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Introduction to Data Science

By

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S.V.V.D.Jagadeesh

Sr. Assistant Professor

Dept of Artificial Intelligence & Data Science

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LAKIREDDY BALI REDDY COLLEGE OF ENGINEERING

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At the end of this unit, Student will be able to:

  • CO2: Apply the Machine Learning Activities on Data(Apply-L3)

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Unit-II Outcomes

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  • Machine learning is a field of study that gives computers the ability to learn without being explicitly programmed. – Arther Samuel
  • Machine learning is the process by which a computer can work more accurately as it collects and learns from the data it is given.- Mike Roberts�

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Machine Learning

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  • Regression and classification are of primary importance to a data scientist.
  • To achieve these goals, one of the main tools a data scientist uses is machine learning.
  • The uses for regression and automatic classification are wide ranging

■ Finding oil fields, gold mines, or archeological sites based on existing sites

■ Finding place names or persons in text (classification)

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Applications of ML in Data Science

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■ Identifying people based on pictures or voice recordings

■ Recognizing birds based on their whistle

■ Identifying profitable customers

■ Proactively identifying car parts that are likely to fail

■ Identifying tumors and diseases

■ Predicting the amount of money a person will spend on product X

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Applications of ML in Data Science

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■ Predicting the number of eruptions of a volcano in a period

■ Predicting your company’s yearly revenue

■ Predicting which team will win the Champions League in soccer

Sometimes Machine Learning can also be used to find the root cause of an action

■ Understanding and optimizing a business process, such as determining which products add value to a product line

■ Discovering what causes diabetes

■ Determining the causes of traffic jams

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Applications of ML in Data Science

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Python Tools Used in ML

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■ SciPy is a library that integrates fundamental packages often used in scientific computing such as NumPy, matplotlib, Pandas, and SymPy.

■ NumPy gives you access to powerful array functions and linear algebra functions.

■ Matplotlib is a popular 2D plotting package with some 3D functionality.

■ Pandas is a high-performance, but easy-to-use, data-wrangling package.

It introduces dataframes to Python, a type of in-memory data table.

It’s a concept that should sound familiar to regular users of R.

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Packages for Working with Data in Memory

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■ SymPy is a package used for symbolic mathematics and computer algebra.

■ StatsModels is a package for statistical methods and algorithms.

■ Scikit-learn is a library filled with machine learning algorithms.

■ RPy2 allows you to call R functions from within Python.

R is a popular open source statistics program.

■ NLTK (Natural Language Toolkit) is a Python toolkit with a focus on text analytics

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Packages for Working with Data in Memory

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■ Numba and NumbaPro—These use just-in-time compilation to speed up applications written directly in Python and a few annotations.

NumbaPro also allows you to use the power of your graphics processor unit (GPU).

■ PyCUDA—This allows you to write code that will be executed on the GPU instead of your CPU and is therefore ideal for calculation-heavy applications.

It works best with problems that lend themselves to being parallelized and need little input compared to the number of required computing cycles. shortfall.

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Optimizing Operations

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■ Cython, or C for Python—This brings the C programming language to Python.

C is a lower-level language, so the code is closer to what the computer eventually uses (bytecode).

The closer code is to bits and bytes, the faster it executes.

■ Blaze—Blaze gives you data structures that can be bigger than your computer’s main memory, enabling you to work with large data sets.

■ Dispy and IPCluster—These packages allow you to write code that can be distributed over a cluster of computers.

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Thursday, January 2, 2025

Optimizing Operations

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■ PP—Python is executed as a single process by default.

With the help of PP you can parallelize computations on a single machine or over clusters.

■ Pydoop and Hadoopy—These connect Python toHadoop, a common big data framework.

■ PySpark—This connects Python and Spark, an in-memory big data framework.

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Optimizing Operations

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  • Unit-II Outcomes
  • Machine Learning
  • Applications of ML in Data Science
  • Benefits and Challenges of Data Science
  • Python tools used in ML
  • Packages for Working With Data in Memory
  • Optimizing Operations

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Summary

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