IPython Interactive Computing and Visualization Cookbook: Sharpen your high-performance numerical computing and data science skills with Jupyter Notebook

IPython Interactive Computing and Visualization Cookbook: Sharpen your high-performance numerical computing and data science skills with Jupyter Notebook
IPython Interactive Computing and Visualization Cookbook: Sharpen your high-performance numerical computing and data science skills with Jupyter Notebook
English | MP4 | AVC 1920×1080 | AAC 48KHz 2ch | 9h 07m | 13.4 GB

Learn to use IPython and Jupyter Notebook for your data analysis and visualization work.

Python is one of the leading open source platforms for data science and numerical computing. IPython and the associated Jupyter Notebook offer efficient interfaces to Python for data analysis and interactive visualization, and they constitute an ideal gateway to the platform.

This course is equipped with several ready-to-use, focused recipes for high-performance scientific computing and data analysis to help you write better and faster code. You’ll be able to apply your learnings to various real-world examples, ranging from applied mathematics, scientific modeling, to machine learning. The course introduces you to effective programming techniques such as code quality and reproducibility, code optimization, and graphics card programming. You’ll also learn how to use different features of IPython and Jupyter Notebook in data science, signal and image processing, and applied mathematics.

By the end of this course, you’ll learn how to easily analyze and visualize all types of data in Jupyter Notebook.

Learn

  • Visualize data and create interactive plots in the Jupyter Notebook
  • Write fast Python programs with NumPy, ctypes, Numba, and other libraries
  • Analyze data with Bayesian or frequentist statistics
  • Simulate deterministic and stochastic dynamical systems in Python
  • Get familiar with math in Python using SymPy and Sage
  • Profile and optimize your code and conduct reproducible interactive computing experiments
Table of Contents

A Tour of Interactive Computing with Jupyter and IPython
1 Course Overview
2 Installation and Setup
3 Basics of Jupyter Notebook
4 NumPy arrays
5 Introduction to Interact feature of IPython
6 Use of NumPy Arrays
7 Writing Functions
8 H5py Files

Data Visualization
9 Styles of Matplotlib
10 Statistical Plots with Seaborn
11 Regression Plots
12 Bokeh Library
13 Interactive Visualization Library

Statistical Data Analysis
14 Lesson Overview
15 Statistical Hypothesis Testing
16 Coding Problems
17 Exploring a Dataset with Pandas and Matplotlib
18 Bayes Theorem and Posterior Probability
19 Plotting Distribution using Bayes Theorem
20 Chi-Squared Test
21 Maximum Likelihood Estimation
22 Fitting a Probability Distribution to Data
23 Estimating a Probability Distribution

Machine Learning
24 Lesson Overview
25 Cross Validation
26 Support Vector Machines
27 Linear Regression and Ridge Regression
28 Logistic Regression
29 K- nearest neighbors classifier
30 Naïve Bayes Classifier
31 Using Support Vector Machines for classification tasks
32 Using Random Forest
33 Principle Compliant Analysis and Detecting
34 Detecting Hidden Structures in a Dataset with Clustering

Numerical Optimization
35 Lesson Overview
36 Finding the Root of a Mathematical Function
37 Fitting a function to data with non-linear least squares

Signal Processing
38 Signal Processing
39 Analyzing the signal frequency component with a Fast Fourier Transform
40 Applying a Linear Filter to a Digital Signal
41 Computing the Auto Co-relation of a Time Series

Image and Audio Processing
42 Image and Audio Processing
43 Edge Detection
44 Derivatives
45 Sobel Edge Detection
46 Erosion and Dilation Intuition
47 Manipulating the Exposure and Applying Filters
48 Image Segmentation
49 Finding Points of Interests in an Image
50 Detecting Faces in an Image with OpenCV
51 Applying Digital Filters to Speech Sounds

Deterministic Dynamical Systems
52 Types of Dynamical Systems
53 Simulating an Ordinary Differential Equation with SciPy
54 Simulating a Partial Differential Equation

Stochastic Dynamical Systems
55 Simulating a Discrete-time Markov Chain
56 Simulating a Poisson Process
57 Simulating a Stochastic Differential Equation

Graphs, Geometry, and Geographic Information Systems
58 Graphs, Geometry and Geographic Information Systems
59 Manipulating and Visualizing Graphs with NetworkX
60 Resolving Dependencies in a Directed Acyclic Graph with a Topological Sort
61 Computing Connected Components in an Image

Symbolic and Numerical Mathematics
62 Diving into Symbolic Computing with SymPy
63 Solving Equations and Inequalities
64 Analyzing Real-Valued Functions
65 Computing Exact Probabilities and Manipulating Random Variables
66 Number Theory with SymPy
67 Analyzing a Nonlinear Differential System