Lesson: 1 What is Machine Learning? (Layman’s
Description: Lesson: 1 What is Machine Learning? (Laymans term) For understanding Deep Learning, first we need to know what is Machine Learning. In this lesson, we will try to understand machine learning from a Laymans term. Human can learn from
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slide1. Lesson: 1 What is Machine Learning? (Layman’s term) [ For understanding Deep Learning, first we need to know what is Machine Learning.
In this lesson, we will try to understand machine learning from a Layman’s term.]<br>
slide2. Human can learn from past experience
and make decision of its own 2<br>
slide3. What is this object? 3<br>
slide4. What is this object? 4 It is a CAR<br>
slide5. Let us ask the same question to him What is this object? 5<br>
slide6. Let us ask the same question to him What is this object? 6 ?<br>
slide7. [ But, he is a human being. He can observe and learn ]<br>
slide8. Let us make him learn show him 8<br>
slide9. Let us make him learn show him 9<br>
slide10. Let us ask the same question now What is this object? 10 Past experience<br>
slide11. Let us ask the same question now What is this object? 11 CAR<br>
slide12. Machines follow instructions 12 What about a Machine ? [ It can not take decision of its own]<br>
slide13. Machines follow instructions 13 What about a Machine ? We can ask a machine
To perform an arithmetic operations such as
Addition
Multiplication
Division<br>
slide14. Machines follow instructions 14 What about a Machine ? Comparison
Print
Plotting a chart [ But, we can ask a machine to make a decision of its own ]<br>
slide15. 15 What is Machine Learning? [ We want a machine to act like a human]<br>
slide16. 16 What is Machine Learning? [ to identify this object.]<br>
slide17. 17 What is Machine Learning? [ predict the price in future] Price in 2025?<br>
slide18. 18 What is Machine Learning? [ Natural Language understand, and correct grammar ] I made met him yesterday<br>
slide19. 19 What is Machine Learning? recognize face [ Recognize Faces ]<br>
slide20. 20 What is Machine Learning? [ What do we do?
Just like, what we did to human,
we need to provide experience to the machine.
]<br>
slide21. 21 What is Machine Learning? Dataset [
This what we called as Data or Training dataset
So, we first need to provide training dataset to the machine
] +<br>
slide22. 22 What is Machine Learning? Dataset + [ Then, devise algorithms and execute programs on the data
With respect to the underlying target tasks ] +<br>
slide23. 23 What is Machine Learning? Dataset + [ Then, using the programs, Identify required rules ] + +<br>
slide24. 24 What is Machine Learning? Dataset + [extract required patterns ] + +<br>
slide25. 25 What is Machine Learning? Dataset + [ Identify relations ] + +<br>
slide26. 26 What is Machine Learning? Dataset + [ So that machine can derive inferences from the data ] + + =<br>
slide27. In summary, what is machine learning? 27 Given a machine learning problem
Identify and create the appropriate dataset
Perform computation to learn
Required rules, pattern and relations
Output the decision<br>
slide28. Machine Learning Paradigms Supervised
Unsupervised Learning
Reinforcement learning 28 [ We as human being solve various types of problem in our day-to-day life, <pause> Various decisions need to be taken.
Depending on the nature of the problem, machine learning tasks can be broadly divided in ]<br>
slide29. What is Supervised Learning? 29 [In supervised learning, we need some thing called a Labelled Training Dataset ] CAR CAR BIKE BIKE Samples + Labels = Training Dataset<br>
slide30. What is Supervised Learning? 30 [ Given a labelled dataset, the task is to devise a function which takes the dataset, and a new sample, and produces an output value.] CAR CAR BIKE BIKE Samples + Labels = Training Dataset<br>
slide31. What is Supervised Learning? 31 [ Given a labelled dataset, the task is to devise a function which takes the dataset, and a new sample, and produces an output value.] CAR CAR BIKE BIKE Samples + Labels = Training Dataset<br>
slide32. What is Supervised Learning? 32 [ Given a labelled dataset, the task is to devise a function which takes the dataset, and a new sample, and produces an output value.] CAR CAR BIKE BIKE Samples + Labels = Training Dataset<br>
slide33. What is Supervised Learning? 33 [ If the possible output values of the function are predefined and discrete/categorical, it is called Classification CAR CAR BIKE BIKE Samples + Labels = Training Dataset Classification<br>
slide34. What is Supervised Learning? 34 [ Predefined classes means, it will produce output only from the labels defined in the dataset. For example, even if we input a bus, it will produce either CAR or BIKE ] CAR CAR BIKE BIKE Samples + Labels = Training Dataset Classification<br>
slide35. Classifier 35 Elephant Tiger Dataset Identify the Animal ? Classifier Elephant<br>
slide36. Regression 36 Dataset [ If the possible output values of the function are continuous real values, then it is called Regression Regression<br>
slide37. 37 [
The classification and Regression problems are supervised, because the decision depends on the characteristics of the ground truth labels or values present in the dataset, which we define as experience
]<br>
slide38. What is Unsupervised Learning 38 Dataset [ In the unsupervised learning, we do not need to know the labels or Ground truth values ] CAR CAR BIKE BIKE<br>
slide39. What is Unsupervised Learning 39 Dataset [ The task is to identify the patterns like group the similar objects together ] Clustering<br>
slide40. What is Unsupervised Learning 40 Dataset [ Association rules like ] Association Rules Mining<br>
slide41. More Example Unsupervised Learning 41 Dataset<br>
slide42. More Example Unsupervised Learning 42 Dataset<br>
slide43. 43 More Example Unsupervised Learning<br>
slide44. What is Reinforcement Learning 44 [ It is also known as learning from trials and errors ]<br>
slide45. What is Reinforcement Learning 45<br>
slide46. What is Reinforcement Learning 46<br>
slide47. What is Reinforcement Learning 47<br>
slide48. Another Example 48 Agent Task Environment<br>
slide49. Reinforcement Learning 49 Punishment<br>
slide50. Reinforcement Learning 50 Reward<br>
slide51. Reinforcement Learning 51 Reward Baby Learn from the Trials and Errors
Reinforcement Learning<br>
slide52. Summary [ In this lesion, we have learnt ] what is machine learning what are the machine learning paradigms<br>
In this lesson, we will try to understand machine learning from a Layman’s term.]<br>
slide2. Human can learn from past experience
and make decision of its own 2<br>
slide3. What is this object? 3<br>
slide4. What is this object? 4 It is a CAR<br>
slide5. Let us ask the same question to him What is this object? 5<br>
slide6. Let us ask the same question to him What is this object? 6 ?<br>
slide7. [ But, he is a human being. He can observe and learn ]<br>
slide8. Let us make him learn show him 8<br>
slide9. Let us make him learn show him 9<br>
slide10. Let us ask the same question now What is this object? 10 Past experience<br>
slide11. Let us ask the same question now What is this object? 11 CAR<br>
slide12. Machines follow instructions 12 What about a Machine ? [ It can not take decision of its own]<br>
slide13. Machines follow instructions 13 What about a Machine ? We can ask a machine
To perform an arithmetic operations such as
Addition
Multiplication
Division<br>
slide14. Machines follow instructions 14 What about a Machine ? Comparison
Plotting a chart [ But, we can ask a machine to make a decision of its own ]<br>
slide15. 15 What is Machine Learning? [ We want a machine to act like a human]<br>
slide16. 16 What is Machine Learning? [ to identify this object.]<br>
slide17. 17 What is Machine Learning? [ predict the price in future] Price in 2025?<br>
slide18. 18 What is Machine Learning? [ Natural Language understand, and correct grammar ] I made met him yesterday<br>
slide19. 19 What is Machine Learning? recognize face [ Recognize Faces ]<br>
slide20. 20 What is Machine Learning? [ What do we do?
Just like, what we did to human,
we need to provide experience to the machine.
]<br>
slide21. 21 What is Machine Learning? Dataset [
This what we called as Data or Training dataset
So, we first need to provide training dataset to the machine
] +<br>
slide22. 22 What is Machine Learning? Dataset + [ Then, devise algorithms and execute programs on the data
With respect to the underlying target tasks ] +<br>
slide23. 23 What is Machine Learning? Dataset + [ Then, using the programs, Identify required rules ] + +<br>
slide24. 24 What is Machine Learning? Dataset + [extract required patterns ] + +<br>
slide25. 25 What is Machine Learning? Dataset + [ Identify relations ] + +<br>
slide26. 26 What is Machine Learning? Dataset + [ So that machine can derive inferences from the data ] + + =<br>
slide27. In summary, what is machine learning? 27 Given a machine learning problem
Identify and create the appropriate dataset
Perform computation to learn
Required rules, pattern and relations
Output the decision<br>
slide28. Machine Learning Paradigms Supervised
Unsupervised Learning
Reinforcement learning 28 [ We as human being solve various types of problem in our day-to-day life, <pause> Various decisions need to be taken.
Depending on the nature of the problem, machine learning tasks can be broadly divided in ]<br>
slide29. What is Supervised Learning? 29 [In supervised learning, we need some thing called a Labelled Training Dataset ] CAR CAR BIKE BIKE Samples + Labels = Training Dataset<br>
slide30. What is Supervised Learning? 30 [ Given a labelled dataset, the task is to devise a function which takes the dataset, and a new sample, and produces an output value.] CAR CAR BIKE BIKE Samples + Labels = Training Dataset<br>
slide31. What is Supervised Learning? 31 [ Given a labelled dataset, the task is to devise a function which takes the dataset, and a new sample, and produces an output value.] CAR CAR BIKE BIKE Samples + Labels = Training Dataset<br>
slide32. What is Supervised Learning? 32 [ Given a labelled dataset, the task is to devise a function which takes the dataset, and a new sample, and produces an output value.] CAR CAR BIKE BIKE Samples + Labels = Training Dataset<br>
slide33. What is Supervised Learning? 33 [ If the possible output values of the function are predefined and discrete/categorical, it is called Classification CAR CAR BIKE BIKE Samples + Labels = Training Dataset Classification<br>
slide34. What is Supervised Learning? 34 [ Predefined classes means, it will produce output only from the labels defined in the dataset. For example, even if we input a bus, it will produce either CAR or BIKE ] CAR CAR BIKE BIKE Samples + Labels = Training Dataset Classification<br>
slide35. Classifier 35 Elephant Tiger Dataset Identify the Animal ? Classifier Elephant<br>
slide36. Regression 36 Dataset [ If the possible output values of the function are continuous real values, then it is called Regression Regression<br>
slide37. 37 [
The classification and Regression problems are supervised, because the decision depends on the characteristics of the ground truth labels or values present in the dataset, which we define as experience
]<br>
slide38. What is Unsupervised Learning 38 Dataset [ In the unsupervised learning, we do not need to know the labels or Ground truth values ] CAR CAR BIKE BIKE<br>
slide39. What is Unsupervised Learning 39 Dataset [ The task is to identify the patterns like group the similar objects together ] Clustering<br>
slide40. What is Unsupervised Learning 40 Dataset [ Association rules like ] Association Rules Mining<br>
slide41. More Example Unsupervised Learning 41 Dataset<br>
slide42. More Example Unsupervised Learning 42 Dataset<br>
slide43. 43 More Example Unsupervised Learning<br>
slide44. What is Reinforcement Learning 44 [ It is also known as learning from trials and errors ]<br>
slide45. What is Reinforcement Learning 45<br>
slide46. What is Reinforcement Learning 46<br>
slide47. What is Reinforcement Learning 47<br>
slide48. Another Example 48 Agent Task Environment<br>
slide49. Reinforcement Learning 49 Punishment<br>
slide50. Reinforcement Learning 50 Reward<br>
slide51. Reinforcement Learning 51 Reward Baby Learn from the Trials and Errors
Reinforcement Learning<br>
slide52. Summary [ In this lesion, we have learnt ] what is machine learning what are the machine learning paradigms<br>