation
2
Pearson Correlation Coefficient
3
Simple Linear Regression
4
Residuals & Model Evaluation
5
Gradient Descent for Linear Regression
Chapter 12: Statistics for Machine Learning
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1
Statistics in Machine Learning
2
Feature Scaling & Standardization
3
Feature Selection & Statistical Relevance
4
Bias-Variance Tradeoff
5
Data Leakage, Validation & Generalization
Chapter 13: Bayesian Statistics
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1
Bayesian Thinking
2
Prior, Likelihood & Posterior
3
Bayesian Updating
4
MAP Estimation
5
Naive Bayes Classification
Chapter 14: Advanced Probability for AI
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1
Markov Chains
2
Monte Carlo Simulation
3
Entropy & Information
4
Cross-Entropy
5
KL Divergence
Chapter 15: Statistics for Deep Learning
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1
Probabilities in Neural Networks
2
Softmax & Multiclass Probabilities
3
Cross-Entropy Loss in Neural Networks
4
Dropout & Stochastic Regularization
5
Batch Normalization & Activation Statistics
Chapter 16: Time Series Statistics
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1
Introduction to Time Series
2
Trend, Seasonality & Cycles
3
Moving Averages & Smoothing
4
Stationarity & Autocorrelation
5
Forecasting Fundamentals
