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[ DevCourseWeb com ] Udemy - Probability - Stats - The Foundations Of Machine Learning
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1 - Diving in with code
1 - Code env setup and Python crash course.mp4
2 - Getting started with code Feel of data.mp4
3 - Foundations data types and representing data English.srt
3 - Foundations data types and representing data.mp4
4 - Practical note onehot vector encoding English.srt
4 - Practical note onehot vector encoding.mp4
5 - Exploring data types in code English.srt
5 - Exploring data types in code.mp4
6 - Central tendency mean median mode.mp4
7 - Section Review Tasks.html
__MACOSX
_pdfs
_prob-cs
pdfs
_01-intro-to-data.pdf
_02-averages.pdf
_03-variance.pdf
_05-probability.pdf
_06-counting.pdf
_06-prob-rules.pdf
_07-bayes-rule.pdf
_08-random-variables.pdf
_09-distributions.pdf
_10-continuous-distributions.pdf
_11-expected-values.pdf
_12-entropy.pdf
_13-mcmc.pdf
_14-kl-divergence.pdf
prob-cs
_.DS_Store
_.ipynb_checkpoints
_00-basic-python-crash-course.ipynb
_00-nhanes_data_basics.ipynb
_01-data-types.ipynb
_02-studying-variables.ipynb
_03-dispersion.ipynb
_04-coin-flips.ipynb
_05-spam-detection.ipynb
_06-distributions.ipynb
_06-joints-and-marginals.ipynb
_07-decision-tree.ipynb
_08-bayesian-inference.ipynb
_data
data
_.ipynb_checkpoints
_nhanes_2015_2016.csv
_sleep_data.csv
_zoo.data
_zoo.names
ipynb_checkpoints
_nhanes_2015_2016-checkpoint.csv
_sleep_data-checkpoint.csv
_zoo-checkpoint.data
ipynb_checkpoints
_00-basic-python-crash-course-checkpoint.ipynb
_00-nhanes_data_basics-checkpoint.ipynb
_01-data-types-checkpoint.ipynb
_02-studying-variables-checkpoint.ipynb
_03-dispersion-checkpoint.ipynb
_04-coin-flips-checkpoint.ipynb
_05-spam-detection-checkpoint.ipynb
_06-distributions-checkpoint.ipynb
_06-joints-and-marginals-checkpoint.ipynb
_07-decision-tree-checkpoint.ipynb
_08-bayesian-inference-checkpoint.ipynb
_Untitled-checkpoint.ipynb
pdfs
01-intro-to-data.pdf
02-averages.pdf
03-variance.pdf
05-probability.pdf
06-counting.pdf
06-prob-rules.pdf
07-bayes-rule.pdf
08-random-variables.pdf
09-distributions.pdf
10-continuous-distributions.pdf
11-expected-values.pdf
12-entropy.pdf
13-mcmc.pdf
14-kl-divergence.pdf
prob-cs
00-basic-python-crash-course.ipynb
00-nhanes_data_basics.ipynb
01-data-types.ipynb
02-studying-variables.ipynb
03-dispersion.ipynb
04-coin-flips.ipynb
05-spam-detection.ipynb
06-distributions.ipynb
06-joints-and-marginals.ipynb
07-decision-tree.ipynb
08-bayesian-inference.ipynb
DS_Store
data
ipynb_checkpoints
nhanes_2015_2016-checkpoint.csv
sleep_data-checkpoint.csv
zoo-checkpoint.data
nhanes_2015_2016.csv
sleep_data.csv
zoo.data
zoo.names
ipynb_checkpoints
00-basic-python-crash-course-checkpoint.ipynb
00-nhanes_data_basics-checkpoint.ipynb
01-data-types-checkpoint.ipynb
02-studying-variables-checkpoint.ipynb
03-dispersion-checkpoint.ipynb
04-coin-flips-checkpoint.ipynb
05-spam-detection-checkpoint.ipynb
06-distributions-checkpoint.ipynb
06-joints-and-marginals-checkpoint.ipynb
07-decision-tree-checkpoint.ipynb
08-bayesian-inference-checkpoint.ipynb
Untitled-checkpoint.ipynb
2 - Measures of Spread
10 - Section Review Tasks.html
8 - Dispersion and spread in data variance standard deviation.mp4
9 - Dispersion exploration through code English.srt
9 - Dispersion exploration through code.mp4
3 - Applications and Rules for Probability
11 - Intro to uncertainty probability intuition.mp4
12 - Simulating coin flips for probability.mp4
13 - Conditional probability the most important concept in stats English.srt
13 - Conditional probability the most important concept in stats.mp4
14 - Applying conditional probability Bayes rule English.srt
14 - Applying conditional probability Bayes rule.mp4
15 - Application of Bayes rule in real world Spam detection English.srt
15 - Application of Bayes rule in real world Spam detection.mp4
16 - Spam detection implementation issues English.srt
16 - Spam detection implementation issues.mp4
17 - Section Review Tasks.html
4 - Counting
18 - Rules for counting Mostly optional English.srt
18 - Rules for counting Mostly optional.mp4
19 - Section Review Tasks.html
5 - Random Variables Rationale and Applications
20 - Quantifying events random variables.mp4
21 - Two random variables joint probabilities English.srt
21 - Two random variables joint probabilities.mp4
22 - Distributions rationale and importance English.srt
22 - Distributions rationale and importance.mp4
23 - Discrete distributions through code English.srt
23 - Discrete distributions through code.mp4
24 - Continuous distributions probability densities English.srt
24 - Continuous distributions probability densities.mp4
25 - Continuous distributions code English.srt
25 - Continuous distributions code.mp4
26 - Case study sleep analysis structure and code English.srt
26 - Case study sleep analysis structure and code.mp4
27 - Section Review Tasks.html
6 - Visualization in Intuition Building
28 - Visualizing joint distributions the road to ML success.mp4
29 - Dependence and variance of two random variables English.srt
29 - Dependence and variance of two random variables.mp4
30 - Section Review Tasks.html
7 - Applications to the Real World
31 - Expected values decision making through probabilities English.srt
31 - Expected values decision making through probabilities.mp4
32 - Entropy The most important application of expected values.mp4
33 - Applying entropy coding decision trees for machine learning English.srt
33 - Applying entropy coding decision trees for machine learning.mp4
34 - Foundations of Bayesian inference English.srt
34 - Foundations of Bayesian inference.mp4
35 - Bayesian inference code through PyMC3 English.srt
35 - Bayesian inference code through PyMC3.mp4
36 - Section Review Tasks.html
8 - Extra Resources
37 - Bonus Lecture.html
Bonus Resources.txt
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