IPython Interactive Computing and Visualization Cookbook
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Free ChapterA Tour of Interactive Computing with Jupyter and IPython
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Data Visualization
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Statistical Data Analysis
- Lesson Overview
- Statistical Hypothesis Testing
- Coding Problems
- Exploring a Dataset with Pandas and Matplotlib
- Bayes Theorem and Posterior Probability
- Plotting Distribution using Bayes Theorem
- Chi-Squared Test
- Maximum Likelihood Estimation
- Fitting a Probability Distribution to Data
- Estimating a Probability Distribution
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Machine Learning
- Lesson Overview
- Cross Validation
- Support Vector Machines
- Linear Regression and Ridge Regression
- Logistic Regression
- K- nearest neighbors classifier
- Naïve Bayes Classifier
- Using Support Vector Machines for classification tasks
- Using Random Forest
- Principle Compliant Analysis and Detecting
- Detecting Hidden Structures in a Dataset with Clustering
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Numerical Optimization
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Signal Processing
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Image and Audio Processing
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Deterministic Dynamical Systems
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Stochastic Dynamical Systems
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Graphs, Geometry, and Geographic Information Systems
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Symbolic and Numerical Mathematics
About this
video
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.
- Publication date:
- June 2019
- Publisher
- Packt
- Duration
- 9 hours 7 minutes
- ISBN
- 9781838823603
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