This document provides an introduction to machine learning using Python. It outlines the agenda which includes introductions to machine learning and Python as well as a demonstration project using a neural network. Key concepts about machine learning are defined, including the differences between classical and machine learning algorithms. The main types of machine learning are described - supervised, unsupervised, semi-supervised and reinforcement learning. Python is introduced as a programming language for machine learning applications. The document concludes with an outline of a deep learning project in Python using Keras to load data, design a neural network model, train and validate the model for predictions.

1. INTRODUCTION TO MACHINE
LEARNING USING PYTHON
BY: MAHENDRA GUPTA
LECTURER,
GOVT. POLYTECHNIC COLLEGE ANUPPUR, M.P.-484224
MOBLE: 9582429861
EMAIL: mahendragupta2211@gmail.com
: https://github.com/mahendragupta91
WEBSITE: www.learndifferences.org

2. AGENDA
MACHINE LEARNING INTRODUCTION PYTHON INTRODUCTION PROJECT DEMO USING NEURAL
NETWORK
BUDDHA INSTITUTE OF TECHNOLOGY, GORAKHPUR,UP

3. MACHINE LEARNING
Herbert Alexander Simon:
(Turing Award 1975, Nobel Prize in Economics 1978)
“Learning is any process by which a system
improves performance from experience.”
“Machine Learning is concerned with computer
programs that automatically improve their
performance through experience. “
BUDDHA INSTITUTE OF TECHNOLOGY, GORAKHPUR,UP

4. MACHINE LEARNING
 It does not require extra programming
effort to make predictions or learning new
environment.
 Machine learning algorithms build
a mathematical model based on sample
data
 Machine learning focuses on the
development of computer programs that
can access data and use it learn for
themselves
BUDDHA INSTITUTE OF TECHNOLOGY, GORAKHPUR,UP

5. CLASSICAL ALGORITHMS Vs Machine Learning Algorithms
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6. MACHINE LEARNING: EXAMPLE
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7. MACHINE LEARNING: EXAMPLE
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8. MACHINE LEARNING
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9. TYPES OF LEARNING
Supervised Learning
Unsupervised Learning
Semi-supervised Learning
Reinforcement Learning
BUDDHA INSTITUTE OF TECHNOLOGY, GORAKHPUR,UP

10. SUPERVISED LEARNING
 Supervised learning is when the model is getting trained on a labelled dataset.
 Labelled dataset is one which have both input and output parameters.
 Classification Is Supervised learning algorithms:
Decision Tree, Neural Network, SVM,
Regression Analysis etc. are Classification Techniques.
Steps involves in Classification
 Designing Model
 Training the Model
 Validation
 Prediction ( on new data)
BUDDHA INSTITUTE OF TECHNOLOGY, GORAKHPUR,UP

11. UNSUPERVISED LEARNING
 Here the training data does not include labels.
 The system has to learn on its own.
 It is mainly contains with techniques that involves the
grouping of objects
Clustering is a unsupervised learning algorithm.
BUDDHA INSTITUTE OF TECHNOLOGY, GORAKHPUR,UP

12. SEMI-SUPERVISED LEARNING
 Labeled may not be available or its is
insufficient for training.
 In this situation Semi-Supervised Learning
is used.
 Semi-supervised learning is an approach
to machine learning that combines a small
amount of labeled data with a large
amount of unlabeled data during training.
BUDDHA INSTITUTE OF TECHNOLOGY, GORAKHPUR,UP

13. REINFORCEMENT LEARNING
 Reinforcement learning is an area of
Machine Learning.
 It is about taking suitable action to
maximize reward in a particular situation.
 in reinforcement learning, there is no
answer but the reinforcement agent
decides what to do to perform the given
task.
 In the absence of a training dataset, it is
bound to learn from its experience.
 Reinforcement learning is all about making
decisions sequentially.
 output depends on the state of the current input
and the next input depends on the output of the
previous input.
 In Reinforcement learning decision is dependent,
So we give labels to sequences of dependent
decisions
BUDDHA INSTITUTE OF TECHNOLOGY, GORAKHPUR,UP

14. PYTHON PROGRAMMING
 Python is a dynamic, high level, free open
source and interpreted programming language.
 It supports object-oriented programming as well
as procedural oriented programming.
 it is a dynamically typed language.
 GUI Programming Support: PyQt5, PyQt4,
wxPython, or Tk in python.
 Extensible feature:
 Python is Portable language
Python Packages:
NumPy: Handling matrices
OpenCV : Image Processing
Keras : Collection of Neural Network
Tkinter and wxPython : UI widgets
PyTorch and TensorFlow :
Accelerated training and handling input and
output.
BUDDHA INSTITUTE OF TECHNOLOGY, GORAKHPUR,UP

15. IDEs Python Programming
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16. PROJECT OUTLINE
Deep Learning Project in Python with Keras.
1. Load Data.
2. Design Neural Network Model(Using Keras
Model)
3. Compile the Model
4. Train the Neural Network Model
5. Validate (testing) the model
6. Use Model for Predictions for new Data
BUDDHA INSTITUTE OF TECHNOLOGY, GORAKHPUR,UP

17. REFERENCES
1. https://en.wikipedia.org/wiki/Semi-
supervised_learning#:~:text=Semi%2Dsupervised%20learning%20is%20an,with%20only%20labeled%20training%20d
ata).
2. https://medium.com/inside-machine-learning/placeholder-3557ebb3d470
3. https://learndifferences.org/data-information-and-knowledge-differences.php
4. https://www.geeksforgeeks.org/what-is-reinforcement-learning/
5. https://www.youtube.com/watch?v=XvZsiWj6VuU
6. https://machinelearningmastery.com/tutorial-first-neural-network-python-keras/
BUDDHA INSTITUTE OF TECHNOLOGY, GORAKHPUR,UP

18. THANK YOU
mahendragupta2211@gmail.co
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