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آموزش برنامه نویسی Python و علم داده

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Learning Path: From Python Programming to Data Science Publisher:Packtpub Author:Ankita Thakur- Curator Duration:20:57:04

Basic programming and high-end data science techniques In Detail Python has become the language of choice of data scientists for performing data analysis, visualization, and machine learning. If you’re ... - Selection from Learning Path: From Python Programming to Data Science [Video]
Release Date: May 2017
ISBN: 9781788474733
Video Description
Basic programming and high-end data science techniquesIn DetailPython has become the language of choice of data scientists for performing data analysis, visualization, and machine learning. If you’re looking forward to implementing Python in your data science projects to enhance data discovery, then this is the perfect Learning Path for you. Starting out at the basic level, this Learning Path will take you through all the stages of data science in a step-by-step manner.We begin this journey with nailing down the fundamentals of Python. You’ll be introduced to basic and advanced programming concepts of Python before moving on to data science topics. Then, you’ll learn how to perform data analysis by taking advantage of the core data science libraries in the Python ecosystem. You’ll also get a better understanding of the data visualization concepts, how to apply them, and how you can overcome any challenges while implementing them. Moving ahead, you’ll learn to use a wide variety of machine learning algorithms to solve real-world problems. Finally, you’ll learn deep learning along with an introduction to TensorFlow.By the end of the Learning Path, you’ll be able to efficiently make use of Python in your data science projects.Prerequisites: Basic knowledge of any programming language (preferably Python).Resources: Code downloads and errata:Mastering Python - Second EditionLearning Python Data AnalysisPython Data Visualization SolutionsPython Machine Learning SolutionsDeep Learning with PythonPATH PRODUCTSThis path navigates across the following products (in sequential order):Mastering Python - Second Edition (5h 21m)Learning Python Data Analysis (5h 55m)Python Data Visualization Solutions (3h 27m)Python Machine Learning Solutions (4h 27m)Deep Learning with Python (1h 45m)
Chapter 1 : Mastering Python - Second Edition
The Course Overview
00:03:25
Python Basic Syntax and Block Structure
00:11:54
Built-in Data Structures and Comprehensions
00:08:55
First-Class Functions and Classes
00:05:50
Extensive Standard Library
00:05:56
New in Python 3.5
00:06:02
Downloading and Installing Python
00:05:17
Using the Command-Line and the Interactive Shell
00:04:01
Installing Packages with pip
00:03:16
Finding Packages in the Python Package Index
00:04:29
Creating an Empty Package
00:05:50
Adding Modules to the Package
00:05:31
Importing One of the Package's Modules from Another
00:05:26
Adding Static Data Files to the Package
00:02:53
PEP 8 and Writing Readable Code
00:07:51
Using Version Control
00:04:48
Using venv to Create a Stable and Isolated Work Area
00:04:41
Getting the Most Out of docstrings 1: PEP 257 and docutils
00:08:00
Getting the Most Out of docstrings 2: doctest
00:04:04
Making a Package Executable via python -m
00:05:52
Handling Command-Line Arguments with argparse
00:06:22
Interacting with the User
00:04:39
Executing Other Programs with Subprocess
00:09:10
Using Shell Scripts or Batch Files to Run Our Programs
00:03:01
Using concurrent.futures
00:13:53
Using Multiprocessing
00:11:22
Understanding Why This Isn't Like Parallel Processing
00:08:02
Using the asyncio Event Loop and Coroutine Scheduler
00:06:52
Waiting for Data to Become Available
00:03:30
Synchronizing Multiple Tasks
00:06:18
Communicating Across the Network
00:03:45
Using Function Decorators
00:06:45
Function Annotations
00:07:09
Class Decorators
00:05:53
Metaclasses
00:05:35
Context Managers
00:05:52
Descriptors
00:05:38
Understanding the Principles of Unit Testing
00:05:07
Using the unittest Package
00:07:28
Using unittest.mock
00:06:12
Using unittest's Test Discovery
00:04:30
Using Nose for Unified Test Discover and Reporting
00:03:42
What Does Reactive Programming Mean?
00:02:50
Building a Simple Reactive Programming Framework
00:07:22
Using the Reactive Extensions for Python (RxPY)
00:10:22
Microservices and the Advantages of Process Isolation
00:04:13
Building a High-Level Microservice with Flask
00:09:59
Building a Low-Level Microservice with nameko
00:06:25
Advantages and Disadvantages of Compiled Code
00:04:42
Accessing a Dynamic Library Using ctypes
00:07:59
Interfacing with C Code Using Cython
00:12:35
Chapter 2 : Learning Python Data Analysis
The Course Overview
00:03:55
Getting started with Python
00:26:23
Getting Data using the Twitter API
00:20:47
Collecting and Storing Tweets
00:09:27
Database Design
00:10:31
Pandas and Databases
00:05:56
Panda Series, Dataframes, and Columnar Operations
00:21:21
Grouping Operations and Working with Date Columns
00:17:01
Merging Operations and Exporting data to JSON/CSV
00:14:54
Array Features, Bucketting Arrays and Histogram Functions
00:21:02
Simple Aggregations
00:21:23
Linear Algebra
00:04:29
Introducting PyQT and MatplotLib
00:31:47
Creating Charts
00:07:36
Simple XY Plots with Axis Scales
00:04:47
Introduction to the NTLK Package
00:19:00
Bag of Words
00:21:33
Classification of Words
00:09:27
Stemming
00:11:53
Simple Sentiment Analysis
00:05:43
Grouping By Dimensions and Classification of Data Types
00:25:08
Trend Analysis and Deriving New Metrics
00:20:29
Correlation Analysis
00:17:28
Course Summary
00:03:42
Chapter 3 : Python Data Visualization Solutions
The Course Overview
00:03:38
Importing Data from CSV
00:04:33
Importing Data from Microsoft Excel Files
00:04:46
Importing Data from Fix-Width Files
00:03:06
Importing Data from Tab Delimited Files
00:02:23
Importing Data from a JSON Resource
00:05:17
Importing Data from a Database
00:05:09
Cleaning Up Data from Outliers
00:05:54
Importing Image Data into NumPy Arrays
00:06:01
Generating Controlled Random Datasets
00:06:36
Smoothing Noise in Real-World Data
00:04:45
Defining Plot Types and Drawing Sine and Cosine Plots
00:07:53
Defining Axis Lengths and Limits
00:05:16
Defining Plot Line Styles, Properties, and Format Strings
00:01:59
Setting Ticks, Labels, and Grids
00:02:43
Adding Legends and Annotations
00:02:33
Moving Spines to Center
00:01:22
Making Histograms
00:03:59
Making Bar Charts with Error Bars
00:03:23
Making Pie Charts Count
00:01:59
Plotting with Filled Areas
00:01:56
Drawing Scatter Plots with Colored Markers
00:02:13
Adding a Shadow to the Chart Line
00:03:56
Adding a Data Table to the Figure
00:02:26
Using Subplots
00:03:57
Customizing Grids
00:03:05
Creating Contour Plots
00:03:24
Filling an Under-Plot Area
00:02:01
Drawing Polar Plots
00:02:56
Visualizing the filesystem Tree Using a Polar Bar
00:03:03
Creating 3D Bars
00:05:33
Creating 3D Histograms
00:03:13
Animating with OpenGL
00:06:02
Plotting with Images
00:06:18
Displaying Images with Other Plots in the Figure
00:03:52
Plotting Data on a Map Using Basemap
00:05:23
Generating CAPTCHA
00:06:36
Understanding Logarithmic Plots
00:05:19
Creating a Stem Plot
00:04:18
Drawing Streamlines of Vector Flow
00:03:28
Using Colormaps
00:05:17
Using Scatter Plots and Histograms
00:04:29
Plotting the Cross Correlation Between Two Variables
00:03:27
The Importance of Autocorrelation
00:04:11
Drawing Barbs
00:06:24
Making a Box-and-Whisker Plot
00:03:37
Making Gantt Charts
00:03:50
Making Error Bars
00:04:40
Making Use of Text and Font Properties
00:04:00
Understanding the Difference between pyplot and OO API
00:05:13
Chapter 4 : Python Machine Learning Solutions
Preprocessing Data Using Different Techniques
00:06:15
Label Encoding
00:02:26
Building a Linear Regressor
00:04:26
Regression Accuracy and Model Persistence
00:03:41
Building a Ridge Regressor
00:02:41
Building a Polynomial Regressor
00:02:33
Estimating housing prices
00:03:46
Computing relative importance of features
00:01:54
Estimating bicycle demand distribution
00:04:35
Building a Simple Classifier
00:03:40
Building a Logistic Regression Classifier
00:04:51
Building a Naive Bayes’ Classifier
00:02:11
Splitting the Dataset for Training and Testing
00:01:23
Evaluating the Accuracy Using Cross-Validation
00:04:07
Visualizing the Confusion Matrix and Extracting the Performance Report
00:04:14
Evaluating Cars based on Their Characteristics
00:05:12
Extracting Validation Curves
00:02:49
Extracting Learning Curves
00:01:37
Extracting the Income Bracket
00:03:36
Building a Linear Classifier Using Support Vector Machine
00:04:24
Building Nonlinear Classifier Using SVMs
00:01:47
Tackling Class Imbalance
00:02:54
Extracting Confidence Measurements
00:02:37
Finding Optimal Hyper-Parameters
00:02:17
Building an Event Predictor
00:03:45
Estimating Traffic
00:02:40
Clustering Data Using the k-means Algorithm
00:03:08
Compressing an Image Using Vector Quantization
00:03:38
Building a Mean Shift Clustering
00:02:36
Grouping Data Using Agglomerative Clustering
00:03:05
Evaluating the Performance of Clustering Algorithms
00:02:56
Automatically Estimating the Number of Clusters Using DBSCAN
00:03:34
Finding Patterns in Stock Market Data
00:02:35
Building a Customer Segmentation Model
00:02:22
Building Function Composition for Data Processing
00:03:26
Building Machine Learning Pipelines
00:03:55
Finding the Nearest Neighbors
00:01:56
Constructing a k-nearest Neighbors Classifier
00:04:19
Constructing a k-nearest Neighbors Regressor
00:02:44
Computing the Euclidean Distance Score
00:02:09
Computing the Pearson Correlation Score
00:01:55
Finding Similar Users in a Dataset
00:01:35
Generating Movie Recommendations
00:02:35
Preprocessing Data Using Tokenization
00:03:00
Stemming Text Data
00:02:23
Converting Text to Its Base Form Using Lemmatization
00:02:11
Dividing Text Using Chunking
00:02:03
Building a Bag-of-Words Model
00:02:59
Building a Text Classifier
00:04:43
Identifying the Gender
00:02:18
Analyzing the Sentiment of a Sentence
00:03:10
Identifying Patterns in Text Using Topic Modelling
00:04:52
Reading and Plotting Audio Data
00:02:34
Transforming Audio Signals into the Frequency Domain
00:02:10
Generating Audio Signals with Custom Parameters
00:01:46
Synthesizing Music
00:02:10
Extracting Frequency Domain Features
00:02:06
Building Hidden Markov Models
00:02:19
Building a Speech Recognizer
00:03:12
Transforming Data into the Time Series Format
00:03:07
Slicing Time Series Data
00:01:32
Operating on Time Series Data
00:01:42
Extracting Statistics from Time Series
00:02:29
Building Hidden Markov Models for Sequential Data
00:04:16
Building Conditional Random Fields for Sequential Text Data
00:04:27
Analyzing Stock Market Data with Hidden Markov Models
00:02:26
Operating on Images Using OpenCV-Python
00:03:08
Detecting Edges
00:02:47
Histogram Equalization
00:02:31
Detecting Corners and SIFT Feature Points
00:03:47
Building a Star Feature Detector
00:01:35
Creating Features Using Visual Codebook and Vector Quantization
00:04:11
Training an Image Classifier Using Extremely Random Forests
00:02:30
Building an object recognizer
00:01:54
Capturing and Processing Video from a Webcam
00:01:58
Building a Face Detector using Haar Cascades
00:02:40
Building Eye and Nose Detectors
00:01:54
Performing Principal Component Analysis
00:02:17
Performing Kernel Principal Component Analysis
00:02:03
Performing Blind Source Separation
00:02:16
Building a Face Recognizer Using a Local Binary Patterns Histogram
00:04:14
Building a Perceptron
00:02:40
Building a Single-Layer Neural Network
00:01:37
Building a deep neural network
00:02:19
Creating a Vector Quantizer
00:01:41
Building a Recurrent Neural Network for Sequential Data Analysis
00:02:24
Visualizing the Characters in an Optical Character Recognition Database
00:01:48
Building an Optical Character Recognizer Using Neural Networks
00:02:28
Plotting 3D Scatter plots
00:02:43
Plotting Bubble Plots
00:01:16
Animating Bubble Plots
00:01:57
Drawing Pie Charts
00:01:34
Plotting Date-Formatted Time Series Data
00:01:33
Plotting Histograms
00:01:05
Visualizing Heat Maps
00:01:15
Animating Dynamic Signals
00:02:07
Chapter 5 : Deep Learning with Python
The Course Overview
00:03:52
What Is Deep Learning?
00:04:09
Open Source Libraries for Deep Learning
00:04:31
Deep Learning "Hello World!" Classifying the MNIST Data
00:07:57
Introduction to Backpropagation
00:05:24
Understanding Deep Learning with Theano
00:05:04
Optimizing a Simple Model in Pure Theano
00:07:54
Keras Behind the Scenes
00:05:24
Fully Connected or Dense Layers
00:04:46
Convolutional and Pooling Layers
00:06:40
Large Scale Datasets, ImageNet, and Very Deep Neural Networks
00:05:17
Loading Pre-trained Models with Theano
00:05:16
Reusing Pre-trained Models in New Applications
00:07:22
Theano "for" Loops – the "scan" Module
00:05:18
Recurrent Layers
00:06:28
Recurrent Versus Convolutional Layers
00:03:43
Recurrent Networks –Training a Sentiment Analysis Model for Text
00:06:50
Bonus Challenge – Automatic Image Captioning
00:04:41
Captioning TensorFlow – Google's Machine Learning Library
00:05:15

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