پیشنهاد فرادرس

آموزش انجام علم داده با Python

دسته بندی ها: آموزش پایتون (Python) ، علم داده (Data Science) ، آموزش های پلورال سایت (Pluralsight)

آیا می خواهید یک دانشمند داده شوید؟

اگر چنین است، این دوره شما را با مفاهیم و ابزارهایی که در کسب این مهارت کمک می کند و کار بر روی هر پروژه علم داده آشنا می کند. در این دوره با مفهوم علم داده، استخراج داده از انواع مختلف منابع و مدل یادگیری ماشینی به عنوان API endpoint که می تواند در راه حل داده های دنیای واقعی مورد استفاده قرار گیرد آشنا می شوید. پس از آن شما یاد خواهید گرفت که از کتابخانه های استاندارد مختلف در اکوسیستم پایتون مانند Pandas، NumPy، Matplotlib، Scikit-Learn، Pickle، Flask برای رفع مراحل مختلف پروژه های علم داده مانند استخراج داده ها، تمیز کردن و پردازش داده ها، ساخت و ارزیابی مدل یادگیری ماشین استفاده کنید.

سرفصل ها:

  • مرور دوره
  • معرفی دوره
  • مخاطبان دوره
  • پیش نیازهای دوره
  • بررسی چرخه پروژه علم داده
  • چرا پایتون برای علم داده؟
  • راه اندازی محیط کار
  • توزیع پایتون برای علم داده
  • Python 3.x در مقابل  Python 2.x
  • نسخه ی نمایشی: نصب توزیع Anaconda
  • Jupyter Notebook
  • نسخه ی نمایشی: راه اندازی Jupyter Notebook در ماشین محلی
  • نسخه ی نمایشی: نوت بوک Jupyter - مبانی
  • نسخه ی نمایشی: Jupyter Notebook - توابع MAGIC
  • قالب پروژه علم داده
  • نسخه ی نمایشی: ایجاد قالب پروژه ی علم داده ی Cookiecutter
  • نسخه ی نمایشی: پروژه را به Git اضافه کنید
  • استخراج داده
  • استخراج داده از پایگاه داده
  • استخراج داده از API ها
  • استخراج داده با Web Scraping
  • مجموعه های داده
  • انجام تغییرات در Git
  • معرفی NumPy و Pandas
  • EDA: ساختار اولیه
  • EDA: آمار خلاصه
  • اندازه گیری مرکزیت
  • اندازه گیری مرکزیت: Mean
  • اندازه گیری مرکزیت: Median
  • اندازه گیری گسترش
  • اندازه گیری گسترش: محدوده
  • اندازه گیری گسترش: درصد و Boxplot
  • اندازه گیری گسترش: واریانس و انحراف استاندارد
  • EDA: توزیع
  • EDA: گروه بندی
  • Data Munging
  • Missing Value
  • رمزگذاری ویژگی های طبقه بندی شده
  • ساخت و ارزیابی مدل های پیش بینی شده
  • مبانی یادگیری ماشینی
  • معیارهای عملکرد
  • مدل Baseline
  • مدل رگرسیون خطی
  • مدل رگرسیون منطقی
  • Underfitting در مقابل Overfitting
  • بهینه سازی پارامتر: GridSearch
  • اعتبار سنجی متقابل
  • اعتبار سنجی متقابل K-Fold
  • عادی سازی و استاندارد سازی ویژگی
  • پایداری مدل
  • توسعه API یادگیری ماشینی
  • خلاصه
Doing Data Science with Python Publisher:Pluralsight Author:Abhishek Kumar Duration:6h 25m Level:Beginner

This course shows you how to work on an end-to-end data science project including processing data, building & evaluating machine learning model, and exposing the model as an API in a standardized approach using various Python libraries.
Do you want to become a Data Scientist? If so, this course will equip you with concepts and tools that can bring you to speed and you can utilize the skills acquired in this course to work on any data science project in a standardized approach. This course, Doing Data Science with Python, follows a pragmatic approach to tackle end-to-end data science project cycle right from extracting data from different types of sources to exposing your machine learning model as API endpoints that can be consumed in a real-world data solution. This course will not only help you to understand various data science related concepts, but also help you to implement the concepts in an industry standard approach by utilizing Python and related libraries.
First, you will be introduced to the various stages of a typical data science project cycle and a standardized project template to work on any data science project. Then, you will learn to use various standard libraries in the Python ecosystem such as Pandas, NumPy, Matplotlib, Scikit-Learn, Pickle, Flask to tackle different stages of a data science project such as extracting data, cleaning and processing data, building and evaluating machine learning model. Finally you'll dive into exposing the machine learning model as APIs. You will also go through a case study that will encompass the whole course to learn end-to-end execution of a data science project. By the end of this course, you will have a solid foundation to handle any data science project and have the knowledge to apply various Python libraries to create your own data science solutions.

Course Overview
1m 50s
Course Overview
1m 50s
Course Introduction
16m 3s
Course Introduction
4m 43s
Target Audience
0m 38s
Course Prerequisites
1m 21s
Data Science Project Cycle Overview
2m 25s
Why Python for Data Science?
2m 3s
Course Outline
3m 59s
Summary
0m 52s
Setting up Working Environment
40m 40s
Introduction
0m 29s
Overview
0m 39s
Python Distributions for Data Science
2m 4s
Python 3.x vs. Python 2.x
1m 5s
Demo: Installing Ananconda Distribution
3m 59s
Jupyter Notebook
1m 42s
Demo: Setting up Jupyter Notebook on Local Machine
1m 56s
Demo: Jupyter Notebook - Basics
6m 59s
Demo: Jupyter Notebook - Magic Functions
8m 10s
Data Science Project Template
3m 9s
Demo: Setting up Cookiecutter Data Science Project Template
5m 18s
Versioning for Data Science Projects
1m 43s
Demo: Add Project to Git
2m 8s
Summary
1m 12s
Extracting Data
46m 29s
Introduction
1m 6s
Overview
1m 13s
Extracting Data from Databases
1m 28s
Demo: Extracting Data from Databases
7m 34s
Extracting Data Through APIs
2m 20s
Demo: Extracting Data Through APIs
3m 51s
Extracting Data Using Web Scraping
2m 8s
Demo: Web Scraping Using Requests and BeautifulSoup
6m 59s
Demo: Getting Titanic Dataset Using Requests : Part 1 - Initial Preparation
3m 8s
Demo: Getting Titanic Dataset Using Requests : Part 2 - Downloading Data
7m 45s
Demo: Creating Reproducible Script for Getting Titanic Data
4m 35s
Public Datasets
1m 46s
Committing Changes to Git
1m 17s
Summary
1m 12s
Exploring and Processing Data - Part 1
52m 10s
Introduction
5m 16s
Overview
1m 18s
Introduction to NumPy and Pandas
2m 14s
EDA: Basic Structure
1m 0s
Demo: Investigating Basic Structure
10m 20s
Demo: Selection, Indexing, and Filtering
5m 57s
EDA: Summary Statistics
1m 29s
Centrality Measure
0m 23s
Centrality Measure: Mean
1m 36s
Centrality Measure: Median
1m 44s
Spread Measure
0m 47s
Spread Measure: Range
1m 47s
Spread Measure: Percentiles and Boxplot
3m 25s
Spread Measure: Variance and Standard Deviation
2m 10s
Demo: Getting Summary Statistics for Numerical Features
5m 27s
Counts and Proportions
1m 29s
Demo: Summary Statistics for Categorical Feature
4m 33s
Summary
1m 6s
Exploring and Processing Data - Part 2
32m 39s
Introduction
1m 21s
Overview
1m 1s
EDA: Distributions
1m 10s
Univariate Distribution: Histogram and KDE Plot
4m 58s
Demo: Creating Univariate Distribution Plots
2m 26s
Bivariate Distribution: Scatter Plot
1m 24s
Demo: Creating Scatter Plots
3m 44s
EDA: Grouping
2m 23s
Demo: Grouping and Aggregation
5m 13s
Crosstab
1m 9s
Demo: Crosstab
1m 34s
Pivot Table
2m 16s
Demo: Pivot Table
2m 53s
Summary
0m 59s
Exploring and Processing Data - Part 3
1h 23m
Introduction
2m 1s
Overview
1m 17s
Data Munging
1m 42s
Missing Value: Issues and Solution
2m 37s
Missing Value Imputation Techniques
3m 23s
Demo: Treating Missing Values Using Pandas - Part 1
6m 51s
Demo: Treating Missing Values Using Pandas - Part 2
1m 41s
Demo: Treating Missing Values Using Pandas - Part 3
9m 47s
Outliers: Detection and Treatment
4m 3s
Demo: Detecting and Treating Outliers Using Pandas and NumPy
7m 2s
Feature Engineering
2m 9s
Demo: Feature Creation Using Pandas and NumPy – Part 1
2m 29s
Demo: Feature Creation Using Pandas and NumPy – Part 2
2m 59s
Demo: Feature Creation Using Pandas and NumPy – Part 3
1m 11s
Demo: Feature Creation Using Pandas and NumPy – Part 4
4m 23s
Categorical Feature Encoding
0m 38s
Categorical Feature Encoding: Binary Encoding
1m 9s
Categorical Feature Encoding: Label Encoding
1m 40s
Categorical Feature Encoding: One-hot Encoding
1m 38s
Demo: Categorical Feature Encoding Using Pandas
3m 26s
Demo: Drop and Reorder Columns Using Pandas
1m 53s
Demo: Save Dataframe to File Using Pandas
2m 47s
Demo: Reproducible Script for Data Processing Using Pandas and NumPy
6m 46s
Demo: Creating Visualization Using MatPlotlib
6m 38s
Demo: Committing Changes to Git
1m 17s
Summary
1m 24s
Building and Evaluating Predictive Models – Part 1
1h 2m
Introduction
2m 54s
Overview
1m 51s
Machine Learning Basics
0m 56s
Machine Learning Basics: Representation and Generalization
1m 56s
Machine Learning Basics: Spam Classification
2m 55s
Machine Learning Basics: Supervised Learning
3m 23s
Machine Learning Basics: Unsupervised Learning
1m 48s
Titanic Disaster Data Challenge
2m 1s
Classifier
4m 26s
Performance Metrics
1m 16s
Performance Metrics: Accuracy
1m 9s
Performance Metrics: Precision and Recall
3m 29s
Classifier Evaluation
2m 56s
Baseline Model
2m 15s
Demo: Preparing Data for Machine Learning Model
6m 29s
Demo: Building and Evaluating Baseline Model
4m 27s
Demo: Making the First Kaggle Submission
5m 5s
Linear Regression Model
2m 55s
Logistic Regression Model
4m 56s
Demo: Building Logistic Regression Using Scikit-Learn
2m 36s
Demo: Making Second Kaggle Submission
1m 36s
Summary
1m 19s
Building and Evaluating Predictive Models – Part 2
49m 50s
Introduction
1m 32s
Overview
1m 48s
Underfitting vs. Overfitting
2m 35s
Regularization
3m 43s
Hyperparameter Optimization: GridSearch
1m 48s
Crossvalidation
1m 50s
K-Fold Crossvalidation
1m 27s
Demo: Hyperparameter Optimization Using GridSearchCV
2m 35s
Demo: Making Third Kaggle Submission
1m 22s
Feature Normalization and Standardization
2m 19s
Demo: Feature Normalization and Standardization Using Scikit-Learn
3m 53s
Model Persistence
1m 6s
Demo: Model Persistence Using Pickle
4m 3s
Machine Learning API Development
2m 22s
Demo: Hello World API Using Flask
4m 50s
Demo: Machine Learning API Using Flask
6m 39s
Demo: Committing Changes to Git
1m 25s
Summary
2m 5s
Where to Go from Here?
2m 19s

پیشنهاد فرادرس

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