Data Science & Machine Learning: Naive Bayes in Python [1/2025, ENG] torrent

Обучающие видео » Компьютерные видеоуроки и обучающие интерактивные DVD » Machine/Deep Learning, Neural Networks

Data Science & Machine Learning: Naive Bayes in Python

Год выпуска: 1/2025
Производитель: Udemy
Сайт производителя: https://www.udemy.com/course/data-science-machine-learning-naive-bayes-in-python/
Автор: Lazy Programmer Inc.
Продолжительность: 7h 29m 1s
Тип раздаваемого материала: Видеоурок
Язык: Английский
Субтитры: Английский
Описание:

What you'll learn Apply Naive Bayes to image classification (Computer Vision)
Apply Naive Bayes to text classification (NLP)
Apply Naive Bayes to Disease Prediction, Genomics, and Financial Analysis
Understand Naive Bayes concepts and algorithm
Implement multiple Naive Bayes models from scratch Requirements Decent Python programming skills
Experience with Numpy, Matplotlib, and Pandas (we'll be using these)
For advanced portions: know probability Description

In this self-paced course, you will learn how to apply Naive Bayes to many real-world datasets in a wide variety of areas, such as: computer vision
natural language processing
financial analysis
healthcare
genomics Why should you take this course? Naive Bayes is one of the fundamental algorithms in machine learning, data science, and artificial intelligence. No practitioner is complete without mastering it.

This course is designed to be appropriate for all levels of students, whether you are beginner, intermediate, or advanced. You'll learn both the intuition for how Naive Bayes works and how to apply it effectively while accounting for the unique characteristics of the Naive Bayes algorithm. You'll learn about when and why to use the different versions of Naive Bayes included in Scikit-Learn, including GaussianNB, BernoulliNB, and MultinomialNB.

In the advanced section of the course, you will learn about how Naive Bayes really works under the hood. You will also learn how to implement several variants of Naive Bayes from scratch, including Gaussian Naive Bayes, Bernoulli Naive Bayes, and Multinomial Naive Bayes. The advanced section will require knowledge of probability, so be prepared!

Thank you for reading and I hope to see you soon!

Suggested Prerequisites: Decent Python programming skill
Comfortable with data science libraries like Numpy and Matplotlib
For the advanced section, probability knowledge is required WHAT ORDER SHOULD I TAKE YOUR COURSES IN? Check out the lecture "Machine Learning and AI Prerequisite Roadmap" (available in the FAQ of any of my courses, including my free course) UNIQUE FEATURES Every line of code explained in detail - email me any time if you disagree
Less than 24 hour response time on Q&A on average
Not afraid of university-level math - get important details about algorithms that other courses leave out Who this course is for: Beginner Python developers curious about data science and machine learning
Students and professionals interested in machine learning fundamentals Формат видео: MP4
Видео: avc, 1280x720, 16:9, 30.000 к/с, 251 кб/с
Аудио: aac lc sbr, 44.1 кгц, 62.8 кб/с, 2 аудио
[spoiler="Изменения/Changes"]

[/spoiler]
[spoiler="MediaInfo"]
General
Complete name : D:\2\Udemy - Data Science & Machine Learning Naive Bayes in Python (1.2025)\07. Effective Learning Strategies for Machine Learning (AppendixFAQ by Student Requ\2. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4
Format : MPEG-4
Format profile : Base Media
Codec ID : isom (isom/iso2/avc1/mp41)
File size : 50.6 MiB
Duration : 22 min 4 s
Overall bit rate : 320 kb/s
Frame rate : 30.000 FPS
Recorded date : 2025-02-04 12:36:35.2180940+03:30
Writing application : Lavf61.9.100

Video
ID : 1
Format : AVC
Format/Info : Advanced Video Codec
Format profile : [email protected]
Format settings : CABAC / 4 Ref Frames
Format settings, CABAC : Yes
Format settings, Reference frames : 4 frames
Format settings, GOP : M=4, N=60
Codec ID : avc1
Codec ID/Info : Advanced Video Coding
Duration : 22 min 4 s
Bit rate : 251 kb/s
Nominal bit rate : 600 kb/s
Width : 1 280 pixels
Height : 720 pixels
Display aspect ratio : 16:9
Frame rate mode : Constant
Frame rate : 30.000 FPS
Color space : YUV
Chroma subsampling : 4:2:0
Bit depth : 8 bits
Scan type : Progressive
Bits/(Pixel*Frame) : 0.009
Stream size : 39.6 MiB (78%)
Writing library : x264 core 164 r3095 baee400
Encoding settings : cabac=1 / ref=3 / deblock=1:0:0 / analyse=0x1:0x111 / me=umh / subme=6 / psy=1 / psy_rd=1.00:0.00 / mixed_ref=1 / me_range=16 / chroma_me=1 / trellis=1 / 8x8dct=0 / cqm=0 / deadzone=21,11 / fast_pskip=1 / chroma_qp_offset=-2 / threads=22 / lookahead_threads=3 / sliced_threads=0 / nr=0 / decimate=1 / interlaced=0 / bluray_compat=0 / constrained_intra=0 / bframes=3 / b_pyramid=2 / b_adapt=1 / b_bias=0 / direct=1 / weightb=1 / open_gop=0 / weightp=2 / keyint=60 / keyint_min=6 / scenecut=0 / intra_refresh=0 / rc_lookahead=60 / rc=cbr / mbtree=1 / bitrate=600 / ratetol=1.0 / qcomp=0.60 / qpmin=0 / qpmax=69 / qpstep=4 / vbv_maxrate=600 / vbv_bufsize=1200 / nal_hrd=none / filler=0 / ip_ratio=1.40 / aq=1:1.00
Codec configuration box : avcC

Audio
ID : 2
Format : AAC LC SBR
Format/Info : Advanced Audio Codec Low Complexity with Spectral Band Replication
Commercial name : HE-AAC
Format settings : Explicit
Codec ID : mp4a-40-2
Duration : 22 min 4 s
Bit rate mode : Constant
Bit rate : 62.8 kb/s
Channel(s) : 2 channels
Channel layout : L R
Sampling rate : 44.1 kHz
Frame rate : 21.533 FPS (2048 SPF)
Compression mode : Lossy
Stream size : 9.91 MiB (20%)
Title : default
Default : Yes
Alternate group : 1
[/spoiler]
[spoiler="Скриншоты"]

[/spoiler]