PYTHON AND MACHINE
LEARNING FOR COMPLETE
BEGINNERS
BY UDEMY.
SHARAVANA KUMAR S
22AD122
COURSE COMPOSED OF?
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BASICS AND ADVANCED PYTHON
HANDLING ERRORS
OOPS
GUI AND GAME OF LIFE
MODULES:PACKAGING
FUNCTIONAL PROGRAMMING AND FILES
NUMPY
GRAPHS AND PLOTTING
PANDAS AND REGRESSION
CLUSTERING
NALVE BAYES
DECISION TREES
ARTIFICIAL NEURAL NETWORKS
WHAT IS MACHINE LEARNING?
The word Artificial Intelligence comprises of two words “Artificial” and “Intelligence”. Artificial
refers to something which is made by humans or non-natural thing and Intelligence means
the ability to understand or think.
AI is the study of how to train the computers so that computers can do things which at
present human can do better. Therefore, AI is an intelligence where we want to add all the
capabilities to machine that human contains.
WHAT IS MACHINE LEARNING?
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Machine Learning (ML) is an automated learning with little or no human intervention. It
involves programming computers so that they learn from the available inputs. The main
purpose of machine learning is to explore and construct algorithms that can learn from the
previous data and make predictions on new input data
The capability of Artificial Intelligence systems to learn by extracting patterns from data is
known as Machine Learning.
Machine Learning is an idea to learn from examples and experience, without being explicitly
programmed. Instead of writing code, you feed data to the generic algorithm, and it builds
logic based on the data given.
Python implements popular machine learning techniques such as Classification,
Regression, Recommendation, and Clustering.
Python offers ready-made framework for performing data mining tasks on large volumes of
data effectively in lesser time
APPLICATIONS OF MACHINE ELARNING:
●Web
search
●Computational biology
●Finance
●E-commerce
●Space exploration
●Robotics
●Information extraction
●Social networks
●Debugging
●Data mining
●Expert systems
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Vision processing
Language processing
Forecasting things like stock
market trends, weather
Pattern recognition
Games
Robotics
NUMPY:
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NumPy is a Python library used for working with arrays.
It also has functions for working in domain of linear algebra, fourier transform, and matrices.
NumPy was created in 2005 by Travis Oliphant. It is an open source project and you can use
it freely.
NumPy stands for Numerical Python.
Why Use NumPy?
In Python we have lists that serve the purpose of arrays, but they are slow to process.
NumPy aims to provide an array object that is up to 50x faster than traditional Python lists.
The array object in NumPy is called ndarray, it provides a lot of supporting functions that
make working with ndarray very easy.
Arrays are very frequently used in data science, where speed and resources are very
important.
PANDAS:
Pandas is an open-source library in Python that is made mainly for working with relational or labeled
data both easily and intuitively. It provides various data structures and operations for manipulating
numerical data and time series. This library is built on top of the NumPy library of Python. Pandas is
fast and it has high performance & productivity for users.
Why Use Pandas?
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Fast and efficient for manipulating and analyzing data.
Data from different file objects can be easily loaded.
Flexible reshaping and pivoting of data sets
Provides time-series functionality.
What can you do using Pandas?
Pandas are generally used for data science but have you wondered why? This is because
pandas are used in conjunction with other libraries that are used for data science. It is built
on the top of the NumPy library which means that a lot of structures of NumPy are used or
replicated in Pandas. The data produced by Pandas are often used as input for plotting
functions of Matplotlib, statistical analysis in SciPy, and machine learning algorithms
in Scikit-learn. Here is a list of things that we can do using Pandas.
APPLICATIONS:
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Data set cleaning, merging, and joining.
Easy handling of missing data (represented as NaN) in floating point as well as nonfloating point data.
Columns can be inserted and deleted from DataFrame and higher dimensional objects.
Powerful group by functionality for performing split-apply-combine operations on data
sets.
Data Visulaization
DECISION TREES
A decision tree is a non-parametric supervised learning algorithm, which is utilized for both
classification and regression tasks. It has a hierarchical, tree structure, which consists of a root node,
branches, internal nodes and leaf nodes.
ARTIFICIAL NEURAL NETWORKS
The term "Artificial Neural Network" is derived from Biological neural networks that
develop the structure of a human brain. Similar to the human brain that has neurons
interconnected to one another, artificial neural networks also have neurons that are
interconnected to one another in various layers of the networks. These neurons are
known as nodes.
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Parallel processing capability:
Artificial neural networks have a numerical value that can perform more than one task
simultaneously.
Storing data on the entire network:
Data that is used in traditional programming is stored on the whole network, not on a
database. The disappearance of a couple of pieces of data in one place doesn't
prevent the network from working.
Capability to work with incomplete knowledge:
After ANN training, the information may produce output even with inadequate data. The
loss of performance here relies upon the significance of missing data.
Having a memory distribution:
For ANN is to be able to adapt, it is important to determine the examples and to
encourage the network according to the desired output by demonstrating these
examples to the network. The succession of the network is directly proportional to the
chosen instances, and if the event can't appear to the network in all its aspects, it can
produce false output.
Having fault tolerance:
Extortion of one or more cells of ANN does not prohibit it from generating output, and
this feature makes the network fault-tolerance.