Udemy – Data Science 2020 : Complete Data Science & Machine Learning 2020-3

Description

Data Science 2020: Complete Data Science & Machine Learning is the name of a training course from Udemy that introduces you to machine learning and data science and shows you how to use Python, math, and statistics. Machine learning and data science are some of the most popular skills of the day that are a little difficult to learn. Many students are looking for a comprehensive course to explain all the topics related to machine learning and data science. If you are one of these students, do not miss this course.

This course with more than 250 lessons and 25 hours of practical training in machine learning and data science will help you to fully learn these topics and prepare for the hot market of artificial intelligence. Today, machine learning and data science are used in many industries, including automotive, banking, healthcare, media, etc., and allow these industries to grow in a variety of ways. In this course, you will see a lot of practical exercises and learn valuable skills.

Items that will be taught in this course

Full acquaintance with machine learning and data science

Learn Python programming from scratch

Familiarity with algebra, arithmetic, and mathematics for data science

Complete statistics training

Data analysis

Understand regression

Familiarity with classification algorithms

Data Science 2020 course specifications: Complete Data Science & Machine Learning

English language

Duration: 26 hours and 14 minutes

Number of courses: 281

Level of education: Intermediate

Instructor: Jitesh Khurkhuriya, Jiteshs Data Science & Machine Learning A-Z Team

File format: mp4

Topics

281 lectures 26:14:51

Introduction 4 lectures 14:20

— Part 1: Essential Python Programming — 21 lectures 01:42:41

— Part 2: Essential Mathematics — 28 lectures 02:30:24

What is Data Science and Machine Learning? 8 lectures 42:00

— Part 3: Essential Statistics — 1 lecture 00:24

Descriptive Statistics 5 lectures 27:28

Data Visualization 20 lectures 01:29:55

Inferential Statistics, Distributions and Hypothesis 14 lectures 02:19:19

— Part 4: Data Pre-Processing — 12 lectures 01:18:31

— Part 5: Regression ——– 1 lecture 00:08

Simple Linear Regression 9 lectures 46:17

Multiple Linear Regression 14 lectures 01:16:23

Project 4 – Kaggle Bike Demand Predictions 16 lectures 01:45:08

— Part 6: Classification ——— 1 lecture 00:20

Logistic Regression 9 lectures 42:32

Support Vector Machines (SVM) 11 lectures 58:16

Decision Trees 7 lectures 39:01

Random Forest 3 lectures 14:38

Evaluate Classification Models 7 lectures 50:33

— Part 7: Feature Selection —— 1 lecture 00:18

Univariate Feature Selection 13 lectures 01:08:51

Recursive Feature Elimination 5 lectures 29:21

— Part 8: Dimensionality Reduction — 5 lectures 31:42

—- Part 9 – Regularization —- 12 lectures 01:06:50

—- Part 10 – Model Selection —– 1 lecture 00:39

Cross Validation for Model Selection 6 lectures 29:13

Hyperparameter Tuning for Model Selection 11 lectures 56:01

— Part 11: Deep Learning —- 25 lectures 02:28:43

—- Part 12 – Clustering or Cluster Analysis —- 11 lectures 01:04:52

Prerequisite

No prerequisites. I will teach right from basics in Python to Advanced Deep Learning

Passion to deal with data analysis

Installation

After Extract, watch with your favorite Player.

English subtitle

Quality: 720p