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Video Description
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 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 Edition

Learning Python Data Analysis

Python Data Visualization Solutions

Python Machine Learning Solutions

Deep Learning with Python


This 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)

Table of Contents
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
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
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
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
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
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
Using Subplots 00:03:57
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