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Machine Learning as a Daily Work for a Programmer- Volodymyr Vorobiov

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Ruby Meditation #15

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Machine Learning as a Daily Work for a Programmer- Volodymyr Vorobiov

  1. 1. RUBYGARAGE2017 TECHNOLOGYMATTERS What It Is And How It Works Machine Learning Volodymyr Vorobiov Software Development Consultant at RubyGarage
  2. 2. Machine learning is a subset of artificial intelligence whose goalis to give computers the ability to teach themselves, whereas artificial intelligence is a general concept of smart machines. In other words, artificial intelligence is implemented through machine learning or - to be more precise - through machine learning algorithms. RUBYGARAGE2017 TECHNOLOGYMATTERS artificial intelligence TEACH YOUR COMPUTER
  3. 3. EXAMPLES OF HOW MACHINE LEARNING IS USED IN THE REAL WORLD RUBYGARAGE2017 TECHNOLOGYMATTERS - Facial recognition - Voice recognition - Text recognition - Diagnostics into medicine, - Self-driving cars - Robots behavior adjustment - Ads targeting, - Predictions in financial trading - Virtual and augmented reality - Astronomy and space ???
  4. 4. The 21st century is the age of data. It’s literally everywhere. In fact, there has been an exponential growth in the volume of data over the past decade; the total amount of data doubles every two years. Most of it, however, isn’t used. Huge volumes of data can be tagged, structured, and analyzed, revealing a lot of valuable information. Only machine learning algorithms can easily cope with this task. RUBYGARAGE2017 TECHNOLOGYMATTERS WHY THE FUTURE BELONGS TO MACHINE LEARNING
  5. 5. RUBYGARAGE2017 TECHNOLOGYMATTERS HOW MACHINE LEARNING WORKS Preprocessing Learinng Evaluation Prediction Labels Raw Data Labels Labels Final Model New DataTraining Dataset Test Dataset Learning Algorithm (putting data into the necessary shape) (creating a model with the help of training data) (model assessment using test data) application of the model)
  6. 6. TOOLS RUBYGARAGE2017 TECHNOLOGYMATTERS - Python - Pandas - Powerful data analysis library for Python Pandas is a powerful data analysis Python library that provides flexible and fast data structures for processing “relational” or “labeled” data. This is a fundamental data analysis toolkit in Python. - Scikit-learn - Machine Learning in Python These are simple and effective open-source tools for data mining and analysis. - Statsmodels This is a Python module providing functions and classes to estimate different statistical models as well as to conduct tests and explore statistical data. The Statsmodels module offers a comprehensive list of result statistics. - Matplotlib Matplotlib is a Python 2D plotting library that releases publication quality figures in multiple formats and interactive environments in different platforms.
  7. 7. The quality of the data and the amount of useful information that it contains are key factors that determine how well a machine learning algorithm can learn. Therefore, it is absolutely critical that we make sure to examine and preprocess a dataset before we feed it to a learning algorithm. - Removing and imputing missing values from the dataset - Getting categorical data into shape for machine learning algorithms - Selecting relevant features for the model construction RUBYGARAGE2017 TECHNOLOGYMATTERS DATA PREPROCESSING
  8. 8. DATA PREPROCESSING DATASET PRESENTATION RUBYGARAGE2017 TECHNOLOGYMATTERS Independent variables Dependent variables
  9. 9. IMPORTING THE DATASET
  10. 10. IMPORTING THE DATASET
  11. 11. RUBYGARAGE2017 TECHNOLOGYMATTERS DEALING WITH MISSING DATA Most computational tools are unable to handle such missing values or would produce unpredictable results if we simply ignored them. Therefore, it is crucial that we take care of those missing values before we proceed with further analyses.
  12. 12. - Eliminating samples or features with missing values The easiest solution to this problem is simply to remove samples with missing values from a dataset. However, this seemingly handy approach has a number of drawbacks. For example, removing too many of such samples is likely to compromise the quality of the analysis. - Imputing missing values The solution is to use various interpolation techniques that help to “guess” the missing values from other samples in a dataset. RUBYGARAGE2017 TECHNOLOGYMATTERS DEALING WITH MISSING DATA
  13. 13. IMPUTING MISSING VALUES
  14. 14. IMPUTING MISSING VALUES RESULTS
  15. 15. RUBYGARAGE2017 TECHNOLOGYMATTERS HANDLING CATEGORICAL DATA
  16. 16. ENCODE LABELS
  17. 17. ENCODE LABELS RESULTS
  18. 18. RUBYGARAGE2017 TECHNOLOGYMATTERS DUMMY VARIABLES
  19. 19. DUMMY VARIABLES
  20. 20. DUMMY VARIABLES RESULTS
  21. 21. RUBYGARAGE2017 TECHNOLOGYMATTERS DUMMY VARIABLE TRAP
  22. 22. PARTITIONING A DATASET INTO TRAINING AND TEST SETS
  23. 23. TRAINING AND TEST SETS RESULTS
  24. 24. BRINGING FEATURES ONTO THE SAME SCALE
  25. 25. SAME SCALE RESULTS
  26. 26. TRAINING AND SELECTING A PREDICTIVE MODEL RUBYGARAGE2017 TECHNOLOGYMATTERS - Supervised learning - Regression - Classification - Unsupervised learning - Clustering - Dimensionality Reduction - Reinforcement Learning - Association Rule Learning - Natural Language Processing - Deep Learning - Model Selection
  27. 27. SUPERVISED LEARNING RUBYGARAGE2017 TECHNOLOGYMATTERS For making predictions about the future Regression For predicting continuous outcomes Classification For predicting class labels
  28. 28. REGRESSION RUBYGARAGE2017 TECHNOLOGYMATTERS Regression models (both linear and non-linear) are used for predicting a real value, like salary for example. If your independent variable is time, then you are forecasting future values, otherwise your model is predicting present but unknown values.
  29. 29. SIMPLE LINEAR REGRESSION RUBYGARAGE2017 TECHNOLOGYMATTERS y x Constant Coefficent Dependent variable (DV) Independent variable (IV) y = b + b*x1 10
  30. 30. DATASET PRESENTATION. EXPERIENCE AND SALARY. RUBYGARAGE2017 TECHNOLOGYMATTERS
  31. 31. SIMPLE LINEAR REGRESSION TRAINING
  32. 32. SIMPLE LINEAR REGRESSION TRAINING RUBYGARAGE2017 TECHNOLOGYMATTERS
  33. 33. MULTIPLE LINEAR REGRESSION RUBYGARAGE2017 TECHNOLOGYMATTERS Constant Coefficent Dependent variable (DV) Independent variable (IVs) y = b + b*x + b*x ... + b*x1 1 2 2 n n0
  34. 34. DATASET PRESENTATION. INVESTMENT FUND STATISTIC. RUBYGARAGE2017 TECHNOLOGYMATTERS
  35. 35. MULTIPLE LINEAR REGRESSION TRAINING
  36. 36. EVALUATING REGRESSION MODELS PERFORMANCE RUBYGARAGE2017 TECHNOLOGYMATTERS 1. All-in 2. Backward Elimination 3. Forward Selection 4. Bidirectional Elimination 5. Score Comparison Stepwise Regression
  37. 37. BACKWARD ELIMINATION RUBYGARAGE2017 TECHNOLOGYMATTERS STEP 1: Select a significance level to stay in the model (e.g. SL = 0.05) STEP 2: Fit the full model with all possible predictors STEP 3: Consider the predictor with the highest P-value. If P > SL, go to STEP 4, otherwise go to FIN STEP 4: Remove the predictor STEP 5: Fit model without this variable*
  38. 38. BACKWARD ELIMINATION TRAINING
  39. 39. BACKWARD ELIMINATION TRAINING STEP 1
  40. 40. BACKWARD ELIMINATION TRAINING STEP 4
  41. 41. EVALUATING PERFORMANCE R-SQUARED RUBYGARAGE2017 TECHNOLOGYMATTERS SUM (y - y^) -> min 2 ii Experience Simple Linear Regression: Salary ($) y^i yi
  42. 42. EVALUATING PERFORMANCE R-SQUARED RUBYGARAGE2017 TECHNOLOGYMATTERS SS = SUM (y - y^) 2 i ires SS = SUM (y - y ) 2 i avgtot yavg Experience Simple Linear Regression: Salary ($)
  43. 43. EVALUATING PERFORMANCE ADJUSTED R-SQUARED RUBYGARAGE2017 TECHNOLOGYMATTERS p - number of regressors n - sample size
  44. 44. ADJUSTED R-SQUARED STEP 3
  45. 45. ADJUSTED R-SQUARED STEP 4
  46. 46. ADJUSTED R-SQUARED STEP 5
  47. 47. POLYNOMIAL REGRESSION RUBYGARAGE2017 TECHNOLOGYMATTERS y x y = b + b x + b x1 1 2 1 2 0
  48. 48. POLYNOMIAL REGRESSION. DATASET PRESENTATION. BLUFFING DETECTOR RUBYGARAGE2017 TECHNOLOGYMATTERS
  49. 49. POLYNOMIAL REGRESSION. FITTING THE DATASET
  50. 50. POLYNOMIAL REGRESSION. TRAINING THE MODEL
  51. 51. POLYNOMIAL REGRESSION RESULTS RUBYGARAGE2017 TECHNOLOGYMATTERS
  52. 52. SUPPORT VECTOR REGRESSION BASED ON SUPPORT VECTOR MACHINE
  53. 53. SUPPORT VECTOR REGRESSION. RESULTS. RUBYGARAGE2017 TECHNOLOGYMATTERS
  54. 54. WHAT IF RUBYGARAGE2017 TECHNOLOGYMATTERS X1 X2
  55. 55. DECISION TREE REGRESSION RUBYGARAGE2017 TECHNOLOGYMATTERS Split 4 Split 2 Split 1 Split 3 200 20 40 170 X1 X2 1023 0.7-64.1300.5 65.7 Y
  56. 56. DECISION TREE REGRESSION RUBYGARAGE2017 TECHNOLOGYMATTERS X < 201 X < 2002 300.5 65.7 1023 -64.1 0.7 X < 1702 X < 401 yes no yes no yes no yes no
  57. 57. DECISION TREE REGRESSION TRAINING
  58. 58. DECISION TREE REGRESSION RESULT RUBYGARAGE2017 TECHNOLOGYMATTERS
  59. 59. ENSEMBLE LEARNING. RANDOM FOREST REGRESSION. RUBYGARAGE2017 TECHNOLOGYMATTERS STEP 1: Pick at random K data points from the Training set. STEP 2: Build the Decision Tree associated to these K data points. STEP 3: Choose the number Ntree of trees you want to build and repeat STEPS 1 & 2 STEP 4: For a new data point, make each one of your Ntree trees predict the value of Y to for the data point in question, and assign the new data point the average across all of the predicted Y values.
  60. 60. RANDOM FOREST REGRESSION TRAINING
  61. 61. RANDOM FOREST REGRESSION RESULT RUBYGARAGE2017 TECHNOLOGYMATTERS
  62. 62. REGRESSION MODELS. PROS AND CONS. RUBYGARAGE2017 TECHNOLOGYMATTERS
  63. 63. CLASSIFICATION RUBYGARAGE2017 TECHNOLOGYMATTERS Unlike regression where you predict a continuous number, you use classification to predict a category. There is a wide variety of classification applications from medicine to marketing.
  64. 64. LOGISTIC REGRESSION RUBYGARAGE2017 TECHNOLOGYMATTERS This is new: Action (Y/N) Age We know this: Salary ($) Experience y = b0 + b1*x
  65. 65. LOGISTIC REGRESSION RUBYGARAGE2017 TECHNOLOGYMATTERS Action (Y/N) Action (Y/N) Age Age
  66. 66. LOGISTIC REGRESSION RUBYGARAGE2017 TECHNOLOGYMATTERS
  67. 67. LOGISTIC REGRESSION RUBYGARAGE2017 TECHNOLOGYMATTERS
  68. 68. LOGISTIC REGRESSION RUBYGARAGE2017 TECHNOLOGYMATTERS
  69. 69. LOGISTIC REGRESSION PREDICTION RUBYGARAGE2017 TECHNOLOGYMATTERS
  70. 70. DATASET PRESENTATION. SOCIAL NETWORK ADS. RUBYGARAGE2017 TECHNOLOGYMATTERS
  71. 71. LOGISTIC REGRESSION. PREPROCESSING
  72. 72. LOGISTIC REGRESSION. TRAINING
  73. 73. LOGISTIC REGRESSION. TRAINING SET RESULTS RUBYGARAGE2017 TECHNOLOGYMATTERS
  74. 74. LOGISTIC REGRESSION. TEST SET RESULTS. RUBYGARAGE2017 TECHNOLOGYMATTERS
  75. 75. K-NEAREST NEIGHBORS RUBYGARAGE2017 TECHNOLOGYMATTERS
  76. 76. K-NEAREST NEIGHBORS RUBYGARAGE2017 TECHNOLOGYMATTERS STEP 1: Choose the number K of neighbors STEP 2: Take the K nearest neighbors of the new data point, according to the Euclidean distance STEP 3: Among these K neighbors, count the number of data points in each category STEP 4: Assign the new data point to the category where you counted the most neighbors Your Model is Ready
  77. 77. K-NEAREST NEIGHBORS RUBYGARAGE2017 TECHNOLOGYMATTERS
  78. 78. K-NEAREST NEIGHBORS RUBYGARAGE2017 TECHNOLOGYMATTERS Category 1: 3 neighbors Category 2: 2 neighbors
  79. 79. K-NEAREST NEIGHBORS. TRAINING
  80. 80. K-NEAREST NEIGHBORS. TRAINING SET RESULTS RUBYGARAGE2017 TECHNOLOGYMATTERS
  81. 81. K-NEAREST NEIGHBORS. TEST SET RESULTS RUBYGARAGE2017 TECHNOLOGYMATTERS
  82. 82. SUPPORT VECTOR MACHINES RUBYGARAGE2017 TECHNOLOGYMATTERS
  83. 83. SUPPORT VECTOR MACHINES TRAINING
  84. 84. RUBYGARAGE2017 TECHNOLOGYMATTERS SUPPORT VECTOR MACHINES. TRAINING SET RESULTS.
  85. 85. RUBYGARAGE2017 TECHNOLOGYMATTERS SUPPORT VECTOR MACHINES. TEST SET RESULTS.
  86. 86. RUBYGARAGE2017 TECHNOLOGYMATTERS KERNEL SVM
  87. 87. RUBYGARAGE2017 TECHNOLOGYMATTERS KERNEL SVM
  88. 88. RUBYGARAGE2017 TECHNOLOGYMATTERS KERNEL SVM
  89. 89. RUBYGARAGE2017 TECHNOLOGYMATTERS KERNEL SVM
  90. 90. RUBYGARAGE2017 TECHNOLOGYMATTERS KERNEL SVM
  91. 91. RUBYGARAGE2017 TECHNOLOGYMATTERS KERNEL SVM
  92. 92. RUBYGARAGE2017 TECHNOLOGYMATTERS KERNEL SVM
  93. 93. RUBYGARAGE2017 TECHNOLOGYMATTERS KERNEL SVM
  94. 94. KERNEL SVM TRAINING
  95. 95. NAIVE BAYES. TRAINING SET RESULTS. RUBYGARAGE2017 TECHNOLOGYMATTERS
  96. 96. NAIVE BAYES. TEST SET RESULTS. RUBYGARAGE2017 TECHNOLOGYMATTERS
  97. 97. NAIVE BAYES RUBYGARAGE2017 TECHNOLOGYMATTERS Bayes Theorem
  98. 98. DRIVER OR WAALKER. RUBYGARAGE2017 TECHNOLOGYMATTERS
  99. 99. NAIVE BAYES. BAYES THEOREM. WALKS. RUBYGARAGE2017 TECHNOLOGYMATTERS
  100. 100. NAIVE BAYES. BAYES THEOREM. DRIVES. RUBYGARAGE2017 TECHNOLOGYMATTERS
  101. 101. NAIVE BAYES. P(WALKS). RUBYGARAGE2017 TECHNOLOGYMATTERS
  102. 102. NAIVE BAYES. P(X). RUBYGARAGE2017 TECHNOLOGYMATTERS
  103. 103. NAIVE BAYES. P(X|WALKS). RUBYGARAGE2017 TECHNOLOGYMATTERS
  104. 104. NAIVE BAYES. P(WALKS|X). RUBYGARAGE2017 TECHNOLOGYMATTERS
  105. 105. NAIVE BAYES. P(DRIVES|X). RUBYGARAGE2017 TECHNOLOGYMATTERS
  106. 106. NAIVE BAYES RUBYGARAGE2017 TECHNOLOGYMATTERS
  107. 107. NAIVE BAYES. NEW WALKER. RUBYGARAGE2017 TECHNOLOGYMATTERS
  108. 108. NAIVE BAYES. TRAINING.
  109. 109. NAIVE BAYES. TRAINING SET RESULTS. RUBYGARAGE2017 TECHNOLOGYMATTERS
  110. 110. NAIVE BAYES. TEST SET RESULTS. RUBYGARAGE2017 TECHNOLOGYMATTERS
  111. 111. DECISION TREE CLASSIFICATION RUBYGARAGE2017 TECHNOLOGYMATTERS
  112. 112. DECISION TREE CLASSIFICATION. TRAINING.
  113. 113. DECISION TREE CLASSIFICATION. TRAINING SET RESULTS. RUBYGARAGE2017 TECHNOLOGYMATTERS
  114. 114. DECISION TREE CLASSIFICATION. TEST SET RESULTS. RUBYGARAGE2017 TECHNOLOGYMATTERS
  115. 115. RUBYGARAGE2017 TECHNOLOGYMATTERS RANDOM FOREST CLASSIFICATION STEP 1: Pick at random K data points from the Training set. STEP 2: Build the Decision Tree associated to these K data points. STEP 3: Choose the number Ntree of trees you want to build and repeat STEPS 1 & 2 STEP 4: For a new data point, make each one of your Ntree trees predict the category to which the data point belongs, and assign the new data point to the category that wins the majority vote.
  116. 116. RANDOM FOREST CLASSIFICATION. TRAINING
  117. 117. RUBYGARAGE2017 TECHNOLOGYMATTERS RANDOM FOREST CLASSIFICATION. TRAINING SET RESULTS
  118. 118. RUBYGARAGE2017 TECHNOLOGYMATTERS RANDOM FOREST CLASSIFICATION. TEST SET RESULTS.
  119. 119. EVALUATING CLASSIFICATION MODELS PERFORMANCE. FALSE POSITIVES & FALSE NEGATIVES. RUBYGARAGE2017 TECHNOLOGYMATTERS
  120. 120. RUBYGARAGE2017 TECHNOLOGYMATTERS EVALUATING CLASSIFICATION MODELS PERFORMANCE. CONFUSION MATRIX.
  121. 121. CLASSIFICATION MODELS. PROS AND CONS. RUBYGARAGE2017 TECHNOLOGYMATTERS
  122. 122. CLUSTERING RUBYGARAGE2017 TECHNOLOGYMATTERS Clustering is similar to classification, but the basis is different. In Clustering you don’t know what you are looking for, and you are trying to identify some segments or clusters in your data. When you use clustering algorithms on your dataset, unexpected things can suddenly pop up like structures, clusters and groupings you would have never thought of otherwise.
  123. 123. K-MEANS CLUSTERING RUBYGARAGE2017 TECHNOLOGYMATTERS
  124. 124. K-MEANS CLUSTERING RUBYGARAGE2017 TECHNOLOGYMATTERS STEP 1: Choose the number K of clusters STEP 2: Select at random K points, the centroids (not necessarily from your dataset) STEP 3: Assign each data point to the closest centroid -> That forms K clusters STEP 4: Compute and place the new centroid of each cluster STEP 5: Reassign each data point to the new closest centroid. If any reassignment took place, go to STEP 4, otherwise go to FIN. Your Model is Ready
  125. 125. RUBYGARAGE2017 TECHNOLOGYMATTERS K-MEANS CLUSTERING
  126. 126. RUBYGARAGE2017 TECHNOLOGYMATTERS K-MEANS CLUSTERING
  127. 127. K-MEANS CLUSTERING RUBYGARAGE2017 TECHNOLOGYMATTERS
  128. 128. K-MEANS CLUSTERING RUBYGARAGE2017 TECHNOLOGYMATTERS
  129. 129. K-MEANS CLUSTERING RUBYGARAGE2017 TECHNOLOGYMATTERS
  130. 130. K-MEANS CLUSTERING RUBYGARAGE2017 TECHNOLOGYMATTERS
  131. 131. K-MEANS CLUSTERING RUBYGARAGE2017 TECHNOLOGYMATTERS
  132. 132. K-MEANS CLUSTERING RUBYGARAGE2017 TECHNOLOGYMATTERS
  133. 133. K-MEANS CLUSTERING RANDOM INITIALIZATION PROBLEM RUBYGARAGE2017 TECHNOLOGYMATTERS
  134. 134. K-MEANS CLUSTERING RANDOM INITIALIZATION PROBLEM RUBYGARAGE2017 TECHNOLOGYMATTERS
  135. 135. RUBYGARAGE2017 TECHNOLOGYMATTERS K-MEANS CLUSTERING RANDOM INITIALIZATION PROBLEM
  136. 136. RUBYGARAGE2017 TECHNOLOGYMATTERS K-MEANS CLUSTERING RANDOM INITIALIZATION PROBLEM
  137. 137. K-MEANS SELECTING THE NUMBER OF CLUSTERS RUBYGARAGE2017 TECHNOLOGYMATTERS
  138. 138. K-MEANS SELECTING THE NUMBER OF CLUSTERS RUBYGARAGE2017 TECHNOLOGYMATTERS
  139. 139. DATASET PRESENTATION. MALL CUSTOMERS. RUBYGARAGE2017 TECHNOLOGYMATTERS
  140. 140. K-MEANS. TRAINING. OPTIMAL NUMBER OF CLUSTERS.
  141. 141. K-MEANS. OPTIMAL NUMBER OF CLUSTERS RESULTS. RUBYGARAGE2017 TECHNOLOGYMATTERS
  142. 142. K-MEANS TRAINING
  143. 143. K-MEANS. RESULT RUBYGARAGE2017 TECHNOLOGYMATTERS
  144. 144. HIERARCHICAL CLUSTERING RUBYGARAGE2017 TECHNOLOGYMATTERS
  145. 145. RUBYGARAGE2017 TECHNOLOGYMATTERS HIERARCHICAL CLUSTERING AGGLOMERATIVE STEP 1: Make each data point a single-point cluster That forms N clusters STEP 2: Take the two closest data points and make them one cluster That forms N-1 clusters STEP 3: Take the two closest clusters and make them one cluster That forms N-2 clusters STEP 4: Repeat STEP 3 until there is only one cluster FIN
  146. 146. HIERARCHICAL CLUSTERING AGGLOMERATIVE RUBYGARAGE2017 TECHNOLOGYMATTERS
  147. 147. HIERARCHICAL CLUSTERING AGGLOMERATIVE RUBYGARAGE2017 TECHNOLOGYMATTERS
  148. 148. HIERARCHICAL CLUSTERING AGGLOMERATIVE RUBYGARAGE2017 TECHNOLOGYMATTERS
  149. 149. HIERARCHICAL CLUSTERING AGGLOMERATIVE RUBYGARAGE2017 TECHNOLOGYMATTERS
  150. 150. HIERARCHICAL CLUSTERING AGGLOMERATIVE RUBYGARAGE2017 TECHNOLOGYMATTERS
  151. 151. HIERARCHICAL CLUSTERING AGGLOMERATIVE RUBYGARAGE2017 TECHNOLOGYMATTERS
  152. 152. HIERARCHICAL CLUSTERING AGGLOMERATIVE RUBYGARAGE2017 TECHNOLOGYMATTERS
  153. 153. HIERARCHICAL CLUSTERING AGGLOMERATIVE RUBYGARAGE2017 TECHNOLOGYMATTERS
  154. 154. HIERARCHICAL CLUSTERING DENDROGRAMS RUBYGARAGE2017 TECHNOLOGYMATTERS
  155. 155. HIERARCHICAL CLUSTERING DENDROGRAMS RUBYGARAGE2017 TECHNOLOGYMATTERS
  156. 156. HIERARCHICAL CLUSTERING DENDROGRAMS RUBYGARAGE2017 TECHNOLOGYMATTERS
  157. 157. HIERARCHICAL CLUSTERING DENDROGRAMS RUBYGARAGE2017 TECHNOLOGYMATTERS
  158. 158. HIERARCHICAL CLUSTERING DENDROGRAMS RUBYGARAGE2017 TECHNOLOGYMATTERS 4 clusters
  159. 159. DENDROGRAMS OPTIMAL NUMBER OF CLUSTERS RUBYGARAGE2017 TECHNOLOGYMATTERS
  160. 160. DENDROGRAMS OPTIMAL NUMBER OF CLUSTERS RUBYGARAGE2017 TECHNOLOGYMATTERS
  161. 161. DENDROGRAM. FINDING THE OPTIMAL NUMBER OF CLUSTERS.
  162. 162. DENDROGRAM. RESULTS RUBYGARAGE2017 TECHNOLOGYMATTERS
  163. 163. HIERARCHICAL CLUSTERING. TRAINING.
  164. 164. HIERARCHICAL CLUSTERING RESULT RUBYGARAGE2017 TECHNOLOGYMATTERS
  165. 165. CLUSTERING MODELS. PROS AND CONS RUBYGARAGE2017 TECHNOLOGYMATTERS
  166. 166. REINFORCEMENT LEARNING Reinforcement Learning is a branch of Machine Learning, also called Online Learning. It is used to solve interacting problems where the data observed up to time t is considered to decide which action to take at time t + 1. It is also used for Artificial Intelligence when training machines to perform tasks such as walking. Desired outcomes provide the AI with reward, undesired with punishment. Machines learn through trial and error. RUBYGARAGE2017 TECHNOLOGYMATTERS
  167. 167. THE MULTI-ARMED BANDIT PROBLEM Hot to bet to maximize your return RUBYGARAGE2017 TECHNOLOGYMATTERS
  168. 168. THE MULTI-ARMED BANDIT PROBLEM RUBYGARAGE2017 TECHNOLOGYMATTERS
  169. 169. THE MULTI-ARMED BANDIT PROBLEM RUBYGARAGE2017 TECHNOLOGYMATTERS
  170. 170. UPPER CONFIDENCE BOUND ALGORITHM RUBYGARAGE2017 TECHNOLOGYMATTERS
  171. 171. UPPER CONFIDENCE BOUND ALGORITHM RUBYGARAGE2017 TECHNOLOGYMATTERS
  172. 172. UPPER CONFIDENCE BOUND ALGORITHM RUBYGARAGE2017 TECHNOLOGYMATTERS
  173. 173. UPPER CONFIDENCE BOUND ALGORITHM RUBYGARAGE2017 TECHNOLOGYMATTERS
  174. 174. UPPER CONFIDENCE BOUND ALGORITHM RUBYGARAGE2017 TECHNOLOGYMATTERS
  175. 175. UPPER CONFIDENCE BOUND ALGORITHM RUBYGARAGE2017 TECHNOLOGYMATTERS
  176. 176. UPPER CONFIDENCE BOUND ALGORITHM RUBYGARAGE2017 TECHNOLOGYMATTERS
  177. 177. RANDOM SELECTION
  178. 178. RANDOM SELECTION. RESULTS. RUBYGARAGE2017 TECHNOLOGYMATTERS
  179. 179. UPPER CONFIDENCE BOUND. TRAINING.
  180. 180. UPPER CONFIDENCE BOUND. RESULTS. RUBYGARAGE2017 TECHNOLOGYMATTERS
  181. 181. THOMPSON SAMPLING ALGORITHM RUBYGARAGE2017 TECHNOLOGYMATTERS
  182. 182. BAYESIAN INFERENCE RUBYGARAGE2017 TECHNOLOGYMATTERS
  183. 183. BAYESIAN INFERENCE. EXPLANATION. RUBYGARAGE2017 TECHNOLOGYMATTERS
  184. 184. CREATING DISTRIBUTION BASED ON AN INITIAL DATA RUBYGARAGE2017 TECHNOLOGYMATTERS
  185. 185. PULLING RANDOM VALUES FROM DISTRIBUTIONS RUBYGARAGE2017 TECHNOLOGYMATTERS
  186. 186. ADJUSTING THE PERCEPTION OF THE WORLD RUBYGARAGE2017 TECHNOLOGYMATTERS
  187. 187. THE FINAL MODEL RUBYGARAGE2017 61 TECHNOLOGYMATTERS
  188. 188. UCB VS THOMPSON SAMPLING RUBYGARAGE2017 TECHNOLOGYMATTERS
  189. 189. THOMPSON SAMPLING ALGORITHM. TRAINING.
  190. 190. THOMPSON SAMPLING ALGORITHM. RESULTS. RUBYGARAGE2017 TECHNOLOGYMATTERS
  191. 191. NATURAL LANGUAGE PROCESSING RUBYGARAGE2017 TECHNOLOGYMATTERS Natural Language Processing (or NLP) is applying Machine Learning models to text and language. Teaching machines to understand what is said in spoken and written word is the focus of Natural.
  192. 192. NATURAL LANGUAGE PROCESSING RUBYGARAGE2017 TECHNOLOGYMATTERS Natural Language Processing (or NLP) is applying Machine Learning models to text and language. Teaching machines to understand what is said in spoken and written word is the focus of Natural.
  193. 193. NATURAL LANGUAGE PROCESSING RUBYGARAGE2017 TECHNOLOGYMATTERS Language Processing. Whenever you dictate something into your iPhone / Android device that is then converted to text, that’s an NLP algorithm in action.
  194. 194. NATURAL LANGUAGE PROCESSING RUBYGARAGE2017 TECHNOLOGYMATTERS You can use NLP on an article to predict some categories of the articles you are trying to segment. You can use NLP on a book to predict the genre of the book.
  195. 195. NATURAL LANGUAGE PROCESSING RUBYGARAGE2017 TECHNOLOGYMATTERS A very well-known model in NLP is the Bag of Words model. It is a model used to preprocess the texts to classify before fitting the classification algorithms on the observations containing the texts.
  196. 196. DATASET PRESENTATION. RESTAURANT REVIEWS. RUBYGARAGE2017 TECHNOLOGYMATTERS
  197. 197. NLP. TRAINING. IMPORTING THE DATASET AND CLEANING THE TEXTS.
  198. 198. NLP. TRAINING. CLEANING THE TEXTS. RESULTS.
  199. 199. NLP. TRAINING. CREATING THE BAG OF WORDS MODEL.
  200. 200. NLP. CREATING THE BAG OF WORDS MODEL.
  201. 201. NLP. TRAINING. SPLITTING THE DATASET INTO THE TRAINING SET AND TEST SET.
  202. 202. NLP. TRAINING. FITTING NAIVE BAYES TO THE TRAINING SET.
  203. 203. NLP. TRAINING. PREDICTING AND MAKING THE CONFUSION MATRIX.
  204. 204. NLP. CONFUSION MATRIX. RESULTS.
  205. 205. THE NEURON RUBYGARAGE2017 TECHNOLOGYMATTERS
  206. 206. HOW DO NEURAL NETWORKS LEARN? RUBYGARAGE2017 TECHNOLOGYMATTERS
  207. 207. NEURAL NETWORKS RUBYGARAGE2017 TECHNOLOGYMATTERS
  208. 208. RUBYGARAGE2017 TECHNOLOGYMATTERS TO BE CONTINUED

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