**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

Resolve the captcha to access the links!