Curriculum
7 Sections
29 Lessons
7 Hours
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Diving in with Code
6
2.1
Code env setup and python crash course
19 Minutes
2.2
Getting started with code: Feel of code
12 Minutes
2.3
Foundations, data types and representing data
21 Minutes
2.4
Practical note: one-hot vector encoding
5 Minutes
2.5
Exploring data types in code
12 Minutes
2.6
Central tendency mean, median, mode
20 Minutes
Measures of Spread
2
3.1
Dispersion and spread in data, variance, standard deviation
10 Minutes
3.2
Dispersion exploration through code
11 Minutes
Applications and Rules for Probability
6
4.1
Intro to uncertainty, probability intuition
12 Minutes
4.2
simulating coin flips for probability
17 Minutes
4.3
Dispersion exploration through code
22 Minutes
4.4
Applying conditional probability – Bayes rule
10 Minutes
4.5
Application of Bayes rule in real world – Spam detection
8 Minutes
4.6
Spam detection – implementation issues
10 Minutes
Counting
1
5.1
Rules for counting (Mostly optional)
17 Minutes
Random Variables - Rationale and Applications
7
6.1
Quantifying Events – random variables
10 Minutes
6.2
Two random variables – joint probabilities
14 Minutes
6.3
Distributions – rationale and importance
18 Minutes
6.4
Discrete distributions through code
5 Minutes
6.5
Continuous distributions – probability densities
20 Minutes
6.6
Continuous distributions code
5 Minutes
6.7
Case study – sleep analysis, structure and code
18 Minutes
Visualization In Intuition Building
2
7.1
Visualizing distributions – the road to ML Success
13 Minutes
7.2
Dependence and variance of two random variables
11 Minutes
Applications to the Real World
5
8.1
Expected values – decision making through probabilities
6 Minutes
8.2
Entropy – The most important application of expected values
19 Minutes
8.3
Applying entropy – coding decision trees for machine learning
27 Minutes
8.4
Foundations of Bayesian inference
12 Minutes
8.5
Bayesian inference code through PyMC3
6 Minutes
Applied Probability / Stats for Computer Science, DS and ML
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