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How to use Artificial Intelligence with Python? Edureka

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This Edureka PPT on "Artificial Intelligence With Python" will provide you with a comprehensive and detailed knowledge of Artificial Intelligence concepts with hands-on examples.

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How to use Artificial Intelligence with Python? Edureka

  1. 1. WHY PYTHON FOR AI? FEATURES OF PYTHON INTRODUCTION TO ARTIFICIAL INTELLIGENCE INTRODUCTION TO MACHINE LEARNING www.edureka.co PYTHON PACKAGES FOR AI MACHINE LEARNING ALGORITHMS INTRODUCTION TO DEEP LEARNING NATURAL LANGUAGE PROCESSING (NLP) TEXT MINING
  2. 2. WHY PYTHON FOR AI? www.edureka.co
  3. 3. Less code Pre-built libraries Platform Independent Massive Community Support Ease of learning www.edureka.co
  4. 4. Python is an open-source, object-oriented programming language mainly used for Data Science. StackOverflow calls it as the fastest growing programming language. www.edureka.co
  5. 5. PYTHON PACKAGES FOR AI www.edureka.co
  6. 6. 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 www.edureka.co
  7. 7. 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 www.edureka.co
  8. 8. Numpy is a python library mainly used for computing scientific/mathematical data. NumPy Features of NumPy • Supports multi-dimensional arrays • Numerical analysis • Intuitive www.edureka.co
  9. 9. 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 www.edureka.co
  10. 10. 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 www.edureka.co
  11. 11. 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 www.edureka.co
  12. 12. INTRODUCTION TO ARTIFICIAL INTELLIGENCE www.edureka.co
  13. 13. More Computational Power More Data Better algorithms Broad Investment www.edureka.co
  14. 14. “The science and engineering of making intelligent machines” John McCarthy first coined the term Artificial Intelligence in the year 1956. www.edureka.co
  15. 15. 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 www.edureka.co
  16. 16. 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. www.edureka.co
  17. 17. AI VS ML VS DL www.edureka.co
  18. 18. 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 www.edureka.co
  19. 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 www.edureka.co
  20. 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 www.edureka.co
  21. 21. INTRODUCTION TO MACHINE LEARNING www.edureka.co
  22. 22. 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 www.edureka.co
  23. 23. MACHINE LEARNING PROCESS www.edureka.co
  24. 24. 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 www.edureka.co
  25. 25. 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 www.edureka.co
  26. 26. Data such as weather conditions, humidity level, temperature, pressure, etc are either collected manually or scarped from the web. Step 2: Data Gathering www.edureka.co
  27. 27. 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 www.edureka.co
  28. 28. 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 www.edureka.co
  29. 29. 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 www.edureka.co
  30. 30. 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 www.edureka.co
  31. 31. The final outcome is predicted after performing parameter tuning and improving the accuracy of the model. Step 7: Predictions www.edureka.co
  32. 32. TYPES OF MACHINE LEARNING www.edureka.co
  33. 33. 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 www.edureka.co
  34. 34. 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 www.edureka.co
  35. 35. 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 www.edureka.co
  36. 36. Supervised vs Unsupervised vs Reinforcement Learning www.edureka.co
  37. 37. TYPES OF PROBLEMS SOLVED USING MACHINE LEARNING www.edureka.co
  38. 38. 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 www.edureka.co
  39. 39. MACHINE LEARNING ALGORITHMS www.edureka.co
  40. 40. www.edureka.co
  41. 41. LIMITATIONS OF MACHINE LEARNING www.edureka.co
  42. 42. 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 www.edureka.co
  43. 43. 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 www.edureka.co
  44. 44. INTRODUCTION TO DEEP LEARNING www.edureka.co
  45. 45. 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. www.edureka.co
  46. 46. 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 www.edureka.co
  47. 47. Deep Learning is a collection of statistical machine learning techniques used to learn feature hierarchies based on the concept of artificial neural networks. www.edureka.co
  48. 48. "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. www.edureka.co
  49. 49. 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 www.edureka.co
  50. 50. A Multilayer Perceptron with backpropagation can be used to solve this problem. Dealing with non-linearly separable data: www.edureka.co
  51. 51. 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. www.edureka.co
  52. 52. • 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. www.edureka.co
  53. 53. The Backpropagation algorithm is a supervised learning method for Multilayer Perceptron. Maximum weight is assigned to the most important lead/input. www.edureka.co
  54. 54. NATURAL LANGUAGE PROCESSING (NLP) www.edureka.co
  55. 55. 1,736,111 pictures 204,000,000 emails 4,166,667 likes & 200,000 photos 347,222 tweets www.edureka.co
  56. 56. Structured Data Unstructured Data 21 % www.edureka.co
  57. 57. WHAT IS TEXT MINING? www.edureka.co
  58. 58. 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. www.edureka.co
  59. 59. Text Mining is the logic behind Autocomplete. Autocomplete, suggests the rest of the word. Autocomplete Spam Detection www.edureka.co
  60. 60. Predictive typing Spell checker www.edureka.co
  61. 61. WHAT IS NLP? www.edureka.co
  62. 62. 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 www.edureka.co
  63. 63. 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) www.edureka.co
  64. 64. APPLICATIONS OF NLP www.edureka.co
  65. 65. 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. www.edureka.co
  66. 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. Human language Chatbots use NLP to convert human language into desirable actions. www.edureka.co Computer Science Artificial Intelligence
  67. 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 NLP can be used in Machine Translation by study the morphological analysis of each word and translate it into another language. www.edureka.co Computer Science Artificial Intelligence
  68. 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 Advertisement Matching uses NLP to recommend ads based on your history. www.edureka.co Computer Science Artificial Intelligence
  69. 69. TERMINOLOGIES IN NLP www.edureka.co
  70. 70. 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 www.edureka.co
  71. 71. 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 www.edureka.co
  72. 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 www.edureka.co
  73. 73. Normalize words into its base form or root form Detected Detection DetectingDetections Detect Stemming www.edureka.co
  74. 74. • 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 www.edureka.co
  75. 75. Are stop words helpful? www.edureka.co
  76. 76. 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 www.edureka.co
  77. 77. Copyright © 2017, edureka and/or its affiliates. All rights reserved. www.edureka.co
  78. 78. www.edureka.co

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