Udemy - Probability - Stats - The Foundations Of Machine Learning
- CategoryOther
- TypeTutorials
- LanguageEnglish
- Total size2.4 GB
- Uploaded Byfreecoursewb
- Downloads51
- Last checkedApr. 28th '23
- Date uploadedApr. 27th '23
- Seeders 1
- Leechers47
Infohash : 640531A7ED9BCCBD56CFC675135D9585316FE35F
Probability / Stats: The Foundations Of Machine Learning 
https://DevCourseWeb.com
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English (US) | Size: 2.45 GB | Duration: 6h 41m
Real-world, code-oriented learning for programmers to use prob/stats in all of CS, Data Science and Machine Learning
What you'll learn
Necessary concepts in stats and probability
Important concepts in the subject necessary for Data Science and/or ML
Distributions and their importance
Entropy - the foundation of all Machine Learning
Intro to Bayesian Inference
Applying concepts through code
Exceptional SUPPORT: Questions answered within the day. Try it!
Requirements
Basic coding knowledge
No maths background needed (beyond basic arithmetic)
Crash course of Python provided in the contents
Files:
[ DevCourseWeb.com ] Udemy - Probability - Stats - The Foundations Of Machine Learning- Get Bonus Downloads Here.url (0.2 KB) ~Get Your Files Here ! 1 - Diving in with code
- 1 - Code env setup and Python crash course.mp4 (164.6 MB)
- 2 - Getting started with code Feel of data.mp4 (84.4 MB)
- 3 - Foundations data types and representing data English.srt (34.9 KB)
- 3 - Foundations data types and representing data.mp4 (145.1 MB)
- 4 - Practical note onehot vector encoding English.srt (8.0 KB)
- 4 - Practical note onehot vector encoding.mp4 (29.2 MB)
- 5 - Exploring data types in code English.srt (21.5 KB)
- 5 - Exploring data types in code.mp4 (139.9 MB)
- 6 - Central tendency mean median mode.mp4 (118.5 MB)
- 7 - Section Review Tasks.html (2.2 KB) __MACOSX
- _pdfs (0.2 KB)
- _prob-cs (0.2 KB) pdfs
- _01-intro-to-data.pdf (0.3 KB)
- _02-averages.pdf (0.3 KB)
- _03-variance.pdf (0.3 KB)
- _05-probability.pdf (0.2 KB)
- _06-counting.pdf (0.2 KB)
- _06-prob-rules.pdf (0.3 KB)
- _07-bayes-rule.pdf (0.3 KB)
- _08-random-variables.pdf (0.2 KB)
- _09-distributions.pdf (0.2 KB)
- _10-continuous-distributions.pdf (0.2 KB)
- _11-expected-values.pdf (0.2 KB)
- _12-entropy.pdf (0.2 KB)
- _13-mcmc.pdf (0.2 KB)
- _14-kl-divergence.pdf (0.2 KB)
- _.DS_Store (0.1 KB)
- _.ipynb_checkpoints (0.2 KB)
- _00-basic-python-crash-course.ipynb (0.2 KB)
- _00-nhanes_data_basics.ipynb (0.2 KB)
- _01-data-types.ipynb (0.2 KB)
- _02-studying-variables.ipynb (0.2 KB)
- _03-dispersion.ipynb (0.2 KB)
- _04-coin-flips.ipynb (0.2 KB)
- _05-spam-detection.ipynb (0.2 KB)
- _06-distributions.ipynb (0.2 KB)
- _06-joints-and-marginals.ipynb (0.2 KB)
- _07-decision-tree.ipynb (0.2 KB)
- _08-bayesian-inference.ipynb (0.2 KB)
- _data (0.2 KB) data
- _.ipynb_checkpoints (0.2 KB)
- _nhanes_2015_2016.csv (0.2 KB)
- _sleep_data.csv (0.2 KB)
- _zoo.data (0.2 KB)
- _zoo.names (0.2 KB) ipynb_checkpoints
- _nhanes_2015_2016-checkpoint.csv (0.2 KB)
- _sleep_data-checkpoint.csv (0.2 KB)
- _zoo-checkpoint.data (0.2 KB)
- _00-basic-python-crash-course-checkpoint.ipynb (0.2 KB)
- _00-nhanes_data_basics-checkpoint.ipynb (0.2 KB)
- _01-data-types-checkpoint.ipynb (0.2 KB)
- _02-studying-variables-checkpoint.ipynb (0.2 KB)
- _03-dispersion-checkpoint.ipynb (0.2 KB)
- _04-coin-flips-checkpoint.ipynb (0.2 KB)
- _05-spam-detection-checkpoint.ipynb (0.2 KB)
- _06-distributions-checkpoint.ipynb (0.2 KB)
- _06-joints-and-marginals-checkpoint.ipynb (0.2 KB)
- _07-decision-tree-checkpoint.ipynb (0.2 KB)
- _08-bayesian-inference-checkpoint.ipynb (0.2 KB)
- _Untitled-checkpoint.ipynb (0.2 KB)
- 01-intro-to-data.pdf (2.5 MB)
- 02-averages.pdf (1.0 MB)
- 03-variance.pdf (1,001.3 KB)
- 05-probability.pdf (4.7 MB)
- 06-counting.pdf (5.5 MB)
- 06-prob-rules.pdf (6.7 MB)
- 07-bayes-rule.pdf (6.2 MB)
- 08-random-variables.pdf (5.9 MB)
- 09-distributions.pdf (3.4 MB)
- 10-continuous-distributions.pdf (5.7 MB)
- 11-expected-values.pdf (1.6 MB)
- 12-entropy.pdf (5.0 MB)
- 13-mcmc.pdf (3.1 MB)
- 14-kl-divergence.pdf (2.7 MB)
- 00-basic-python-crash-course.ipynb (46.4 KB)
- 00-nhanes_data_basics.ipynb (115.2 KB)
- 01-data-types.ipynb (9.0 KB)
- 02-studying-variables.ipynb (296.5 KB)
- 03-dispersion.ipynb (87.0 KB)
- 04-coin-flips.ipynb (148.4 KB)
- 05-spam-detection.ipynb (15.9 KB)
- 06-distributions.ipynb (295.6 KB)
- 06-joints-and-marginals.ipynb (258.6 KB)
- 07-decision-tree.ipynb (30.0 KB)
- 08-bayesian-inference.ipynb (2.7 KB)
- DS_Store (6.0 KB) data ipynb_checkpoints
- nhanes_2015_2016-checkpoint.csv (744.5 KB)
- sleep_data-checkpoint.csv (280.6 KB)
- zoo-checkpoint.data (4.0 KB)
- nhanes_2015_2016.csv (744.5 KB)
- sleep_data.csv (280.6 KB)
- zoo.data (4.0 KB)
- zoo.names (2.5 KB) ipynb_checkpoints
- 00-basic-python-crash-course-checkpoint.ipynb (46.4 KB)
- 00-nhanes_data_basics-checkpoint.ipynb (115.2 KB)
- 01-data-types-checkpoint.ipynb (2.8 KB)
- 02-studying-variables-checkpoint.ipynb (296.5 KB)
- 03-dispersion-checkpoint.ipynb (7.1 KB)
- 04-coin-flips-checkpoint.ipynb (9.1 KB)
- 05-spam-detection-checkpoint.ipynb (14.2 KB)
- 06-distributions-checkpoint.ipynb (51.1 KB)
- 06-joints-and-marginals-checkpoint.ipynb (7.2 KB)
- 07-decision-tree-checkpoint.ipynb (30.0 KB)
- 08-bayesian-inference-checkpoint.ipynb (2.7 KB)
- Untitled-checkpoint.ipynb (0.1 KB)
- 10 - Section Review Tasks.html (0.5 KB)
- 8 - Dispersion and spread in data variance standard deviation.mp4 (43.0 MB)
- 9 - Dispersion exploration through code English.srt (16.8 KB)
- 9 - Dispersion exploration thr
Code:
- udp://tracker.torrent.eu.org:451/announce
- udp://tracker.tiny-vps.com:6969/announce
- http://tracker.foreverpirates.co:80/announce
- udp://tracker.cyberia.is:6969/announce
- udp://exodus.desync.com:6969/announce
- udp://explodie.org:6969/announce
- udp://tracker.opentrackr.org:1337/announce
- udp://9.rarbg.to:2780/announce
- udp://tracker.internetwarriors.net:1337/announce
- udp://ipv4.tracker.harry.lu:80/announce
- udp://open.stealth.si:80/announce
- udp://9.rarbg.to:2900/announce
- udp://9.rarbg.me:2720/announce
- udp://opentor.org:2710/announce