The Hitchhiker's Guide to Machine Learning with Python & Apache Spark
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The document outlines a tutorial on using Apache Spark and Python for machine learning applications, covering topics such as data science, programming goals, statistical tools, clustering, and classification. It includes detailed agendas, examples, and practical exercises related to real-world datasets like the Titanic and State of the Union speeches. The tutorial emphasizes hands-on learning and aims to provide insights into scalable data management and analytics.
![Agenda
o Spark Data Science DevOps
• Spark, Python Machine Learning
• Goals/non-goals
• Intro to Spark
• Stack, Mechanisms – RDD
• Datasets : SOTU, Titanic, Frequent
Flier
• Statistical Toolbox
• Summary, Correlations
o “Mood Of the Union”
• State of the Union w/ Washington,
Lincoln, FDR, JFK, Clinton, Bush
Obama
• Map reduce, parse text
o Clustering
• K-means for Gallactic Hoppers!
o Break [3:15-3:45)
o Predicting Survivors with Classification
• Decision Trees
• NaiveBayes (Titanic data set)
o Linear Regression
o Recommendation Engine
• Collab Filtering w/movie lens
o Discussions/Slack
Oct
29
2-‐3:15
(75min),
3:45-‐5:00
(75
min)
=
150
min
[20]
2:00
–
2:20
[30]
2:20
–
2:50
[25]
2:50
–
3:15
[30]
3:45
–
4:15
[10]
4:15
–
4:25
[20]
4:25
–
4:45
[15]
4:45
–
5:00](https://image.slidesharecdn.com/bdtc-py-09-140929235634-phpapp02/85/The-Hitchhiker-s-Guide-to-Machine-Learning-with-Python-Apache-Spark-2-320.jpg)
The Hitchhiker's Guide to Machine Learning with Python & Apache Spark
- 1. “I want to die on Mars but not on impact” — Elon Musk, interview with Chris Anderson "There are no facts, only interpretations." - Friedrich Nietzsche “The shrewd guess, the fertile hypothesis, the courageous leap to a tentative conclusion – these are the most valuable coin of the thinker at work” -- Jerome Seymour Bruner The Hitchhiker's Guide to Machine Learning with Python Apache Spark @ksankar // doubleclix.wordpress.com http://www.bigdatatechcon.com/ classes.html#TheHitchhikersGuidetoMachineLearningwithPythonandApacheSparkPartI October 29, 2014
- 2. Agenda o Spark Data Science DevOps • Spark, Python Machine Learning • Goals/non-goals • Intro to Spark • Stack, Mechanisms – RDD • Datasets : SOTU, Titanic, Frequent Flier • Statistical Toolbox • Summary, Correlations o “Mood Of the Union” • State of the Union w/ Washington, Lincoln, FDR, JFK, Clinton, Bush Obama • Map reduce, parse text o Clustering • K-means for Gallactic Hoppers! o Break [3:15-3:45) o Predicting Survivors with Classification • Decision Trees • NaiveBayes (Titanic data set) o Linear Regression o Recommendation Engine • Collab Filtering w/movie lens o Discussions/Slack Oct 29 2-‐3:15 (75min), 3:45-‐5:00 (75 min) = 150 min [20] 2:00 – 2:20 [30] 2:20 – 2:50 [25] 2:50 – 3:15 [30] 3:45 – 4:15 [10] 4:15 – 4:25 [20] 4:25 – 4:45 [15] 4:45 – 5:00
- 3. Goals non-goals Goals ¤ Understand how to program Machine Learning with Spark Python ¤ Focus on programming ML application ¤ Give you a focused time to work thru examples § Work with me. I will wait if you want to catch-up ¤ Less theory, more usage - let us see if this works ¤ As straightforward as possible § The programs can be optimized Non-goals ¡ Go deep into the algorithms • We don’t have sufficient time. The topic can be easily a 5 day tutorial ! ¡ Dive into spark internals • That is for another day ¡ The underlying computation, communication, constraints distribution is a fascinating subject • Paco does a good job explaining them ¡ A passive talk • Nope. Interactive hands-on
- 4. About Me o Chief Data Scientist at BlackArrow.tv o Have been speaking at OSCON, PyCon, Pydata et al The Nuthead band ! o Reviewing Packt Book “Machine Learning with Spark” o Picked up co-authorship Second Edition of “Fast Data Processing with Spark” o Have done lots of things: • Big Data (Retail, Bioinformatics, Financial, AdTech), • Written Books (Web 2.0, Wireless, Java,…) • Standards, some work in AI, • Guest Lecturer at Naval PG School,… • Planning MS-CFinance or Statistics or Computational Math o Volunteer as Robotics Judge at First Lego league World Competitions o @ksankar, doubleclix.wordpress.com
- 5. Spark Data Science DevOps 2:00
- 6. Close Encounters — 1st ◦ This Tutorial — 2nd ◦ Do More Hands-on Walkthrough — 3nd ◦ Listen To Lectures ◦ More competitions …
- 7. Spark Installation o Install Spark 1.1.0 in local Machine o https://spark.apache.org/downloads.html • Pre-built For Hadoop 2.4 is fine o Download uncompress o Remember the path use it wherever you see /usr/local/spark/ o I have downloaded in /usr/local have a softlink spark to the latest version
- 8. Tutorial Materials o Github : https://github.com/xsankar/cloaked-ironman o Clone or download zip o Open terminal o cd ~/cloaked-ironman o IPYTHON=1 IPYTHON_OPTS=notebook --pylab inline /usr/local/spark/bin/ pyspark o Note : o I have a soft link “spark” in my /usr/local that points to the spark version that I use. For example ln -s spark-1.1.0/ spark o Click on ipython dashboard o Just look thru the ipython notebooks
- 9. Data Science - Context Reason Model Deploy o Scalable Model Deployment o Big Data automation purpose built appliances (soft/ hard) o Manage SLAs response times Collect Store Transform o Volume o Velocity o Streaming Data o Canonical form o Data catalog o Data Fabric across the organization o Access to multiple sources of data o Think Hybrid – Big Data Apps, Appliances Infrastructure o Metadata o Monitor counters Metrics o Structured vs. Multi-‐ structured o Flexible Selectable § Data Subsets § Attribute sets o Refine model with § Extended Data subsets § Engineered Attribute sets o Validation run across a larger data set Data Management Data Science Visualize Recommend Predict Explore o Dynamic Data Sets o 2 way key-‐value tagging of datasets o Extended attribute sets o Advanced Analytics o Performance o Scalability o Refresh Latency o In-‐memory Analytics o Advanced Visualization o Interactive Dashboards o Map Overlay o Infographics ¤ Bytes to Business a.k.a. Build the full stack ¤ Find Relevant Data For Business ¤ Connect the Dots
- 10. Velocity Volume Variety Data Science - Context Context Connect edness Interface Inference Intelligence “Data of unusual size” that can't be brute forced o Three Amigos o Interface = Cognition o Intelligence = Compute(CPU) Computational(GPU) o Infer Significance Causality
- 11. Day in the life of a (super) Model Intelligence Inference Data Representation Interface Algorithms AIributes Parameters Data (Scoring) Model SelecMon Reason Learn Models Visualize, Recommend, Explore Model Assessment Dimensionality ReducMon Feature SelecMon
- 12. Data Science Maturity Model Spark Isolated Analytics Integrated Analytics Aggregated Analytics Automated Analytics Data Small Data Larger Data set Big Data Big Data Factory Model Context Local Domain Cross-‐domain + External Cross domain + External Model, Reason Deploy • Single set of boxes, usually owned by the Model Builders • Departmental • Deploy -‐ Central AnalyMcs Infrastructure • Models sMll owned operated by Modelers • Partly Enterprise-‐wide • Central AnalyMcs Infrastructure • Model Reason – by Model Builders • Deploy, Operate – by ops • Residuals and other metrics monitored by modelers • Enterprise-‐wide • Distributed AnalyMcs Infrastructure • AI Augmented models • Model Reason – by Model Builders • Deploy, Operate – by ops • Data as a moneMzed service, extending to eco system partners • Reports • Dashboards • Dashboards + some APIs • Dashboards + Well defined APIs + programming models Type • DescripMve ReacMve • + PredicMve • + AdapMve • AdapMve Datasets • All in the same box • Fixed data sets, usually in temp data spaces • Flexible Data AIribute sets • Dynamic datasets with well-‐defined refresh policies Workload • Skunk works • Business relevant apps with approx SLAs • High performance appliance clusters • Appliances and clusters for mulMple workloads including real Mme apps • Infrastructure for emerging technologies Strategy • Informal definiMons • Data definiMons buried in the analyMcs models • Some data definiMons • Data catalogue, metadata AnnotaMons • Big Data MDM Strategy
- 13. The Sense Sensibility of a DataScien3st DevOps Factory = OperaMonal Lab = InvesMgaMve hIp://doubleclix.wordpress.com/2014/05/11/the-‐sense-‐ sensibility-‐of-‐a-‐data-‐scienMst-‐devops/
- 14. Spark-The Stack
- 15. hIp://databricks.com/blog/2014/10/10/spark-‐breaks-‐previous-‐large-‐scale-‐sort-‐record.html
- 16. RDD – The workhorse of Spark o Resilient Distributed Datasets • Collection that can be operated in parallel o Transformations – create RDDs • Map, Filter,… o Actions – Get values • Collect, Take,… o We will apply these operations during this tutorial
- 17. Algorithm spectrum o Regression o Logit o CART o Ensemble : Random Forest o Clustering o KNN o Genetic Alg o Simulated Annealing o Collab Filtering o SVM o Kernels o SVD o NNet o Boltzman Machine o Feature Learning Machine Learning Cute Math Ar0ficial Intelligence
- 18. ALL MLlib APIs are not available in Python (as of 1.1.0) API Spark 1.1.0 Spark 1.2.0 Java/Scala Python Basic Statistics ✔ ✔ Linear Models ✔ ✔ Decision Trees ✔ ✔ Random Forest ✖ ✖ Collab Filtering ✔ ✔ Clustering-KMeans ✔ ✔ Clustering-Hierarchical ✖ ✖ SVD ✔ ✖ PCA ✔ ✖ Standard Scaler, Normalizer ✔ ✖ Model Evaluation-PR/ROC Spark 1.2 MLlib JIRA h=p://bit.ly/1ywotkm
- 19. Statistical Toolbox o Sample data : Car mileage data hIps://github.com/apache/spark/blob/master/examples/src/main/python/mllib/correlaMons.py
- 21. “Mood Of the Union” with TF-IDF 2:20
- 22. Scenario – Mood Of the Union o It has been said that the State of the Union speech by the President of USA reflects the social challenge faced by the country ? o If so, can we infer the mood of the country by analyzing SOTU ? o If we embark on this line of thought, how would we do it with Spark python ? o Is it different from Hadoop-MapReduce ? o Is it better ?
- 23. POA (Plan Of Action) o Collect State of the Union speech by George Washington, Abe Lincoln, FDR, JFK, Bill Clinton, GW Bush Barack Obama o Read the 7 SOTU from the 7 presidents into 7 RDDs o Create word vectors o Transform into word frequency vectors o Remove stock common words o Inspect to n words to see if they reflect the sentiment of the time o Compute set difference and see how new words have cropped up o Compute TF-IDF (homework!)
- 24. Lookout for these interesting Spark features o RDD Map-Reduce o How to parse input o Removing common words o Sort rdd by value
- 25. iPython notebook at https://github.com/xsankar/cloaked-ironman Read Create word vector
- 26. Remove Common Words – 1 of 3 iPython notebook at https://github.com/xsankar/cloaked-ironman
- 27. Remove Common Words – 2 of 3
- 28. Remove Common Words – 3 of 3
- 29. FDR vs. Barack Obama as reflected by SOTU
- 30. Barack Obama vs. Bill Clinton
- 31. GWB vs Abe Lincoln as reflected by SOTU
- 32. Epilogue o Interesting Exercise o Highlights • Map-reduce in a couple of lines ! • But it is not exactly the same as Hadoop Mapreduce (see the excellent blog by Sean Owen1) • Set differences using substractByKey • Ability to sort a map by values (or any arbitrary function, for that matter) o To Explore as homework: • TF-IDF in http://spark.apache.org/docs/latest/mllib-feature-extraction.html#tf-idf • Haven’t seen it in python for 1.1. hIp://blog.cloudera.com/blog/2014/09/how-‐to-‐translate-‐from-‐mapreduce-‐to-‐apache-‐spark/
- 33. Clustering 2:50
- 34. Scenario – Clustering with Spark o InterGallactic Airlines have the GallacticHoppers frequent flyer program have data about their customers who participate in the program. o The airlines execs have a feeling that other airlines will poach their customers if they do not keep their loyal customers happy. o So the business want to customize promotions to their frequent flier program. o Can they just have one type of promotion ? o Should they have different types of incentives ? o Who exactly are the customers in their GallacticHoppers program ? o Recently they have deployed an infrastructure with Spark o Can Spark help in this business problem ?
- 35. Clustering - Theory o Clustering is unsupervised learning o While the computers can dissect a dataset into “similar” clusters, it still needs human direction domain knowledge to interpret guide o Two types: • Centroid based clustering – k-means clustering • Tree based Clustering – hierarchical clustering o Spark implements the Scalable Kmeans++ • Paper : http://theory.stanford.edu/~‾sergei/papers/vldb12- kmpar.pdf
- 36. Lookout for these interesting Spark features o Application of Statistics toolbox o Center Scale RDD o Filter RDDs
- 37. Clustering - API o from pyspark.mllib.clustering import KMeans o Kmeans.train o train(cls, data, k, maxIterations=100, runs=1, initializationMode=k-means||) o K = number of clusters to create, default=2 o initializationMode = The initialization algorithm. This can be either random to choose random points as initial cluster centers, or k-means|| to use a parallel variant of k-means++ (Bahmani et al., Scalable K-Means++, VLDB 2012). Default: k-means|| o KMeansModel.predict o Maps a point to a cluster
- 38. Data iPython notebook at https://github.com/xsankar/cloaked-ironman
- 39. Read Data Create RDD
- 40. Train Predict
- 41. Calculate error
- 42. But Data is not even
- 43. So let us center scale the data and try again
- 44. Let us try with 5 clusters Looks Good
- 45. Let us map the cluster to our data
- 46. And interpret them We have mulMple cluster types: • 1 : Very AcMve – Give them the most aIenMon • 3 : Very AcMve on-‐line, few flights – Give them on-‐line coupons • 4 : RelaMvely new customers, not that acMve – Give them flight coupons to encourage them to fly more. Ask them why they are not flying. May be they are flying to desMnaMons (say Jupiter) where InterGallacMc has less gates Note : • This is just a sample interpreta0on. • In real life we would “noodle” over the clusters tweak them to be useful, interpretable and dis0nguishable. • May be 3 is more suited to create targeted promo0ons iPython notebook at https://github.com/xsankar/cloaked-ironman
- 47. Epilogue o KMeans in Spark has enough controls o It does a decent job o We were able to control the clusters based on our experience (2 cluster is too low, 10 is too high, 5 seems to be right) o We can see that the Scalable KMeans has control over runs, parallelism et al. (Home work : explore the scalability) o We were able to interpret the results with domain knowledge and arrive at a scheme to solve the business opportunity o Naturally we would tweak the clusters to fit the business viability. 20 clusters with corresponding promotion schemes are unwieldy, even if the WSSE is the minimum.
- 48. Break 3:15
- 49. Predicting Survivors with Classification 3:45
- 50. Titanic Passenger Metadata • Small • 3 Predictors • Class • Sex • Age • Survived? Classification - Scenario o This is a knowledge exercise o Classify survival from the titanic data o Gives us a quick dataset to run test classification iPython notebook at https://github.com/xsankar/cloaked-ironman
- 51. Classifying Classifiers Statistical Structural Regression Naïve Bayes Bayesian Networks Rule-‐based Distance-‐based Neural Networks Production Rules Decision Trees Multi-‐layer Perception Functional Ensemble Nearest Neighbor Linear Spectral Wavelet kNN Random Forests Learning vector Quantization Logistic Regression1 Boosting SVM 1Max Entropy Classifier Ref: Algorithms of the Intelligent Web, Marmanis Babenko
- 52. Regression Classifiers Continuous Variables Categorical Variables Decision Trees k-‐NN(Nearest Neighbors) Bias Variance Model Complexity Over-fitting Bagging BoosMng CART
- 53. Classification - Spark API o Logistic Regression o SVMWIthSGD o DecisionTrees o Data as LabelledPoint (we will see in a moment) o DecisionTree.trainClassifier(data, numClasses, categoricalFeaturesInfo, impurity=gini, maxDepth=4, maxBins=100) o Impurity – “entropy” or “gini” o maxBins = control to throttle communication at the expense of accuracy • Larger = Higher Accuracy • Smaller = less communication (as # of bins = number of instances) o data adaptive – i.e. decision tree samples on the driver and figures out the bin spacing i.e. the places you slice for binning o intelligent framework - need this for scale
- 54. Lookout for these interesting Spark features o Concept of Labeled Point how to create an RDD of LPs o Print the tree o Calculate Accuracy MSE from RDDs
- 55. Read data extract features iPython notebook at https://github.com/xsankar/cloaked-ironman
- 56. Create the model
- 57. Extract labels features
- 58. Calculate Accuracy MSE
- 60. Decision Tree – Best Practices DecisionTree.trainClassifier(data, numClasses, categoricalFeaturesInfo, impurity=gini, maxDepth=4, maxBins=100) maxDepth Tune with Data/Model SelecMon maxBins Set low, monitor communicaMons, increase if needed # RDD parMMons Set to # of cores • Usually the recommendation is that the RDD partitions should be over partitioned ie “more partitions than cores”, because tasks take different times, we need to utilize the compute power and in the end they average out • But for Machine Learning especially trees, all tasks are approx equal computationally intensive, so over partitioning doesn’t help • Joe Bradley talk (reference below) has interesting insights hIps://speakerdeck.com/jkbradley/mllib-‐decision-‐trees-‐at-‐sf-‐scala-‐baml-‐meetup
- 61. Future … o Actually we should split the data to training test sets o Then use different feature sets to see if we can increase the accuracy o Leave it as Homework o In 1.2 … o Random Forest • Bagging • PR for Random Forest o Boosting o Alpine lab sequoia Forest: coordinating merge o Model Selection Pipeline ; Design Doc
- 62. Boosting — Goal ◦ Model Complexity (-) ◦ Variance (-) ◦ Prediction Accuracy (+) ◦ “Output of weak classifiers into a powerful commiIee” ◦ Final PredicMon = weighted majority vote ◦ Later classifiers get misclassified points – With higher weight, – So they are forced – To concentrate on them ◦ AdaBoost (AdapMveBoosting) ◦ BoosMng vs Bagging – Bagging – independent trees -‐ Spark shines here – BoosMng – successively weighted
- 63. Random Forests+ — Goal ◦ Model Complexity (-) ◦ Variance (-) ◦ Prediction Accuracy (+) ◦ Builds large collecMon of de-‐correlated trees averages them ◦ Improves Bagging by selecMng i.i.d* random variables for splipng ◦ Simpler to train tune ◦ “Do remarkably well, with very li=le tuning required” – ESLII ◦ Less suscepMble to over fipng (than boosMng) ◦ Many RF implementaMons – Original version -‐ Fortran-‐77 ! By Breiman/Cutler – Python, R, Mahout, Weka, Milk (ML toolkit for py), matlab * i.i.d – independent identically distributed + http://www.stat.berkeley.edu/~breiman/RandomForests/cc_home.htm
- 64. ◦ Two Step – Develop a set of learners – Combine the results to develop a composite predictor ◦ Ensemble methods can take the form of: – Using different algorithms, – Using the same algorithm with different sepngs – Assigning different parts of the dataset to different classifiers ◦ Bagging Random Forests are examples of ensemble method Ref: Machine Learning In Action Ensemble Methods — Goal ◦ Model Complexity (-) ◦ Variance (-) ◦ Prediction Accuracy (+)
- 65. Random Forests o While Boosting splits based on best among all variables, RF splits based on best among randomly chosen variables o Simpler because it requires two variables – no. of Predictors (typically √k) no. of trees (500 for large dataset, 150 for smaller) o Error prediction • For each iteration, predict for dataset that is not in the sample (OOB data) • Aggregate OOB predictions • Calculate Prediction Error for the aggregate, which is basically the OOB estimate of error rate • Can use this to search for optimal # of predictors • We will see how close this is to the actual error in the Heritage Health Prize o Assumes equal cost for mis-prediction. Can add a cost function o Proximity matrix applications like adding missing data, dropping outliers Ref: R News Vol 2/3, Dec 2002 Statistical Learning from a Regression Perspective : Berk A Brief Overview of RF by Dan Steinberg
- 67. Linear Regression - API LabeledPoint The features and labels of a data point LinearModel weights, intercept LinearRegressionModel Base predict() LinearRegressionModel LinearRegressionWithS GD train(cls, data, iterations=100, step=1.0, miniBatchFraction=1.0, initialWeights=None, regParam=1.0, regType=None, intercept=False) LassoModel Least-squares fit with an l_1 penalty term. LassoWithSGD train(cls, data, iterations=100, step=1.0, regParam=1.0, miniBatchFraction=1.0,initialWeights=None) RidgeRegressionModel Least-squares fit with an l_2 penalty term. RidgeRegressionWithS GD train(cls, data, iterations=100, step=1.0, regParam=1.0, miniBatchFraction=1.0, initialWeights=None)
- 69. Use LR model for prediction calculate MSE
- 70. Step size is important, the model can diverge !
- 75. Recommendation Personalization - Spark Automated Analytics-‐ Let Data tell story Feature Learning, AI, Deep Learning Learning Models -‐ fit parameters as it gets more data Dynamic Models – model selection based on context o Knowledge Based o Demographic Based o Content Based o Collaborative Filtering o Item Based o User Based o Latent Factor based o User Rating o Purchased o Looked/Not purchased Spark (in 1.1.0) implements the user based ALS collaboraMve filtering Ref: ALS -‐ CollaboraMve Filtering for Implicit Feedback Datasets, Yifan Hu ; ATT Labs., Florham Park, NJ ; Koren, Y. ; Volinsky, C. ALS-‐WR -‐ Large-‐Scale Parallel CollaboraMve Filtering for the Nevlix Prize, Yunhong Zhou, Dennis Wilkinson, Robert Schreiber, Rong Pan
- 76. Spark Collaborative Filtering API o ALS.train(cls, ratings, rank, iterations=5, lambda_=0.01, blocks=-1) o ALS.trainImplicit(cls, ratings, rank, iterations=5, lambda_=0.01, blocks=-1, alpha=0.01) o MatrixFactorizationModel.predict(self, user, product) o MatrixFactorizationModel.predictAll(self, usersProducts)
- 77. Read Parse
- 78. Split Train
- 79. Evaluate
- 80. Epilogue o We explored interesting APIs in Spark o ALS-Collab Filtering o RDD Operations • Join (HashJoin) • In memory, Grace, Recursive hash join hIp://technet.microsox.com/en-‐us/library/ms189313(v=sql.105).aspx
- 81. Questions ? 4:45
- 82. Reference 1. SF Scala SF Bay Area Machine Learning, Joseph Bradley: Decision Trees on Spark http://functional.tv/post/98342564544/sfscala-sfbaml-joseph-bradley-decision-trees-on- spark 2. http://stats.stackexchange.com/questions/21222/are-mean-normalization-and-feature-scaling- needed-for-k-means-clustering 3. http://stats.stackexchange.com/questions/19216/variables-are-often-adjusted-e-g-standardised- before-making-a-model-when-is 4. http://funny-pictures.picphotos.net/tongue-out-smiley-face/smile-day.net*wp-content* uploads*2012*01*Tongue-Out-Smiley-Face1.jpg/ 5. https://speakerdeck.com/jkbradley/mllib-decision-trees-at-sf-scala-baml-meetup 6. http://www.rosebt.com/1/post/2011/10/big-data-analytics-maturity-model.html 7. http://blogs.gartner.com/matthew-davis/
- 83. Essential Reading List o A few useful things to know about machine learning - by Pedro Domingos • http://dl.acm.org/citation.cfm?id=2347755 o The Lack of A Priori Distinctions Between Learning Algorithms by David H. Wolpert • http://mpdc.mae.cornell.edu/Courses/MAE714/Papers/ lack_of_a_priori_distinctions_wolpert.pdf o http://www.no-free-lunch.org/ o YC.o Cn trolling the false discovery rate: a practical and powerful approach to multiple testing Benjamini, Y. and Hochberg, • http://www.stat.purdue.edu/~‾doerge/BIOINFORM.D/FALL06/Benjamini%20and%20Y %20FDR.pdf o A Glimpse of Googl, NASA,Peter Norvig + The Restaurant at the End of the Universe • http://doubleclix.wordpress.com/2014/03/07/a-glimpse-of-google-nasa-peter-norvig/ o Avoid these three mistakes, James Faghmo • https://medium.com/about-data/73258b3848a4 o Leakage in Data Mining: Formulation, Detection, and Avoidance • http://www.cs.umb.edu/~‾ding/history/470_670_fall_2011/papers/ cs670_Tran_PreferredPaper_LeakingInDataMining.pdf
- 84. For your reading viewing pleasure … An ordered List ① An Introduction to Statistical Learning • http://www-bcf.usc.edu/~‾gareth/ISL/ ② ISL Class Stanford/Hastie/Tibsharani at their best - Statistical Learning • http://online.stanford.edu/course/statistical-learning-winter-2014 ③ Prof. Pedro Domingo • https://class.coursera.org/machlearning-001/lecture/preview ④ Prof. Andrew Ng • https://class.coursera.org/ml-003/lecture/preview ⑤ Prof. Abu Mostafa, CaltechX: CS1156x: Learning From Data • https://www.edx.org/course/caltechx/caltechx-cs1156x-learning-data-1120 ⑥ Mathematicalmonk @ YouTube • https://www.youtube.com/playlist?list=PLD0F06AA0D2E8FFBA ⑦ The Elements Of Statistical Learning • http://statweb.stanford.edu/~‾tibs/ElemStatLearn/ http://www.quora.com/Machine-Learning/Whats-the-easiest-way-to-learn-machine- learning/
- 85. References: o An Introduction to scikit-learn, pycon 2013, Jake Vanderplas • http://pyvideo.org/video/1655/an-introduction-to-scikit-learn-machine-learning o Advanced Machine Learning with scikit-learn, pycon 2013, Strata 2014, Olivier Grisel • http://pyvideo.org/video/1719/advanced-machine-learning-with-scikit-learn o Just The Basics, Strata 2013, William Cukierski Ben Hamner • http://strataconf.com/strata2013/public/schedule/detail/27291 o The Problem of Multiple Testing • http://download.journals.elsevierhealth.com/pdfs/journals/1934-1482/ PIIS1934148209014609.pdf
- 86. The Beginning As The End How did we do ? 4:45