رویداد به پایان رسیده است!
برنامه نویسی پایتون (طول دوره 32 ساعت) در تاریخ جمعه ۵ خرداد ۱۳۹۶ به پایان رسیده است. (جزئیات بیشتر)
رویدادهای زیر را به شما پیشنهاد میکنیم:
برنامه نویسی پایتون (طول دوره 32 ساعت)
شروع رویداد
جمعه ۱۸ فروردین ۹۶ ۱۷:۰۰
پایان رویداد
جمعه ۵ خرداد ۹۶ ۲۱:۰۰
مکان رویدادتهران
موضوع رویدادتکنولوژی / برنامه نویسی

برگزارکنندهی رویداد گزارش
مهلت ثبتنام برای این رویداد به پایان رسیده است.
سر فصل مطالب
Intro to Python Programming (Level 01)
Introduction to python
Introduction to python
Introduction to Open-Source Language
Python Core and Versions
o Pycharm, Anaconda
o Jupyter, Qt console, Notebook, Ipython, Anaconda Navigator
Reserved Words and Naming Conventions
Python Structure
Arithmetic operations
Python Collections (Lists, Tuples, Sets and Dictionaries)
Import Clause
Copying Collections (Deep and Shallow Copy)
Intro to Classes, Functions
Read and write Output/Input
o Formatting Output/Input and Dialog Boxes
o Access and Permission
o Handling I/O Exceptions
Typecasting and Parsing
Functions and Classes
Built-in Modules (Sys, Math, Time, Pickle, …)
Access Modifiers (Public- Private -Protected)
User-Defined Classes
o Constructors, Copy Constructor, Object Instantiation
User-defined Methods
o Set and Get methods
o Finalizers
o Overloading versus Overriding Methods
Regular Expressions
Simple Character Matches
Special Characters
Greedy Matches
Grouping
Substituting
Splitting a given String
Compiling Regular Expressions
Object Oriented concepts:
Four fundamental Object-oriented programming (OOP) concepts
o Inheritance, Polymorphism, Abstraction and Encapsulation
Interfaces ( Abstract base ) and Packages
Serialization and Deserialization (Pickle…)
Database Connectivity
o Oracle, SQL server, and MongoDB
Intro to Python Programming (Level 02)
Data Integration
Plain Text (Standard and UTF-8)
Excel /CSV Format
Multimedia Files and Services (Read / Write)
Web Crawler (urllib, request, soup, scrapy…)
Data Serialization (Pickle, Dill, JSON, YAML…)
PyDI: Python Data Integration package
Introduction to Pentaho: Data Integration, Business Analytics and Big Data
Database (SQL/NoSQL connections)
Essential Data Science Libraries
NumPy - Numeric Computation
Pandas - Data structures and Exploratory Analysis.
Matplotlib - Plotting and Visualization Tool
IPython - Interactive shell to Explore Data and Debug Errors.
SciPy - Extends NumPy with more Mathematical Functionality
Image Processing (Essential Framework)
Introduction to general concepts in image processing
Scipy - Basic Image Manipulation and Processing
PIL - Basics Creating an image. Save Image. Load Image. Show image
OpenCV - Designed to solve Computer Vision problem
Scikit-Image - Collection of Algorithms for Image Processing
Machine Learning (Essential Framework)
Scikit-learn Package (Installation and Implementation)
o Learning models in sklearn
o k-means Clustering, Decision Trees, Linear Regression,
o K-Nearest-Neighbors, Predictive Models, Logistic Regression,
o Support Vector Machines,
o Dimensionality Reduction
o K-Fold Cross-Validation and Confusion matrices
Deep Learning Package (Installation and Implementation)
o GPU programming in Python (PyCUDA, PyOpenCL, …)
o Theano; Python library speed and stability optimizations
o TensorFlow; High-performance serving system for machine learning models
o Caffe; Deep learning framework
o Keras; Deep Learning library for Theano and TensorFlow
o Pylearn2; Deep learning research library
Web and Text Mining
Review on Web Crawler Library
Introduction to Natural language processing (NLP)
NLTK Exploration; The Natural Language Toolkit
HAZM “Farsi Mining”; Library for Digesting Persian Text
Pattern; natural language processing, Text Learning
Elasticsearch; Full Text Search into Python
Bigdata Framework
Introduction to big data frameworks
Hadoop Python MapReduce
Python Core and Versions
o Pycharm, Anaconda
o Jupyter, Qt console, Notebook, Ipython, Anaconda Navigator
Reserved Words and Naming Conventions
Python Structure
Arithmetic operations
Python Collections (Lists, Tuples, Sets and Dictionaries)
Import Clause
Copying Collections (Deep and Shallow Copy)
Intro to Classes, Functions
Read and write Output/Input
o Formatting Output/Input and Dialog Boxes
o Access and Permission
o Handling I/O Exceptions
Typecasting and Parsing
Functions and Classes
Built-in Modules (Sys, Math, Time, Pickle, …)
Access Modifiers (Public- Private -Protected)
User-Defined Classes
o Constructors, Copy Constructor, Object Instantiation
User-defined Methods
o Set and Get methods
o Finalizers
o Overloading versus Overriding Methods
Regular Expressions
Simple Character Matches
Special Characters
Greedy Matches
Grouping
Substituting
Splitting a given String
Compiling Regular Expressions
Object Oriented concepts:
Four fundamental Object-oriented programming (OOP) concepts
o Inheritance, Polymorphism, Abstraction and Encapsulation
Interfaces ( Abstract base ) and Packages
Serialization and Deserialization (Pickle…)
Database Connectivity
o Oracle, SQL server, and MongoDB
Intro to Python Programming (Level 02)
Data Integration
Plain Text (Standard and UTF-8)
Excel /CSV Format
Multimedia Files and Services (Read / Write)
Web Crawler (urllib, request, soup, scrapy…)
Data Serialization (Pickle, Dill, JSON, YAML…)
PyDI: Python Data Integration package
Introduction to Pentaho: Data Integration, Business Analytics and Big Data
Database (SQL/NoSQL connections)
Essential Data Science Libraries
NumPy - Numeric Computation
Pandas - Data structures and Exploratory Analysis.
Matplotlib - Plotting and Visualization Tool
IPython - Interactive shell to Explore Data and Debug Errors.
SciPy - Extends NumPy with more Mathematical Functionality
Image Processing (Essential Framework)
Introduction to general concepts in image processing
Scipy - Basic Image Manipulation and Processing
PIL - Basics Creating an image. Save Image. Load Image. Show image
OpenCV - Designed to solve Computer Vision problem
Scikit-Image - Collection of Algorithms for Image Processing
Machine Learning (Essential Framework)
Scikit-learn Package (Installation and Implementation)
o Learning models in sklearn
o k-means Clustering, Decision Trees, Linear Regression,
o K-Nearest-Neighbors, Predictive Models, Logistic Regression,
o Support Vector Machines,
o Dimensionality Reduction
o K-Fold Cross-Validation and Confusion matrices
Deep Learning Package (Installation and Implementation)
o GPU programming in Python (PyCUDA, PyOpenCL, …)
o Theano; Python library speed and stability optimizations
o TensorFlow; High-performance serving system for machine learning models
o Caffe; Deep learning framework
o Keras; Deep Learning library for Theano and TensorFlow
o Pylearn2; Deep learning research library
Web and Text Mining
Review on Web Crawler Library
Introduction to Natural language processing (NLP)
NLTK Exploration; The Natural Language Toolkit
HAZM “Farsi Mining”; Library for Digesting Persian Text
Pattern; natural language processing, Text Learning
Elasticsearch; Full Text Search into Python
Bigdata Framework
Introduction to big data frameworks
Hadoop Python MapReduce
مدرس
دکتر مهدی حبیب زاده
(دکترای علوم رایانه - دانشگاه کنکوردیا - مونترال - کانادا)
روز های برگزاری
عنوان | شروع | پایان | |
---|---|---|---|
جمعه ها به طول 9 جلسه شروع جلسه 18 فروردین | ۱۷:۱۵ | ۲۰:۳۰ |