Machine Learning & Data Science with Python, Kaggle & Pandas

ดาวน์โหลดคอร์สเรียน Machine Learning & Data Science with Python, Kaggle & Pandas ฟรี

ข้อมูลไฟล์ (File Info) : Development / Data Science / Machine Learning

Published 4/2023
Created by Oak Academy
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 217 Lectures ( 29h 39m ) | Size: 9 GB

Machine Learning A-Z course from zero with Python, Kaggle, Pandas and Numpy for data analysis with hands-on examples

สิ่งที่คุณจะได้เรียนรู้ (What you’ll learn) :

• Machine learning isn’t just useful for predictive texting or smartphone voice recognition. Machine learning is constantly being applied to new industries.
• Learn Machine Learning with Hands-On Examples
• What is Machine Learning?
• Machine Learning Terminology
• Evaluation Metrics
• What are Classification vs Regression?
• Evaluating Performance-Classification Error Metrics
• Evaluating Performance-Regression Error Metrics
• Supervised Learning
• Cross Validation and Bias Variance Trade-Off
• Use matplotlib and seaborn for data visualizations
• Machine Learning with SciKit Learn
• Linear Regression Algorithm
• Logistic Regresion Algorithm
• K Nearest Neighbors Algorithm
• Decision Trees And Random Forest Algorithm
• Support Vector Machine Algorithm
• Unsupervised Learning
• K Means Clustering Algorithm
• Hierarchical Clustering Algorithm
• Principal Component Analysis (PCA)
• Recommender System Algorithm
• Python instructors on OAK Academy specialize in everything from software development to data analysis, and are known for their effective.
• Python is a general-purpose, object-oriented, high-level programming language.
• Python is a multi-paradigm language, which means that it supports many programming approaches. Along with procedural and functional programming styles
• Python is a widely used, general-purpose programming language, but it has some limitations. Because Python is an interpreted, dynamically typed language
• Python is a general programming language used widely across many industries and platforms. One common use of Python is scripting, which means automating tasks.
• Python is a popular language that is used across many industries and in many programming disciplines. DevOps engineers use Python to script website.
• Python has a simple syntax that makes it an excellent programming language for a beginner to learn. To learn Python on your own, you first must become familiar
• Machine learning describes systems that make predictions using a model trained on real-world data.
• Machine learning is being applied to virtually every field today. That includes medical diagnoses, facial recognition, weather forecasts, image processing.
• It’s possible to use machine learning without coding, but building new systems generally requires code.
• Python is the most used language in machine learning. Engineers writing machine learning systems often use Jupyter Notebooks and Python together.
• Machine learning is generally divided between supervised machine learning and unsupervised machine learning. In supervised machine learning.
• Machine learning is one of the fastest-growing and popular computer science careers today. Constantly growing and evolving.
• Machine learning is a smaller subset of the broader spectrum of artificial intelligence. While artificial intelligence describes any “intelligent machine”
• A machine learning engineer will need to be an extremely competent programmer with in-depth knowledge of computer science, mathematics, data science.
• Python machine learning, complete machine learning, machine learning a-z

ข้อกำหนด (Requirements) :

• Basic knowledge of Python Programming Language
• Be Able To Operate & Install Software On A Computer
• Free software and tools used during the machine learning a-z course
• Determination to learn machine learning and patience.
• Motivation to learn the 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 learn matplotlib
• Desire to learn pandas and numpy
• Desire to learn machine learning a-z, complete machine learning
• Any device you can watch the course, such as a mobile phone, computer or tablet.
• Watching the lecture videos completely, to the end and in order.
• Nothing else! It’s just you, your computer and your ambition to get started today.
• LIFETIME ACCESS, course updates, new content, anytime, anywhere, on any device.

คำอธิบาย (Description) :

Hello there,

Welcome to the ” Machine Learning & Data Science with Python, Kaggle & Pandas “ Course

Machine Learning A-Z course from zero with Python, Kaggle, Pandas and Numpy for data analysis with hands-on examples

Machine learning is a branch of artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy.

You can develop the foundational skills you need to advance to building neural networks and creating more complex functions through the Python and R programming languages. Machine learning helps you stay ahead of new trends, technologies, and applications in this field.

Machine learning is being applied to virtually every field today. That includes medical diagnoses, facial recognition, weather forecasts, image processing, and more. In any situation in which pattern recognition, prediction, and analysis are critical, machine learning can be of use. Machine learning is often a disruptive technology when applied to new industries and niches. Machine learning engineers can find new ways to apply machine learning technology to optimize and automate existing processes. With the right data, you can use machine learning technology to identify extremely complex patterns and yield highly accurate predictions.

It’s hard to imagine our lives without machine learning. Predictive texting, email filtering, and virtual personal assistants like Amazon’s Alexa and the iPhone’s Siri, are all technologies that function based on machine learning algorithms and mathematical models. Python, machine learning, django, python programming, machine learning python, python for beginners, data science. Kaggle, statistics, r, python data science, deep learning, python programming, django, machine learning a-z, data scientist, python for data science

Pandas is an open source Python package that is most widely used for data science/data analysis and machine learning tasks. Pandas is built on top of another package named Numpy, which provides support for multi-dimensional arrays.

Pandas is mainly used for data analysis and associated manipulation of tabular data in DataFrames. Pandas allows importing data from various file formats such as comma-separated values, JSON, Parquet, SQL database tables or queries, and Microsoft Excel. data analysis, pandas, numpy, numpy stack, numpy python, python data analysis, python, Python numpy, data visualization, pandas python, python pandas, python for data analysis, python data, data visualization.

…

คอร์สนี้เหมาะกับใคร (Who this course is for) :

• Anyone who wants to start learning “Machine Learning”
• Anyone who needs a complete guide on how to start and continue their career with machine learning
• Students Interested in Beginning Data Science Applications in Python Environment
• People Wanting to Specialize in Anaconda Python Environment for Data Science and Scientific Computing
• Students Wanting to Learn the Application of Supervised Learning (Classification) on Real Data Using Python
• Anyone eager to learn python for data science and machine learning bootcamp with no coding background
• Anyone who plans a career in data scientist,
• Software developer whom want to learn python,
• Anyone interested in machine learning a-z
• People who want to become data scientist
• Poeple who want tp learn complete machine learning

เนื้อหาหลักสูตร (Overview) :

Installations
5 lectures • 32min

NumPy Library Introduction
2 lectures • 22min

Creating NumPy Array in Python
9 lectures • 41min

Functions in the NumPy Library
7 lectures • 42min

Indexing, Slicing, and Assigning NumPy Arrays
9 lectures • 1hr 5min

Operations in Numpy Library
4 lectures • 35min

Pandas Library Introduction
2 lectures • 7min

Series Structures in the Pandas Library
7 lectures • 49min

DataFrame Structures in Pandas Library
4 lectures • 19min

Element Selection Operations in DataFrame Structures
6 lectures • 47min

Structural Operations on Pandas DataFrame
6 lectures • 53min

Multi-Indexed DataFrame Structures
3 lectures • 22min

Structural Concatenation Operations in Pandas DataFrame
6 lectures • 58min

Functions That Can Be Applied on a DataFrame
9 lectures • 1hr 41min

Pivot Tables in Pandas Library
2 lectures • 19min

File Operations in Pandas Library
5 lectures • 34min

First Contact with Machine Learning
5 lectures • 15min

Evaluation Metrics in Machine Learning
4 lectures • 49min

Supervised Learning with Machine Learning
1 lecture • 5min

Linear Regression Algorithm in Machine Learning A-Z
5 lectures • 1hr 22min

Bias Variance Trade-Off in Machine Learning
1 lecture • 11min

Logistic Regression Algorithm in Machine Learning A-Z
6 lectures • 1hr 2min

K-fold Cross-Validation in Machine Learning A-Z
2 lectures • 11min

K Nearest Neighbors Algorithm in Machine Learning A-Z
4 lectures • 34min

Hyperparameter Optimization
2 lectures • 16min

Decision Tree Algorithm in Machine Learning A-Z
6 lectures • 44min

Random Forest Algorithm in Machine Learning A-Z
3 lectures • 20min

Support Vector Machine Algorithm in Machine Learning A-Z
5 lectures • 38min

Unsupervised Learning with Machine Learning
1 lecture • 4min

K Means Clustering Algorithm in Machine Learning A-Z
5 lectures • 32min

Hierarchical Clustering Algorithm in machine learning data science
3 lectures • 18min

Principal Component Analysis (PCA) in Machine Learning A-Z
4 lectures • 23min

Recommender System Algorithm in Machine Learning A-Z
2 lectures • 9min

First Contact with Kaggle
5 lectures • 47min

Competition Section on Kaggle
2 lectures • 44min

Dataset Section on Kaggle
1 lecture • 16min

Code Section on Kaggle
3 lectures • 47min

Discussion Section on Kaggle
1 lecture • 6min

Other Most Used Options on Kaggle
3 lectures • 27min

Details on Kaggle
4 lectures • 32min

Introduction to Machine Learning with Real Hearth Attack Prediction Project
6 lectures • 1hr 6min

First Organization
3 lectures • 23min

Preparation For Exploratory Data Analysis (EDA)
4 lectures • 41min

Exploratory Data Analysis (EDA) – Uni-variate Analysis
5 lectures • 58min

Exploratory Data Analysis (EDA) – Bi-variate Analysis
14 lectures • 1hr 56min

Preparation for Modelling in Machine Learning
11 lectures • 1hr 14min

Modelling for machine learning
8 lectures • 1hr 1min

Conclusion
1 lecture • 4min

Extra
1 lecture • 1min

📋 More Info

ตัวอย่างหลักสูตร
(Course Preview)

File Info

Official Website : www.udemy.com/course/machine-learning-data-science-with-kaggle-pandas-numpy/
License : FOR EDUCATIONAL PURPOSES ONLY
ชื่อไฟล์ : Machine Learning & Data Science with Python, Kaggle & Pandas.rar
ขนาดไฟล์ : 9.47 GB
นามสกุลไฟล์ : *.rar
Server : Google Drive
วันที่อัพโหลด : 12/04/2023 
แก้ไขล่าสุด : 19/01/2024 | แก้ไขลิ้งค์แล้ว 
รหัสผ่าน : sbz

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