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Artificial Intelligence Course | AI Tutorial For Beginners | Artificial Intelligence Training | Simplilearn

This Artificial Intelligence presentation will help you understand what is Artificial Intelligence, types of Artificial Intelligence, ways of achieving Artificial Intelligence and applications of Artificial Intelligence. In the end, we will also implement a use case on TensorFlow in which we will predict whether a person has diabetes or not. Artificial Intelligence is a method of making a computer, a computer-controlled robot or a software think intelligently in a manner similar to the human mind. AI is accomplished by studying the patterns of the human brain and by analyzing the cognitive process. Artificial Intelligence is emerging as the next big thing in the technology field. Organizations are adopting AI and budgeting for certified professionals in the field, thus the demand for trained and certified professionals in AI is increasing. As this new field continues to grow, it will have an impact on everyday life and lead to considerable implications for many industries. Now, let us deep dive into the AI tutorial video and understand what is this Artificial Intelligence all about and how it can impact human life.

The topics covered in this Artificial Intelligence presentation are as follows:

1. What is Artificial intelligence?
2. Types of Artificial intelligence
3. Ways of achieving artificial intelligence
4. Applications of Artificial intelligence
5. Use case - Predicting if a person has diabetes or not

Simplilearn’s Artificial Intelligence course provides training in the skills required for a career in AI. You will master TensorFlow, Machine Learning and other AI concepts, plus the programming languages needed to design intelligent agents, deep learning algorithms & advanced artificial neural networks that use predictive analytics to solve real-time decision-making problems without explicit programming.

Why learn Artificial Intelligence?
The current and future demand for AI engineers is staggering. The New York Times reports a candidate shortage for certified AI Engineers, with fewer than 10,000 qualified people in the world to fill these jobs, which according to Paysa earn an average salary of $172,000 per year in the U.S. (or Rs.17 lakhs to Rs. 25 lakhs in India) for engineers with the required skills.

Those who complete the course will be able to:

1. Master the concepts of supervised and unsupervised learning
2. Gain practical mastery over principles, algorithms, and applications of machine learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of machine learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, Naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
Comprehend the theoretic

Learn more at: https://www.simplilearn.com

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Artificial Intelligence Course | AI Tutorial For Beginners | Artificial Intelligence Training | Simplilearn

  1. 1. Artificial Intelligence
  2. 2. What’s in it for you? Ways of achieving artificial intelligence Types of Artificial intelligence Applications ofArtificial Intelligence Use case - predicting if a person has diabetes or not What is Artificial Intelligence? content
  3. 3. Brief History of Artificial Intelligence The word ‘Artificial Intelligence’ coined by John McCarthy ‘Shakey’ was the first general purpose mobile robot built Supercomputer ‘Deep blue’ was designed which defeated the world Chess champion in a game First commercially successful robotic vacuum cleaner created Speech recognition, RPA, dancing robots, smart homes and many more to come from AI 1956 1969 1997 2002 2005-2018 What is Artificial Intelligence?
  4. 4. What is Artificial Intelligence? Hey! What am I?
  5. 5. What is Artificial Intelligence? You are what we call ‘Artificial Intelligence’
  6. 6. What is Artificial Intelligence? I am your creator
  7. 7. What is Artificial Intelligence? Artificial Intelligence is a branch of Computer Science dedicated to creating intelligent machines that work and react like humans.
  8. 8. What is Artificial Intelligence? Thanks! Any task you want me to do for you?
  9. 9. What is Artificial Intelligence? Get me a cup of coffee?
  10. 10. What is Artificial Intelligence?
  11. 11. What is Artificial Intelligence? Here you go!
  12. 12. Brief History of Artificial Intelligence The word ‘Artificial Intelligence’ coined by John McCarthy ‘Shakey’ was the first general purpose mobile robot built Supercomputer ‘Deep blue’ was designed which defeated the world Chess champion in a game First commercially successful robotic vacuum cleaner created Speech recognition, RPA, dancing robots, smart homes and many more to come from AI 1956 1969 1997 2002 2005-2018 Types of Artificial Intelligence
  13. 13. Types of Artificial Intelligence Hi there! I have discovered four different types of Ai. Come have a look!
  14. 14. This kind of AI are purely reactive and do not hold the ability to form memories or use past experiences to make decisions.These machines are designed to do specific jobs Types of Artificial Intelligence Reactive machines
  15. 15. Types of Artificial Intelligence Reactive machines This kind of AI are purely reactive and do not hold the ability to form memories or use past experiences to make decisions.These machines are designed to do specific jobs Limited memory This kind of AI uses past experience and the present data to make a decision. Self driving cars are a kind of limited memory AI
  16. 16. Theory of mind These ai machines can socialize and understand human emotions. Machines with such abilities are yet to be built Types of Artificial Intelligence Reactive machines This kind of AI are purely reactive and do not hold the ability to form memories or use past experiences to make decisions.These machines are designed to do specific jobs Limited memory This kind of AI uses past experience and the present data to make a decision. Self driving cars are a kind of limited memory AI
  17. 17. Self awareness this is the future ofAi.These machines will be super intelligent, sentient and conscious Theory of mind Types of Artificial Intelligence Reactive machines This kind of AI are purely reactive and do not hold the ability to form memories or use past experiences to make decisions.These machines are designed to do specific jobs Limited memory These ai machines can socialize and understand human emotions. Machines with such abilities are yet to be built This kind of AI uses past experience and the present data to make a decision. Self driving cars are a kind of limited memory AI
  18. 18. Brief History of Artificial Intelligence The word ‘Artificial Intelligence’ coined by John McCarthy ‘Shakey’ was the first general purpose mobile robot built Supercomputer ‘Deep blue’ was designed which defeated the world Chess champion in a game First commercially successful robotic vacuum cleaner created Speech recognition, RPA, dancing robots, smart homes and many more to come from AI 1956 1969 1997 2002 2005-2018 Achieving Artificial Intelligence
  19. 19. Achieving Artificial Intelligence Machine learning Machine Learning provides Artificial Intelligence with the ability to ‘Learn’.This is achieved by using algorithms that discover patterns and generate insights from the data they are exposed to
  20. 20. Achieving Artificial Intelligence Deep Learning Deep learning provides artificial intelligence the ability to mimic a human brain’s neural network. It can make sense of patterns, noise and sources of confusion in the data Machine learning Machine Learning provides Artificial Intelligence with the ability to ‘Learn’.This is achieved by using algorithms that discover patterns and generate insights from the data they are exposed to
  21. 21. Achieving Artificial Intelligence – Deep Learning let’s try to segregate different kinds of photos using Deep Learning
  22. 22. Achieving Artificial Intelligence – Deep Learning Photographs We provide a large set of photographs for the machine to segregate
  23. 23. Achieving Artificial Intelligence – Deep Learning Photographs The machine goes through features of every photo to distinguish them
  24. 24. Achieving Artificial Intelligence – Deep Learning Photographs This is called ‘feature extraction’ Bingo!
  25. 25. Achieving Artificial Intelligence – Deep Learning Labeled photographs landscapes portraits others Segregated photos Based on the features of each photo, it segregates them Bingo!
  26. 26. Achieving Artificial Intelligence – Deep Learning Let’s see how deep learning works!
  27. 27. Achieving Artificial Intelligence – Deep Learning This is a neural network
  28. 28. There are three main layers in a neural network Achieving Artificial Intelligence – Deep Learning
  29. 29. The photos that we want to segregate go into the input layer Input layer Achieving Artificial Intelligence – Deep Learning
  30. 30. The hidden layers are responsible for all the mathematical computations or feature extraction on our inputs Input layer Hidden layers Achieving Artificial Intelligence – Deep Learning
  31. 31. The accuracy of the predicted output generally depends on the number of hidden layers we have Input layer Hidden layers Achieving Artificial Intelligence – Deep Learning
  32. 32. The output layer gives us the segregated photos Input layer Hidden layers Output layer portrait Landscape Achieving Artificial Intelligence – Deep Learning
  33. 33. Let’s predict the airline ticket prices using machine learning Achieving Artificial Intelligence – Machine Learning
  34. 34. These are the factors based on which we are going to make the predictions Achieving Artificial Intelligence – Machine Learning
  35. 35. Origin airport Destination airportDeparture date airlines Achieving Artificial Intelligence – Machine Learning
  36. 36. Here are some historical data of ticket prices to train the machine Old data Achieving Artificial Intelligence – Machine Learning
  37. 37. Now that our machine is trained, let’s give it new data for which it will predict the prices Old data New data Achieving Artificial Intelligence – Machine Learning
  38. 38. Old data New data The price is $1000!! Achieving Artificial Intelligence – Machine Learning
  39. 39. Brief History of Artificial Intelligence The word ‘Artificial Intelligence’ coined by John McCarthy ‘Shakey’ was the first general purpose mobile robot built Supercomputer ‘Deep blue’ was designed which defeated the world Chess champion in a game First commercially successful robotic vacuum cleaner created Speech recognition, RPA, dancing robots, smart homes and many more to come from AI 1956 1969 1997 2002 2005-2018 Applications of Artificial Intelligence
  40. 40. The room is dark isn’t it?
  41. 41. Let’s see what happens when I enter it!
  42. 42. The sensors in my room detect my presence and switch on the lights
  43. 43. That is one of the many applications of Artificial intelligence
  44. 44. That’s one of the many applications of artificial intelligence Ok bye!
  45. 45. Let’s watch some tv!!
  46. 46. Did someone say tv?
  47. 47. The sound sensors on the tv detect my voice and turn on the tv!
  48. 48. Here, you have one more application of artificial intelligence
  49. 49. Brief History of Artificial Intelligence The word ‘Artificial Intelligence’ coined by John McCarthy ‘Shakey’ was the first general purpose mobile robot built Supercomputer ‘Deep blue’ was designed which defeated the world Chess champion in a game First commercially successful robotic vacuum cleaner created Speech recognition, RPA, dancing robots, smart homes and many more to come from AI 1956 1969 1997 2002 2005-2018 Use Case – Predict if a person has Diabetes
  50. 50. Use Case Hi! I’ll be helping you out with the use case
  51. 51. Use Case The problem statement is to predict if a person has diabetes or not! Predict if a patient has diabetes based on previous test data Problem Statement
  52. 52. Use Case The features are…! Number of times pregnant
  53. 53. Use Case The features are…! Number of times pregnant Glucose concentration
  54. 54. Use Case The features are…! Number of times pregnant Glucose concentration Blood pressure
  55. 55. Use Case The features are…! Number of times pregnant Glucose concentration Blood pressure age
  56. 56. Use Case The features are…! Number of times pregnant Glucose concentration Blood pressure age insulin
  57. 57. Use Case Let’s start off with the code!
  58. 58. Use Case #loading dataset import pandas as pd diabetes = pd.read_csv('pima-indians-diabetes.csv’) diabetes.head()
  59. 59. Use Case #Cleaning Data cols_to_norm = ['Number_pregnant', 'Glucose_concentration', 'Blood_pressure', 'Triceps','Insulin', 'BMI', 'Pedigree’] diabetes[cols_to_norm] = diabetes[cols_to_norm].apply(lambda x: (x - x.min()) / (x.max() - x.min())) diabetes.head()
  60. 60. Use Case #importing tensorflow Import tensorflow as tf diabetes.columns
  61. 61. Use Case #categorical features assigned_group = tf.feature_column.categorical_column_with_vocabulary_list ('Group',['A','B','C','D']) #converting continuous to categorical import matplotlib.pyplot as plt %matplotlib inline diabetes['Age'].hist(bins=20)
  62. 62. Use Case age_buckets = tf.feature_column.bucketized_column(age, boundaries=[20,30,40,50,60,70,80]) #combining all the features feat_cols = [num_preg ,plasma_gluc,dias_press ,tricep ,insulin,bmi,diabetes_pedigree ,assigned_group, age_buckets] #splitting the data x_data = diabetes.drop('Class',axis=1) labels = diabetes['Class’] from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split(x_data,labels,test_size=0.33, random_state=101)
  63. 63. Use Case #applying input function input_func = tf.estimator.inputs.pandas_input_fn(x=X_train,y=y_train,ba tch_size=10,num_epochs=1000,shuffle=True) #creating the model model = tf.estimator.LinearClassifier(feature_columns=feat_cols,n_c lasses=2) model.train(input_fn=input_func,steps=1000)
  64. 64. Use Case #prediction pred_input_func = tf.estimator.inputs.pandas_input_fn( x=X_test, batch_size=10, num_epochs=1, shuffle=False) predictions = model.predict(pred_input_func) list(predictions)
  65. 65. Use Case #evaluating the model eval_input_func = tf.estimator.inputs.pandas_input_fn( x=X_test, y=y_test, batch_size=10, num_epochs=1, shuffle=False) results = model.evaluate(eval_input_func) results
  66. 66. Use Case So, we have managed to have an accuracy of 71% and that’s quite good for our model!
  67. 67. Use Case - Conclusion So, we created a model that can predict if a person has diabetes based on some previous records of people who were diagnosed with diabetes
  68. 68. Use Case - Conclusion The model was implemented on python using tensorflow
  69. 69. Key Takeaways
  70. 70. So what’s your next step?

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