Description
Complete Machine Learning & Data Science with Python | AZ is a zero to one hundred machine learning and data science course in Python programming language published by Udemi Academy. Python is a high-level, multi-purpose language used in various fields such as artificial intelligence, machine learning, data analysis, and more.
Many libraries such as Scikit, NumPy, Pandas, Matplotlib, and Seaborn have been developed based on the Python programming language, each of which is used in various fields such as data analysis and illustration. Machine learning is an emerging and revolutionary technology that we have seen simple and practical examples in the capabilities of Auto-Correct, Predictive text, and voice recognition.
This technology is evolving day by day and has opened its feet to various industries and platforms such as computer games, digital marketing, programming, and so on.
Smart virtual assistants such as Alexa and Siri use machine learning algorithms and mathematical models in Amazon and Apple products. It is predicted that by 2026 there will be about 11.5 million vacancies in the field of data science and machine learning, which promises a bright future for these modern technologies.
Machine-based learning systems are a set of intelligent and decision-making systems that practice a specific mathematical model based on real data and can then make new decisions and make effective predictions. For example, to build a face recognition system, in the first step, we enter a large number of images and faces into the model and enter the desired information. After reviewing the information, the system quickly detects patterns between the information and can make decisions in the future and in new situations completely automatically.
See Also:
Udemy – The Complete Python Masterclass: Build 15 Python Projects 2021
Udemy – A deep understanding of deep learning (with Python intro) 2021
Udemy – Data Analytics Real-World Projects in Python 2020
Udemy – Python Programming Bootcamp 2021
Udemy – Complete NodeJS Developer in 2022 (GraphQL, MongoDB, + more) 2021
What is the Complete Machine Learning & Data Science course with Python | You will learn AZ:
- Machine-based learning systems and their applications and advantages
- Specialized vocabulary machine learning and data science
- Measurement and evaluation criteria and models
- Statistical classification (Classification) And regression analysis
- Development of machine learning systems based on Python
- Supervised Learning
- Cross-Validation Method
- Bias-Variance Tradeoff
- Illustration and display of data in the form of graphic models with Matplotlib and seaborn libraries
- Machine learning with SciKit
- Linear Regression Theory
- Logistic Regression Theory
- K Nearest Neighbors Algorithm
- Decision Trees
- Random Forest Algorithm
- Support Vector Machines Algorithm
- K Means Clustering Algorithm
- Hierarchical Clustering Algorithm
- Principal Component Analysis
- Recommender System
- Syntax Python programming language
- Other applications of Python programming language
Course specifications
Publisher: Udemi
Instructor: Oak Academy
Language: English
Education Level: Basic to Advanced
Number of Courses: 46
Duration: 8 hours and 43 minutes
Course topics on 2021/11
Prerequisites for Complete Machine Learning & Data Science with Python | AZ
Basic knowledge of Python Programming Language
Be Able To Operate & Install Software On A Computer
Free software and tools used during the machine learning az course
Determination to learn machine learning and patience.
Motivation to learn the second largest number of job postings relative program language among all others
Data visualization libraries in python such as seaborn, matplotlib
Curiosity for machine learning python
Desire to learn Python
Desire to work on python machine learning
Desire to learn matplotlib
Desire to learn pandas
Desire to learn NumPy
Desire to work on seaborn
Desire to learn machine learning az, complete machine learning
Course pictures
Installation guide
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English subtitle
Quality: 720p
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Download Part 1 – 1 GB
Download Part 2 – 1 GB
Download Section 3 – 848 MB
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