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Primer to Machine Learning
- 1. Primer to Machine Learning or How to get Machines to Think Jeff Tanner, Engineer @ TUNE
- 2. Take Away from this Talk: What underlies Machine Learning by explaining how it Learns.
- 4. Why?: Curiosity 1.Math driven education Physics and Applied Mathematics 2.first love of application programming was Artifical Intelligence and Neural Nets 3. unsatiated Curiosity to understand Machine Learning
- 5. Graduate and Postgraduate: Artificial Intelligence International consultant: Knowledge Engineering
- 6. Bright idea #1 Learn more about Machine Learning Satisfy Curiosity (and perform upon functional test on rebooted brain)
- 7. Read lots and Watched lots (repeat many times) Siraj RavalAndrew Ng
- 8. Bright idea #2 Eager to Share Curiosity Lecture
- 10. Topics: ● What is it? ● What can it do? ● Explain pipeline? ● Data ● Types ● Algorithms ● Shallow dive
- 11. Code: ● Breakout to checkout: ○ Python Scikit-Learn ○ GNU Octave (MatLab) ○ R Programming https://github.com/jefftune/gitw-2017-ml
- 12. What is Machine Learning?
- 13. What is Machine Learning? Learn from Experience People
- 14. Learn from Experience Follow instructions People Traditional Programming Models What is Machine Learning?
- 15. Learn from Experience Follow instructions Learn from Data People Machine Learning Models Traditional Programming Models What is Machine Learning?
- 16. Machine Learning is an artificial intelligence technology learning over time in an autonomous fashion without being explicitly programmed
- 17. What Machine Learning Can Do: ● Data Modeling ● Classification ● Value Prediction ● Serving Content ● Pattern Recognition
- 18. What Machine Learning Cannot Do: ● Clean the Data ● Leap Over Pareto’s Principle
- 19. Relationship: AI, ML, and DL
- 20. Statistical Learning underlies Machine Learning
- 21. ● Mechanophobia (fear of machines) ● Sophophobia (fear of learning) ● Mechano-Sophophobia? Still scared of Machine Learning?
- 22. ● Easy to believe that ML is hard. ● Embrace predictions.
- 23. Defining “Learning” of Machines
- 24. 1959, Arthur Samuel Machine learning is the practice of giving computers the ability to learn without being explicitly programmed to do so.
- 25. 1997, Tom Mitchell A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P. E * T = P
- 26. Learning Algorithm Cat / Not Cat Cat: 80% this is a cat E Training Prediction T P Model E * T = P
- 27. Checkers: E * T = P E: practice T: playing P: % won
- 28. Handwriting: E * T = P E: handwritten words T: classifying words P: % words
- 29. Self-Driving: E * T = P E: observe human driver T: drive P: % human judged error
- 30. Using to Machine Learning Algorithms
- 31. Supervised: Predict Value Profitin$10,000 Population in 10,000 Profitability
- 34. Reinforcement: Bot Conversation Example Charlotte BrontëThomas Holcroft Christopher Marlowe
- 35. Awareness is like consciousness. Soul is like spirit. But soft is not like hard and weak is not like strong. A mechanic can be both soft and hard, a stewardess can be both weak and strong. This is called philosophy or a world-view.
- 36. BILL. I love a child. MARCELLA. Children are fortunately captivating. BILL. Yet my love is excellent. MARCELLA. My love is spooky yet we must have a child, a spooky child. BILL. Do you follow me?
- 37. What is the Machine Learning pipeline?
- 38. Machine Learning Pipeline a framework converting raw data to usable data training an ML algorithm deliver trained model using model to perform actions
- 40. Machine Learning Pipeline: Training Phase Gather lots of Data, Prepare for Learning.
- 41. Machine Learning Pipeline: Training Phase Training Data is passed to a Learning Algorithm, Create a Hypothesis, check Cost against Weighted Errors.
- 42. Machine Learning Pipeline: Training Phase Generate Model using the Hypothesis with the Least Weighted Errors.
- 47. The Phase of “Learning”
- 56. Tool: R-Programming: Data and Feature Engineering
- 57. Tool: GNU Octave: Algorithm Design
- 58. Tool: Python Scikit-Learn: ML Delivery
- 59. Fictional Prediction discovered doing Machine Learning with R-Programming
- 60. Titanic Passengers List Should Rose have stayed on the lifeboat?
- 61. Titanic Passengers List Jack had a higher likelihood to survive if Rose stayed on the lifeboat and already saved. Jack (single)
- 62. Titanic Passengers List Instead, an extra hour of Jack and Rose running around a sink ship, finalizing with frozen Jack, and Rose saved (again). Thanks Rose. Jack (attached)
- 63. How is Data handling within the Machine Learning pipeline?
- 64. Data Lexicon: Linear Algebra
- 67. Data Lexicon: Data Scientists
- 68. ● Dataset is a Matrix of measures or events. ● Measure has a set of useful Features. ● Feature is either undefined or has a value from a normalized set.
- 69. ● Dataset is a Matrix of measures or events. ● Measure has a set of useful Features. ● Feature is either undefined or has a value from a normalized set.
- 70. ● Dataset is a Matrix of measures or events. ● Measure has a set of useful Features. ● Feature is either undefined or has a value from a normalized set.
- 71. Data Handling
- 76. Types of ML Algorithms
- 78. Types of Machine Learning ● Predictive Model, Labeled data ● Classification ● Numeric prediction In supervised algorithms, you may not know the inner relations of the data you are processing, but you do know very well which is the output that you need from your model. The training of the model usually uses part of the data to “learn”, and part of the data to validate and measure how accurate the model is.
- 80. Types of Machine Learning ● Descriptive model, Unlabeled data ● Clustering ● Pattern discovery With unsupervised algorithms, you still don’t know what you want to get out of the model yet. You probably suspect that there hast to be some kinds of relationships or correlation between the data you have, but data is too complex to try to guess.
- 82. Types of Machine Learning ● Rewards based modeling Reinforcement learning is the field that studies the problems and techniques that try to retro-feed it’s model in order to improve. Relies on being able to monitor the response of the actions taken, and measure against a definition of a “reward”.
- 85. Types of Machine Learning ● When Labelled Data is Costly A class of supervised learning tasks and techniques that also make use of unlabeled data for training. Typically a small amount of labeled data with a large amount of unlabeled data.
- 87. The Math of Intelligence
- 88. You do not need to know the following to deliver Machine Learning solutions
- 89. However… If you truly want to understand or create Machine “learning” algorithms, then Math is helpful.
- 90. And… Understanding the Math of “learning” is addictively fun, Ask my spouse, or maybe not
- 91. These are the most important math concepts that underlie Machine Learning and I will demonstrate where they are applied to “Learning”. Gradient Descent Cross-Entropy Loss Bayes’ Theorem K-Means Linear Regression
- 92. Calculus Linear Algebra Probability Theory Statistics And these math concepts are based upon the fundamentals of ...
- 93. Basic Algebra Have no fear! Applied Mathematics breaks down all Math behind Machine Learning to ...
- 94. This is the Math of Intelligence behind how Machines learn from Data
- 97. regression models the past relationship between variables to predict their future behavior. regression analysis is a set of statistical processes for estimating the relationships among variables. from Statistical Learning, the underpinnings of ML
- 98. Example: Food truck Profit to Population Profitability Profitin$10,000 Population in 10,000 As CEO of a restaurant franchise and are considering different cities for opening a new outlet. The chain already has food trucks in various cities and you have data for Profits and Populations from the cities.
- 99. Food truck Profit to Population Profitability Quiz: What would be the approximate Profit if Food Truck is located in City with Population 150,000? [ ] $50,000 [ ] $100,000 [ ] $150,000 [ ] $200,000 Profitin$10,000 Population in 10,000
- 100. Food truck Profit to Population Profitability Quiz: What would be the approximate Profit if Food Truck is located in City with Population 150,000? [ ] $50,000 [ ] $100,000 [X] $150,000 [ ] $200,000 $138,000 Population in 10,000 Profitin$10,000
- 101. Food truck Profit to Population Profitability Population in 10,000 Profitin$10,000 Linear Regression Algorithm finding the straight line that best-fits the values of a linear function, plotted on a scatter graph as data points, used as the basis for estimating the future values.
- 102. Food truck Profit to Population Profitability Population in 10,000 Profitin$10,000 linear regression line
- 103. ● Data is Food Truck Profit to Population ● Algorithm for Training is using Linear Regression ● Model when Trained will values filled into this function:
- 104. Finding “Linear Regression Line” An actual data point instance:
- 105. Finding “Linear Regression Line” Provide a line of slope and the line intersects axis at location And for a known data point then y-intercept is for this line is
- 106. Finding “Linear Regression Line”
- 107. Finding “Linear Regression Line”
- 108. Finding “Linear Regression Line”
- 109. Finding “Linear Regression Line”
- 110. Finding “Linear Regression Line”
- 111. Finding “Linear Regression Line” a.k.a Global Minimum
- 112. x : population f(x) : predicted profit
- 113. Non-Linear or Polynomial Regression
- 114. Linear Regression Algorithm: Summary Find the linear regression line that best cuts the value data in two in order to make the best value prediction with the least possible error.
- 116. University Admissions Acceptance Using historical data from previous applicants, predict whether a student gets admitted into a university based upon the applicant’s scores on two exams. Exam2score Exam 1 score Admitted Not-Admitted
- 117. University Admissions Acceptance Exam2score Exam 1 score Admitted Not-Admitted Quiz: For a student with scores for Exam 1 at 45 and Exam 2 at 85, does the get accepted? [ ] Yes [ ] No
- 118. University Admissions Acceptance Exam2score Exam 1 score Quiz: For a student with scores for Exam 1 at 45 and Exam 2 at 85, does the get accepted? [X] Yes [ ] No We predict an admission with confidence of 77.6289 %
- 119. University Admissions Acceptance Exam2score Exam 1 score Logistic Regression Algorithm Predicts the probability that an observation falls into one of two categories of a dichotomous dependent variable based on one or more independent variables.
- 120. ● Data is University Admissions: Exam 1, Exam 2, Class: Admitted ● Algorithm for Training is using Logistic Regression ● Model when Trained will values filled into this function:
- 121. Logistic Regression Algorithm It is an logistic regression model where the dependent variable is binary and categorical, that is, where it can take only two values: “0” OR “1”, representing for example: Yes or No Accept or Not Accept Cat or Dog 1 0 0.5
- 122. Logistic Regression Algorithm Example: Swallow: African OR European Based on Airspeed velocity. sigmoid
- 123. Logistic Regression Algorithm “Exam 1” x1 “Exam 2” x2
- 125. Logistic Regression Algorithm prediction_start prediction_min
- 127. Gradient Descent using Log-Loss Log Loss quantifies the accuracy of a classifier by penalising false classifications.
- 129. x1, x2 : exams f(x1, x2) : university acceptance
- 130. Logistic Regression Algorithm: Summary Find the logistic regression line that best cuts the classifications in two in order to make the best class decision with the least possible error.
- 132. University Admissions AcceptanceExam2score Exam 1 score What if the admitted into a university based upon the applicant’s scores on two exams was more restrictive? Admitted Not-Admitted
- 133. University Admissions AcceptanceExam2score Exam 1 score Admitted Not-Admitted And there were fewer actual admissions in the historical data.
- 134. University Admissions AcceptanceExam2score Exam 1 score Admitted Not-Admitted A single line will not cut the data in two.
- 135. University Admissions AcceptanceExam2score Exam 1 score Admitted Not-Admitted Maybe a circle
- 136. Maybe two lines, which works better. This solution requires using Neural Network Algorithm University Admissions AcceptanceExam2score Exam 1 score
- 138. Supervised: Decision Tree: Fruit
- 139. Determine Which Fruit (4, 2) ['Weight (grams)', 'Texture'] ['apple', 'orange']
- 140. Determine Which Fruit Weight Texture Class 150g Bumpy Orange 170g Bumpy Orange 150g Smooth Apple 130g Smooth Apple (4, 2) ['Weight (grams)', 'Texture'] ['apple', 'orange']
- 141. Determine Which Fruit Weight Texture Class 150g Bumpy Orange 170g Bumpy Orange 150g Smooth Apple 130g Smooth Apple Quiz: Between Weight and Texture, which seems more decisive for predicting what Fruit will be determined? [ ] Weight [ ] Texture
- 142. Determine Which Fruit Weight Texture Class 150g Bumpy Orange 170g Bumpy Orange 150g Smooth Apple 130g Smooth Apple Quiz: Between Weight and Texture, which seems more decisive for predicting what Fruit will be determined? [ ] Weight [ ] Texture
- 143. Determine Which Fruit Weight Texture Class 150g Bumpy Orange 170g Bumpy Orange 150g Smooth Apple 130g Smooth Apple Quiz: Between Weight and Texture, which seems more decisive for predicting what Fruit will be determined? [ ] Weight [ ] Texture
- 144. Determine Which Fruit Weight Texture Class 150g Bumpy Orange 170g Bumpy Orange 150g Smooth Apple 130g Smooth Apple Quiz: Between Weight and Texture, which seems more decisive for predicting what Fruit will be determined? [ ] Weight [X] Texture
- 145. Determine Which Fruit Weight Texture Class 150g Bumpy Orange 170g Bumpy Orange 150g Smooth Apple 130g Smooth Apple Decision Tree Algorithm
- 146. Supervised: Decision Tree: Iris
- 147. Classification of Iris Varieties
- 148. Classification of Iris Varieties (150, 4) ['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)'] ['setosa', 'versicolor', 'virginica'] [ 5.1 3.5 1.4 0.2] setosa
- 149. Classification of Iris Varieties: Decision Tree
- 150. Classification of Iris Varieties: Decision Tree Accuracy Score 0.911111111111 Accuracy Score 0.622222222222
- 151. Classification of Iris Varieties: Decision Tree [2 0 0 0 0 0 0 0 0 1 0 0 0 1 1 2 0 0 0 2 2 0 2 2 1 1 2 1 1 0 1 0 1 2 0 1 2 0 2 2 0 1 2 2 1 1 2 2 1 2 2 2 0 0 1 1 1 1 1 1 2 1 2 2 0 0 2 0 2 2 0 2 1 0 2] Predictions [2 2 1 1 2 0 0 0 2 2 1 0 0 0 1 0 0 0 1 0 0 2 2 1 0 2 2 1 0 1 1 0 2 1 0 2 2 0 0 0 2 2 1 1 1 1 1 1 2 0 1 2 1 0 2 0 2 1 0 2 1 2 2 0 0 0 2 1 0 1 2 0 1 2 0] Accuracy Score 0.96
- 153. Classification of Iris Varieties: Random Forest
- 154. Classification of Iris Varieties: Random Forest Accuracy Score 0.622222222222 Accuracy Score 0.955555555556
- 155. Classification of Iris Varieties: Random Forest [2 1 0 2 0 2 0 1 1 1 2 1 1 1 1 0 1 1 0 0 2 1 0 0 2 0 0 1 1 0 2 1 0 2 2 1 0 1 1 1 2 0 2 0 0] Predictions [2 1 0 2 0 2 0 1 1 1 1 1 1 1 1 0 1 1 0 0 2 1 0 0 2 0 0 1 1 0 2 1 0 2 2 1 0 2 1 1 2 0 2 0 0] Accuracy Score 0.955555555556
- 158. Detecting Spam e-mails 100 emails looked at already
- 159. Detecting Spam e-mails Spam Not-Spam
- 160. Detecting Spam e-mails Spam Not-Spam “Winner”
- 161. Detecting Spam e-mails Spam Not-Spam “Winner” Quiz: If an e-mail contains the word “Winner”, what is the Probability of being Spam? [ ] 40% [ ] 60% [ ] 80%
- 162. Detecting Spam e-mails Spam Not-Spam “Winner” Quiz: If an e-mail contains the word “Winner”, what is the Probability of being Spam? [ ] 40% [ ] 60% [ X ] 80% Conclusion: If an e-mail contains the word “Winner”, then the Probability it being Spam is 80%. 80 % 20 %
- 163. Detecting Spam e-mails “Winner” 80 % Spelling error 60 % Missing title 90 % etc....
- 164. Detecting Spam e-mails “Winner” 80 % Spelling error 60 % Missing title 90 % Naive Bayes Algorithm
- 166. Tools to build ML Learning Pipeline
- 167. Tools to build ML Learning Pipeline Python has a rich set of packages for delivering Machine Learning solutions.
- 168. Tools to build ML Learning Pipeline GNU Octave is an open source high-level programming language intended for numerical computations. Great for understanding and defining Algorithms within Machine Learning.Andrew Ng
- 169. Tools to build ML Learning Pipeline R is an open source programming language for statistical computing and graphics. Faster in getting up to speed with Machine Learning. Great for in depth data analysis and feature engineering.
- 170. Tools to build ML Learning Pipeline Machine Learning Philosophy Machine Learning places a greater emphasis on predictive accuracy. Scikit-learn focuses more on helping you to maximize the accuracy of your models.
- 171. Tools to build ML Learning Pipeline Statistical Learning Philosophy Statistical Learning emphasizes model interpretability and uncertainty. R and GNU Octave tends to offer more capabilities for understanding your models and data gathered.
- 173. Machine Learning Examples: Jupyter Notebooks and Code Files https://github.com/jefftune/gitw-2017-ml
- 177. Two end-to-end Solutions: Titanic Passengers
- 178. Food truck Profit to Population Profitability
- 181. Iris Dataset Decision Tree Random Decision Forest
- 184. "Mathew, where's the lamb chop?" whispered Helene. "Lamb chops, you mean," sang Mathew; "you, me, Wendy, and John can't all swallow one lamb chop." "And Mark, he also desires lamb chops," said Wendy. "Now wait," sang Mathew; "let's struggle to understand where spooky old Mark is." "Mark said that he was rambling over to eat with us," cried Helene; "he's sashaying up some turnpike right now." "Mark, oh, Mark, skip briskly; it would facilitate us to start bolting our lamb chops speedily," chanted John carefully.
- 185. Meanwhile Mark winged in, whispering, "A supper, a breakfast, a repast, quick; it can be tasty or well cooked or delicious; I don't care; I'm hungrily famished. I've sauntered some clean streets; I was thinking about yachts, the sea, and the ocean; I'm exhausted." "Yachts?" each of them said. "Yes, yachts, a hoard of yachts floating on the sea. This yacht pondering let me be unwound during my skip over here." "Better yachts in the sea than a sickening electron in a revolting galaxy," hummed Helene.
- 186. Steps of Training Predictive Modelling
Editor's Notes
- Siraj Raval
- Siraj Raval
- Siraj Raval
- Siraj Raval
- Siraj Raval
- Siraj Raval
- Siraj Raval
- https://thenewstack.io/what-machine-learning-can-and-cant-do/ context that a machine learning algorithm would need to understand in order to predict needs in a given business situation
- context that a machine learning algorithm would need to understand in order to predict needs in a given business situation
- machine learning as a subdiscipline of artificial intelligence. Both disciplines overlap, but they also have separate areas of knowledge. In the case of machine learning, it would include data mining techniques (a term generally applied to information extraction techniques based on raw data), which are not part of artificial intelligence.
- Statistical learning theory deals with the problem of finding a predictive function based on data, which has led to successful applications in fields such as computer vision, speech recognition, bioinformatics and baseball.
- After all, you’re teaching machines that work in ones and zeros to reach their own conclusions about the world. You’re teaching them how to think! However, it’s not nearly as hard as the complex and formula-laden literature would have you believe. Like all of the best frameworks we have for understanding our world, e.g. Newton’s Laws of Motion, Jobs to be Done, Supply & Demand — the best ideas and concepts in machine learning are simple. The majority of literature on machine learning, however, is riddled with complex notation, formulae and superfluous language. It puts walls up around fundamentally simple ideas.
- Siraj Raval
- Training Phase: Converting raw data to data usable by ML algorithm Training an ML algorithm Test the prediction-accuracy of the pipeline
- Real-Time Prediction Phase: Using the output of the ML algorithm to perform actions in the real-world
- It’s easy to believe that machine learning is hard. An arcane craft known only to a select few academics.
- It’s easy to believe that machine learning is hard. An arcane craft known only to a select few academics.
- p = sigmoid(\beta_{0} + \beta_{1}x_{1} + \beta_{2}x_{2})
- \Theta_{0} = \frac{1}{m} \times \sum_{1}^{n} (p - y_{i}) \times x_{i}