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2. WHY PYTHON FOR AI?
FEATURES OF PYTHON
INTRODUCTION TO ARTIFICIAL INTELLIGENCE
INTRODUCTION TO MACHINE LEARNING
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PYTHON PACKAGES FOR AI
MACHINE LEARNING ALGORITHMS
INTRODUCTION TO DEEP LEARNING
NATURAL LANGUAGE PROCESSING (NLP)
TEXT MINING

3. WHY PYTHON FOR AI?
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4. Less code
Pre-built libraries
Platform
Independent
Massive Community
Support
Ease of learning
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5. Python is an open-source, object-oriented programming language mainly used for Data Science.
StackOverflow calls it as the fastest growing
programming language.
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6. PYTHON PACKAGES FOR AI
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7. Tensorflow library was developed by Google in collaboration with Brain Team. It is popularly used in
writing Machine Learning algorithms.
Tensorflow
Features of Tensorflow
• Responsive construct
• Flexible
• Easily trainable
• Parallel neural network training
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8. Scikit-learn is a Python library associated with NumPy and SciPy. It is considered as one of the best
libraries for working with complex data.
Scikit-learn
Features of Scikit-learn
• Cross validation
• Unsupervised learning algorithms
• Feature extraction
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9. Numpy is a python library mainly used for computing scientific/mathematical data.
NumPy
Features of NumPy
• Supports multi-dimensional arrays
• Numerical analysis
• Intuitive
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10. Theano is a Python library that allows you to define, optimize, and evaluate mathematical
expressions involving multi-dimensional arrays efficiently.
Theano
Features of Theano
• Tight integration with NumPy
• Transparent use of a GPU
• Extensive unit-testing and self-verification
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11. Keras simplifies the implementation of neural networks. It also provides some of the best utilities for
compiling models, processing data-sets, visualization of graphs, and much more.
Keras
Features of Keras
• Runs smoothly on both CPU & GPU
• Supports all types of Neural Networks
• Completely Python based
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12. The Natural Language Toolkit (NLTK) is an open source Python library for Natural Language
Processing, text analysis and text mining.
NLTK
Features of NLTK
• Study natural language text
• Text analysis
• Sentimental analysis
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13. INTRODUCTION TO ARTIFICIAL INTELLIGENCE
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14. More Computational
Power More Data
Better algorithms
Broad Investment
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15. “The science and engineering of making intelligent machines”
John McCarthy first coined the term Artificial
Intelligence in the year 1956.
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16. The theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual
perception, speech recognition, decision-making and translation between languages.
Machine Learning
Deep Learning
NLP
Computer Vision
Knowledge Base
Expert System
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17. Artificial Narrow Intelligence Artificial General Intelligence Artificial Super Intelligence
The theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual
perception, speech recognition, decision-making and translation between languages.
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18. AI VS ML VS DL
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19. Artificial Intelligence
Machine Learning
Deep Learning
ARTIFICIAL INTELLIGENCE
A technique which enables machines
to mimic human behaviour
MACHINE LEARNING
Subset of AI technique which use
statistical methods to enable machines
to improve with experience
DEEP LEARNING
Subset of ML which make the
computation of multi-layer neural
network feasible
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20. Artificial Intelligence
Machine Learning
Deep Learning
ARTIFICIAL INTELLIGENCE
A technique which enables machines
to mimic human behaviour
MACHINE LEARNING
Subset of AI technique which use
statistical methods to enable machines
to improve with experience
DEEP LEARNING
Subset of ML which make the
computation of multi-layer neural
network feasible
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21. Artificial Intelligence
Machine Learning
Deep Learning
ARTIFICIAL INTELLIGENCE
A technique which enables machines
to mimic human behaviour
MACHINE LEARNING
Subset of AI technique which use
statistical methods to enable machines
to improve with experience
DEEP LEARNING
Subset of ML which make the
computation of multi-layer neural
network feasible
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22. INTRODUCTION TO MACHINE LEARNING
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23. Machine learning is a subset of Artificial Intelligence (AI) which provides machines the ability to
learn automatically & improve from experience without being explicitly programmed.
What Is Machine Learning?
Data Training the Machine Building a Model Predicting Outcome
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24. MACHINE LEARNING PROCESS
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25. The Machine Learning process involves building a Predictive model that can be used to
find a solution for a Problem Statement.
MACHINE LEARNING PROCESS
DefineObjective
DataGathering
PreparingData
DataExplorationBuildingaModel
ModelEvaluation
Predictions
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26. To predict the possibility of rain by studying the weather conditions.
Step 1: Define the objective of the Problem
• Whatarewetryingtopredict?
• Whatarethetargetfeatures?
• Whatistheinputdata?
• What kind of problem are we facing? Binary classification?
Clustering?
WeatherForecastusing
MachineLearning
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27. Data such as weather conditions, humidity level, temperature, pressure, etc are either
collected manually or scarped from the web.
Step 2: Data Gathering
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28. Data Cleaning involves getting rid of inconsistencies in data such as missing values
or redundant variables.
Step 3: Preparing Data
• Transformdataintodesiredformat
• Datacleaning
• Missingvalues
• Corrupteddata
• Removeunnecessarydata
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29. Data Exploration involves understanding the patterns and trends in the data. At this
stage all the useful insights are drawn and correlations between the variables are
understood.
Step 4: Exploratory Data Analysis
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30. At this stage a Predictive Model is built by using Machine Learning Algorithms such as
Linear Regression, Decision Trees, etc.
Step 5: Building a Machine Learning Model
• MachineLearningmodelisbuiltbyusingthetrainingdataset
• The model is the Machine Learning algorithm that predicts the
outputbyusingthedatafedtoit
TrainingData
MachineLearning
Model
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31. The efficiency of the model is evaluated and any further improvement in the model are
implemented.
Step 6: Model Evaluation & Optimization
• Machine Learning model is evaluated by using the testing data
set
• Theaccuracyofthemodeliscalculated
• Further improvement in the model are done by using techniques
likeParametertuning
MachineLearningModel
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32. The final outcome is predicted after performing parameter tuning and improving the
accuracy of the model.
Step 7: Predictions
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33. TYPES OF MACHINE LEARNING
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34. Supervised learning is a technique in which we teach or train the machine
using data which is well labelled.
Supervised Learning
Tom
Tom
Tom
Jerry
Jerry Jerry
Labelled Data
Class ‘Jerry’
Class ‘Tom’
Labelled Output
Known Input
Training phase
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35. Unsupervised learning is the training of machine using information that is unlabeled and
allowing the algorithm to act on that information without guidance.
Unsupervised Learning
Unlabelled Data
Understand patterns & discover outputUnknown Input
Unlabelled Output
Clusters formed based on
feature similarity
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36. Reinforcement Learning is a part of Machine learning where an agent is put in an
environment and he learns to behave in this environment by performing certain actions
and observing the rewards which it gets from those actions.
Reinforcement Learning
Tom or
jerry? Agent
Environment
state
reward action
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37. Supervised vs Unsupervised vs Reinforcement Learning
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38. TYPES OF PROBLEMS SOLVED USING
MACHINE LEARNING
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39. Regression vs Classification vs Clustering
Regression Classification Clustering
• Output is a continuous
quantity
• Output is a categorical
quantity
• Assigns data points into
clusters
• Supervised Learning • Supervised Learning • Unsupervised Learning
• Main aim is to forecast or
predict
• Main aim is to compute the
category of the data
• Main aim is to group similar
items clusters
• Eg: Predict stock market price • Eg: Classify emails as spam or
non-spam
• Eg: Find all transactions which
are fraudulent in nature
• Algorithm: Linear Regression • Algorithm: Logistic Regression • Algorithm: K-means
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40. MACHINE LEARNING ALGORITHMS
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41. www.edureka.co

42. LIMITATIONS OF MACHINE LEARNING
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43. https://www.kaspersky.com/blog/tip-of-the-week-how-to-get-rid-of-unwanted-emails/3005/
LIMITATIONS OF ML
High Dimensional data
Image Recognition
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44. One of the big challenges with traditional Machine Learning models is a process called feature extraction. For
complex problems such as object recognition or handwriting recognition, this is a huge challenge.
Deep Learning
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45. INTRODUCTION TO DEEP LEARNING
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46. Deep Learning models are capable to focus on the right features by themselves, requiring little
guidance from the programmer. These models also partially solve the dimensionality problem.
Why Deep Learning?
The idea behind Deep Learning is to build learning algorithms that mimic the brain.
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47. https://www.kaspersky.com/blog/tip-of-the-week-how-to-get-rid-of-unwanted-emails/3005/
HOW DL WORKS?
Deep learning is a form of machine learning that uses a model of computing that's
very much inspired by the structure of the brain.
Biological Neuron
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48. Deep Learning is a collection of statistical machine learning techniques used to learn feature hierarchies based
on the concept of artificial neural networks.
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49. "What we enjoy from more modern, advanced machine learning is its ability to consume a lot more data, handle layers
and layers of abstraction and be able to 'see' things that a simpler technology would not be able to see, even human
beings might not be able to see," - Wang.
• PayPal uses Deep Learning models to identify fraudulent
activities
• Over four billion transactions are processed
• Deep learning algorithms implement pattern detection to
predict whether a particular transaction is fraudulent or not.
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50. An Artificial Neuron or a Perceptron is a linear model used for binary classification. It models a neuron which
has a set of inputs, each of which is given a specific weight. The neuron computes some function on these
weighted inputs and gives the output.
Perceptron or Artificial Neuron
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51. A Multilayer Perceptron with backpropagation can be used to solve this problem.
Dealing with non-linearly separable data:
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52. A Multi-layer Perceptron has the same structure of a single layer perceptron but with one or more hidden
layers and is thus considered a deep neural network.
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53. • The weights between the units are the primary means of long-term information storage in neural networks
• Updating the weights is the primary way the neural network learns new information
A set of inputs are passed to the first hidden layer, the activations from that layer are passed to the next layer
and so on, until you reach the output layer.
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54. The Backpropagation algorithm is a supervised learning method for Multilayer Perceptron.
Maximum weight is assigned to the most important lead/input.
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55. NATURAL LANGUAGE PROCESSING (NLP)
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56. 1,736,111 pictures
204,000,000 emails
4,166,667 likes &
200,000 photos
347,222 tweets
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57. Structured Data
Unstructured Data
21 %
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58. WHAT IS TEXT MINING?
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59. https://www.kaspersky.com/blog/tip-of-the-week-how-to-get-rid-of-unwanted-emails/3005/
WHAT IS TEXT
MINING?
Text Mining / Text Analytics is the process of deriving meaningful
Information from natural language text.
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60. Text Mining is the logic behind
Autocomplete.
Autocomplete, suggests the rest of the
word.
Autocomplete Spam Detection
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61. Predictive typing Spell checker
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62. WHAT IS NLP?
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63. https://www.kaspersky.com/blog/tip-of-the-week-how-to-get-rid-of-unwanted-emails/3005/
WHAT IS NLP?
Natural Language Processing is a part of computer science and artificial
intelligence which deals with human languages.
Computer
Science
Artificial
Intelligence
Human
language
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64. Text Mining is
the process of deriving high quality
information from the text .
The overall goal is, to turn text into
data for analysis, via application of
Natural Language Processing (NLP)
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65. APPLICATIONS OF NLP
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66. https://www.kaspersky.com/blog/tip-of-the-week-how-to-get-rid-of-unwanted-emails/3005/
WHAT IS NLP?
Natural Language Processing is a part of computer science and artificial
intelligence which deals with human languages.
Computer
Science
Artificial
Intelligence
Human
language
Sentimental Analysis mainly used to analyse social
media content can help us determine the public
opinion on a certain topic.
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67. https://www.kaspersky.com/blog/tip-of-the-week-how-to-get-rid-of-unwanted-emails/3005/
WHAT IS NLP?
Natural Language Processing is a part of computer science and artificial
intelligence which deals with human languages.
Human
language
Chatbots use NLP to convert human language into
desirable actions.
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Computer
Science
Artificial
Intelligence

68. https://www.kaspersky.com/blog/tip-of-the-week-how-to-get-rid-of-unwanted-emails/3005/
WHAT IS NLP?
Natural Language Processing is a part of computer science and artificial
intelligence which deals with human languages.
Human
language
NLP can be used in Machine Translation by study
the morphological analysis of each word and
translate it into another language.
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Computer
Science
Artificial
Intelligence

69. https://www.kaspersky.com/blog/tip-of-the-week-how-to-get-rid-of-unwanted-emails/3005/
WHAT IS NLP?
Natural Language Processing is a part of computer science and artificial
intelligence which deals with human languages.
Human
language
Advertisement Matching uses NLP to recommend
ads based on your history.
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Computer
Science
Artificial
Intelligence

70. TERMINOLOGIES IN NLP
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71. The process of splitting the whole data (corpus) into smaller chunks
is known as tokenization
01 Break a complex sentence into words
02 Understand the importance of each of the words with respect to the
sentence
03 Produce a structural description on an input sentence
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72. 01 Break a complex sentence into words
02 Understand the importance of each of the words with respect to the
sentence
03 Produce a structural description on an input sentence
Tokens are simple
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73. 01 Break a complex sentence into words
02 Understand the importance of each of the words with respect to the
sentence
03 Produce a structural description on an input sentence
Tokens are simple
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74. Normalize words into its base form or root form
Detected Detection DetectingDetections
Detect
Stemming
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75. • Groups together different inflected forms of a
word, called Lemma
• Somehow similar to Stemming, as it maps
several words into one common root
• Output of Lemmatisation is a proper word
• For example, a Lemmatiser should
map gone, going and went into go
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76. Are stop words helpful?
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77. Doc 1
Doc 2
Doc 3
Doc 4
This is fun
1 1 1
0 1 0
1 0 0
1 1 0
Documents Document Term Matrix
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78. Copyright © 2017, edureka and/or its affiliates. All rights reserved.
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79. www.edureka.co