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عنوان اصلی : Python Fundamentals

این مجموعه آموزش ویدیویی محصول موسسه آموزشی LiveLessons است که بر روی 2 حلقه دیسک به همراه فایلهای تمرینی ارائه شده و به مدت زمان 44 ساعت و 52 دقیقه در اختیار علاقه مندان قرار می گیرد.

در ادامه با برخی از سرفصل های درسی این مجموعه آموزش آشنا می شویم :

Introduction to Python Fundamentals: Part 1
Lesson overview
Getting the code
Structure of the examples folder
Installing Anaconda
Updating Anaconda
Package managers
Installing jupyter-matplotlib
Twitter developer account
Getting your questions answered
Lesson overview
Using IPython Interactive Mode as a Calculator
Executing a Python Program Using the IPython Interpreter
Writing and Executing Code in a Jupyter Notebook
Lesson overview
Variables and Assignment Statements
Self Check
Arithmetic
Self Check
Function print and an Intro to Single- and Double-Quoted Strings
Self Check
Triple-Quoted Strings
Self Check
Getting Input from the User
Self Check
Decision Making: The if Statement and Comparison Operators
Self Check
Objects and Dynamic Typing
Self Check
Intro to Data Science: Basic Descriptive Statistics
Self Check
Lesson overview
if Statement
Self Check
if...else and if...elif...else Statements
Self Check
while Statement
00:01:18
for Statement; Iterables, Lists and Iterators; Built-in range Function
Self Check
Augmented Assignments
Self Check
Sequence-Controlled Iteration
Self Check
Sentinel-Controlled Iteration
Built-In Function range: A Deeper Look
Self Check
Using Type Decimal for Monetary Amounts
Self Check
break and continue Statements
Boolean Operators and, or and not
Self Check
Intro to Data Science: Measures of Central Tendency--Mean, Median and Mode
Self Check
Lesson overview
Defining Functions
Self Check
Functions with Multiple Parameters
Self Check
Random-Number Generation
Self Check
Case Study: A Game of Chance
Self Check
math Module Functions
Default Parameter Values
Keyword Arguments
Arbitrary Argument Lists
Self Check
Methods: Functions That Belong to Objects
Scope Rules
import: A Deeper Look
Self Check
Passing Arguments to Functions: A Deeper Look
Self Check
Functional-Style Programming
Intro to Data Science: Measures of Dispersion
Introduction to Python Fundamentals: Part 2
Lesson 05: Sequences: Lists and Tuples
Lesson overview
Lists
Self Check
Tuples
Self Check
Unpacking Sequences
Creating a primitive bar chart
Self Check
Sequence Slicing Part 1: Getting a Subset of a Sequence
Sequence Slicing Part 2: Modifying a List
Self Check
del Statement
Self Check
Passing Lists to Functions
Sorting Lists
Self Check
Searching Sequences
Self Check
Other List Methods
Self Check
Simulating Stacks with Lists
List Comprehensions
Self Check
Generator Expressions
Self Check
Filter, Map and Reduce
Self Check
Other Sequence Processing Functions
Self Check
Two-Dimensional Lists
Self Check
Intro to Data Science: Simulation and Static Visualizations
Sample Graphs for 600, 60,000 and 6,000,000 Die Rolls
Visualizing Die-Roll Frequencies and Percentages--Part 1
Visualizing Die-Roll Frequencies and Percentages--Part 2
Visualizing Die-Roll Frequencies and Percentages--Part 3
Visualizing Die-Roll Frequencies and Percentages--Part 4
Lesson overview
Dictionaries
Creating a Dictionary
Self Check
Iterating through a Dictionary
Basic Dictionary Operarations
Self Check
Dictionary Methods keys and values
Self Check
Dictionary Comparisons
Example: Dictionary of Student Grades
Example: Word Counts
Python Standard Library Module collections
Self Check
Dictionary Method update
Dictionary Comprehensions
Self Check
Sets
Self Check
Comparing Sets
Self Check
Mathematical Set Operations
Self Check
Mutable Set Operators and Methods
Set Comprehensions
Intro to Data Science: Dynamic Visualizations--How Dynamic Visualization Works
Intro to Data Science: Dynamic Visualizations--Implementing Dynamic Visualization, Part 1
Lesson 07: Array-Oriented Programming with NumPy
Creating arrays from Existing Data
Self Check
array Attributes
Self Check
Filling arrays with Specific Values
Creating arrays from Ranges
Self Check
List vs. array Performance: Introducing %timeit
Self Check
array Operators
Self Check
NumPy Calculation Methods
Self Check
Universal Functions
Self Check
Indexing and Slicing
Self Check
Views: Shallow Copies
Deep Copies
Reshaping and Transposing: reshape vs. resize
Reshaping and Transposing: flatten vs. ravel
Reshaping and Transposing: Transposing Rows and Columns
Reshaping and Transposing: Horizontal and Vertical Stacking
Self Check
Intro to Data Science: pandas Series and DataFrames
Intro to Data Science: pandas Series and DataFrames--pandas Series Part 1
Intro to Data Science: pandas Series and DataFrames--pandas Series Part 2
Self Check
Intro to Data Science: pandas Series and DataFrames--Creating DataFrames and Customizing Indices
Intro to Data Science: pandas Series and DataFrames--Accessing a DataFrame's Columns
Intro to Data Science: pandas Series and DataFrames--Selecting Rows via the loc and iloc Attributes
Intro to Data Science: pandas Series and DataFrames--Selecting Rows via Slices and Lists with the loc and iloc Attributes
Intro to Data Science: pandas Series and DataFrames--Selecting Subsets of the Rows and Columns
Intro to Data Science: pandas Series and DataFrames--Boolean Indexing
Intro to Data Science: pandas Series and DataFrames--Accessing a Specific DataFrame Cell by Row and Column
Intro to Data Science: pandas Series and DataFrames--Descriptive Statistics
Intro to Data Science: pandas Series and DataFrames--Transposing the DataFrame with the T Attribute
Intro to Data Science: pandas Series and DataFrames--Sorting by Indices
Intro to Data Science: pandas Series and DataFrames--Sorting by Column Values
Self Check
00:03:
Introduction to Python Fundamentals: Part 3
Lesson overview
Formatting Strings--Presentation Types
Self Check
Formatting Strings--Field Widths and Alignment
Self Check
Formatting Strings--Numeric Formatting
Self Check
Formatting Strings--String's format Method
Self Check
Concatenating and Repeating Strings
Self Check
Stripping Whitespace from Strings
Self Check
Changing Character Case
Self Check
Comparison Operators for Strings
Searching for Substrings
00:05:
Self Check
Replacing Substrings
Self Check
Splitting and Joining Strings
Self Check
00:03:
Characters and Character-Testing Methods
Raw Strings
Introduction to Regular Expressions
re Module and Function fullmatch Part 1--Matching Literal Characters
re Module and Function fullmatch Part 2--Metacharacters, Character Classes and Quantifiers
re Module and Function fullmatch Part 3--Custom Character Classes
re Module and Function fullmatch Part 1--Quantifiers
Self Check
Replacing Substrings and Splitting Strings
Self Check
Other Search Functions; Accessing Matches--Function search: Finding the First Match Anywhere in a String
Other Search Functions; Accessing Matches--Ignoring Case with the Optional flags Keyword Argument
Other Search Functions; Accessing Matches--Metacharacters that Restrict Matches to the Beginning or End of a String
Other Search Functions; Accessing Matches--Functions findall and finditer: Finding All Matches in a String
Other Search Functions; Accessing Matches--Capturing Substrings in a Match
Self Check
Intro to Data Science: Pandas, Regular Expressions and Data Munging Part 1: Introduction
Intro to Data Science: Pandas, Regular Expressions and Data Munging Part 3: Data Validation
Intro to Data Science: Pandas, Regular Expressions and Data Munging Part 4: Reformatting Your Data
Self Check
Lesson overview
Files
Text-File Processing--Writing to a Text File: Introducing the with Statement
Self Check
Text-File Processing--Reading Data from a Text File
Self Check
Updating Text Files
Self Check
Serialization with JSON--JSON Data Format
Serialization with JSON--Serializing an Object to JSON
Serialization with JSON--Deserializing a JSON Object into Python
Serialization with JSON--Displaying JSON Text
Self Check
File Open Modes
Handling Exceptions
Division by Zero and Invalid Input
try Statements
Self Check
finally Clause
Self Check
Explicitly Raising an Exception
Stack Unwinding and Tracebacks
Intro to Data Science: Working with CSV Files--Python Standard Library Module csv
Self Check
Intro to Data Science: Working with CSV Files--Reading CSV Files into Pandas DataFrames
Intro to Data Science: Working with CSV Files--Reading the Titanic Disaster Dataset
Intro to Data Science: Working with CSV Files--Simple Data Analysis with the Titanic Disaster Dataset
Intro to Data Science: Working with CSV Files--Passenger Age Histogram
Introduction to Python Fundamentals: Part 4
Lesson overview
Custom Class Account--Test-Driving Class Account
Custom Class Account--Account Class Definition
Self Check
Controlling Access to Attributes
Properties for Data Access--Test-Driving Class Time
Properties for Data Access--Class Time Definition
Self Check
Properties for Data Access--Class Time Definition Notes
Simulating "Private" Attributes
Case Study: Card Shuffling and Dealing Simulation--Test Driving Classes Card and DeckOfCards
Case Study: Card Shuffling and Dealing Simulation--Class Card and an Introduction to Class Attributes
Case Study: Card Shuffling and Dealing Simulation--Class DeckOfCards
Case Study: Card Shuffling and Dealing Simulation--Displaying Card Images with Matplotlib
Self Check
Inheritance: Base Classes and Subclasses
Building an Inheritance Hierarchy and Introducing Polymorphism--Base Class CommissionEmployee
Building an Inheritance Hierarchy and Introducing Polymorphism--Sublass SalariedCommissionEmployee
Building an Inheritance Hierarchy and Introducing Polymorphism--Processing CommissionEmployees and SalariedCommissionEmployees Polymorphically
Duck Typing and Polymorphism
Operator Overloading
Test-Driving Class Complex
Class Complex Definition
Self Check
Named Tuples
A Brief Intro to Python 3.7's New Data Classes
A Brief Intro to Python 3.7's New Data Classes--Creating a Card Data Class
A Brief Intro to Python 3.7's New Data Classes--Using the Card Data Class
Self Check
A Brief Intro to Python 3.7's New Data Classes--Advantages Over Named Tuples and Traditional Classes
Unit Testing with Docstrings and doctest
Self Check
Namespaces and Scopes
Intro to Data Science: Time Series and Simple Linear Regression--Introduction
Intro to Data Science: Time Series and Simple Linear Regression--Components of the Simple Linear Regression Calculation
Intro to Data Science: Time Series and Simple Linear Regression--Loading the Average High Temperatures into a DataFrame
Intro to Data Science: Time Series and Simple Linear Regression--Cleaning the Data
Intro to Data Science: Time Series and Simple Linear Regression--Calculating Basic Descriptive Statistics for the Dataset
Intro to Data Science: Time Series and Simple Linear Regression--Forecasting Future January Average High Temperatures
Intro to Data Science: Time Series and Simple Linear Regression--Plotting the Average High Temperatures and a Regression Line
Lesson overview
Introduction
TextBlob
Create a TextBlob
Tokenizing Text into Sentences and Words
Parts-of-Speech Tagging
Extracting Noun Phrases
Sentiment Analysis with TextBlob's Default Sentiment Analyzer
Sentiment Analysis with the NaiveBayesAnalyzer
Language Detection and Translation
Inflection: Pluralization and Singularization
Spell Checking and Correction
Normalization: Stemming and Lemmatization
Word Frequencies
Getting Definitions, Synonyms and Antonyms from WordNet
Deleting Stop Word
n-grams
Visualizing Word Frequencies with Pandas
Visualizing Word Frequencies with Word Clouds
Readability Assessment with Textatistic
Named Entity Recognition with spaCy
Similarity Detection with spaCy
Lesson overview
Introduction
Overview of the Twitter APIs
Creating a Twitter Developer Account
Getting Twitter Credentials--Creating an App
What's in a Tweet?
Tweepy
Authenticating with Twitter Via Tweepy
Getting Information About a Twitter Account
Self Check
Introduction to Tweepy Cursors: Getting an Account's Followers and Friends
Determining an Account's Followers
Self Check
Determining Whom an Account Follows
Getting a User's Recent Tweets
Self Check
Searching Recent Tweets
Self Check
Spotting Trends: Twitter Trends API
Places with Trending Topics
Getting a List of Trending Topics
Self Check
Create a Word Cloud from Trending Topics
Self Check
Cleaning/Preprocessing Tweets for Analysis
Twitter Streaming API
Creating a Subclass of StreamListener
Initiating Stream Processing
Twitter Restrictions Note
Tweet Sentiment Analysis
Geocoding and Mapping
Getting and Mapping the Tweets
Utility Functions in tweetutilities.py and Class LocationListener
Lesson overview
Introduction to Watson
IBM Cloud Account and Cloud Console
Watson Services: Watson Assistant Demo
Watson Services: Visual Recognition
Watson Services: Speech to Text
Watson Services: Text to Speech
Watson Services: Language Translator
Watson Services: Natural Language Understanding
Watson Services: Personality Insights
Additional Services and Tools
Watson Developer Cloud Python SDK
Case Study: Traveler's Companion Translation App
Before You run the App
Before You run the App: Registering for the Speech to Text Service
Before You run the App: Registering for the Text to Speech Service
Before You run the App: Registering for the Language Translator Service
Test-Driving the App
SimpleLanguageTranslator.py Script Walkthrough
SimpleLanguageTranslator.py Script Walkthrough: Importing Watson SDK Classes from the ibm_watson Module
SimpleLanguageTranslator.py Script Walkthrough: Other Imported Modules
SimpleLanguageTranslator.py Script Walkthrough: Main Program: Function run_translator
SimpleLanguageTranslator.py Script Walkthrough: Function speech_to_text
SimpleLanguageTranslator.py Script Walkthrough: Function translate
SimpleLanguageTranslator.py Script Walkthrough: Function text_to_speech
SimpleLanguageTranslator.py Script Walkthrough: Function record_audio
SimpleLanguageTranslator.py Script Walkthrough: Function play_audio
Watson Resources
Lesson overview
Introduction to Machine Learning
Case Study: Classification with k-Nearest Neighbors and the Digits Dataset, Part 1
k-Nearest Neighbors Algorithm
k-Nearest Neighbors Algorithm: Hyperparameters and Hyperparameter Tuning
Loading the Dataset
Loading the Dataset: Displaying the Description
Loading the Dataset: Checking the Sample and Target Sizes
Loading the Dataset: A Sample Digit Image
Loading the Dataset: Preparing the Data for Use with Scikit-Learn
Visualizing the Data
Splitting the Data for Training and Testing
Creating the Model
Training the Model
Predicting Digit Classes
Case Study: Classification with k-Nearest Neighbors and the Digits Dataset, Part 2
Metrics for Model Accuracy: Estimator Method score
Metrics for Model Accuracy: Confusion Matrix
Metrics for Model Accuracy: Classification Report
Metrics for Model Accuracy: Visualizing the Confusion Matrix
K-Fold Cross-Validation
Running Multiple Models to Find the Best One
Hyperparameter Tuning
Case Study: Time Series and Simple Linear Regression
Loading the Average High Temperatures into a DataFrame
Splitting the Data for Training and Testing
Training the Model
Testing the Model
Predicting Future Temperatures and Estimating Past Temperatures
Visualizing the Dataset with the Regression Line
Overfitting/Underfitting
Case Study: Multiple Linear Regression with the California Housing Dataset
Loading the Dataset
Exploring the Data with Pandas
Visualizing the Features
Splitting the Data for Training and Testing
Training the Model
Testing the Model
Visualizing the Expected vs. Predicted Prices
Regression Model Metrics
Choosing the Best Model
Case Study: Unsupervised Machine Learning, Part 1--Dimensionality Reduction
Loading the Digits Dataset
Creating a TSNE Estimator for Dimensionality Reduction
Transforming the Digits Dataset's Features into Two Dimensions
Visualizing the Reduced Data
Visualizing the Reduced Data with Different Colors for Each Digit
Visualizing the Reduced Data in 3D
Case Study: Unsupervised Machine Learning, Part 2--k-Means Clustering
Loading the Iris Dataset
Exploring the Iris Dataset: Descriptive Statistics with Pandas
Visualizing the Dataset with a Seaborn pairplot
Using a KMeans Estimator
Dimensionality Reduction with Principal Component Analysis
Choosing the Best Clustering Estimator
Lesson overview
Introduction
Deep Learning Applications
Deep Learning Demos
Keras Resources
Keras Built-In Datasets
Custom Anaconda Environments
Neural Networks
Tensors
Convolutional Neural Networks for Vision; Multi-Classification with the MNIST Dataset
Reproducibility in Keras and Deep Learning
Basic Keras Neural Network
Loading the MNIST Dataset
Data Exploration
Visualizing Digits
Reshaping the Image Data
Normalizing the Image Data
One-Hot Encoding: Converting the Labels From Integers to Categorical Data
Creating the Neural Network
Adding Layers to the Network
Convolution
Adding a Conv2D Convolution Layer to Our Model
Dimensionality of the First Convolution Layer’s Output
Overfitting
Adding a Pooling Layer
Adding Another Convolutional Layer and Pooling Layer
Flattening the Results to One Dimension with a Keras Flatten Layer
Adding a Dense Layer to Reduce the Number of Features
Adding Another Dense Layer to Produce the Final Output
Printing the Model's Summary
Visualizing a Model’s Structure
Compiling the Model
Training and Evaluating the Model
Evaluating the Model on Unseen Data
Making Predictions
Locating the Incorrect Predictions
Visualizing Incorrect Predictions
Displaying the Probabilities for Several Incorrect Predictions
Saving and Loading a Model
Visualizing Neural Network Training with TensorBoard
ConvnetJS: Browser-Based Deep-Learning Training and Visualization
Recurrent Neural Networks for Sequences; Sentiment Analysis with the IMDb Dataset
Loading the IMDb Movie Reviews Dataset
Data Exploration
Movie Review Encodings and Decoding a Review
Data Preparation
00:05:
Creating the Neural Network
Adding an Embedding Layer
Adding an LSTM Layer
Adding a Dense Output Layer
Compiling the Model and Displaying the Summary
Training and Evaluating the Model (1 of 2)
Training and Evaluating the Model (2 of 2)
Tuning Deep Learning Models
Lesson overview
Introduction--Databases
Introduction--Apache Hadoop and Apache Spark
Introduction--Internet of Things
Introduction--Experience Cloud and Desktop Big-Data Software
Introduction--Big Data Sources
Relational Databases and Structured Query Language (SQL)
A books Database
SELECT Queries
WHERE Clause
ORDER BY Clause
Merging Data from Multiple Tables: INNER JOIN
INSERT INTO Statement
UPDATE Statement
DELETE FROM Statement
NoSQL and NewSQL Big-Data Databases: A Brief Tour
NoSQL Key-Value Databases
NoSQL Document Databases
NoSQL Columnar Databases
NoSQL Graph Databases
NewSQL Databases
Case Study: A MongoDB JSON Document Database
Creating the MongoDB Atlas Cluster
Streaming Tweets into MongoDB
Hadoop
Hadoop Overview
Summarizing Word Lengths in Romeo and Juliet via MapReduce
Creating an Apache Hadoop Cluster in Microsoft Azure HDInsight: Part 1
Creating an Apache Hadoop Cluster in Microsoft Azure HDInsight: Part 2
Hadoop Streaming
Implementing the Mapper
Implementing the Reducer
Preparing to Run the MapReduce Example
Running the MapReduce Job
Spark Overview
Docker and the Jupyter Docker Stacks
Word Count with Spark
Spark Word Count on Microsoft Azure
Spark Streaming: Counting Twitter Hashtags Using the pysparknotebook Docker Stack
Streaming Tweets to a Socket
Summarizing Tweet Hashtags; Introducing Spark SQL
Internet of Things and Dashboards
Publish and Subscribe
Visualizing a PubNub Sample Live Stream with a Freeboard Dashboard
Simulating an Internet-Connected Thermostat in Python and Creating a Dashbboard in Freeboard.io
Creating a Python PubNub Subscriber

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ارائه شده بر روی 2 حلقه دیسک به همراه فایلهای تمرینی
مدت زمان آموزش 44 ساعت و 52 دقیقه !
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