├── Adhoc Analytics ├── Bias and Variance.md ├── Clickstream.md ├── Customer Analytics.md ├── Finance.md ├── Flower Image Classification Keras, Tensorflow and Flask.md ├── Lift Analysis.md ├── Predictive Maintenance.md ├── Price Optimization.md ├── Price Prediction.md └── Viterbi Algorithm.md ├── Association Rule Mining └── Readme.md ├── Boosting └── Algorithms.md ├── Conditional Random Fields ├── ReadMe.md └── Terms.md ├── Ensemble Learning ├── Definitions.md ├── Meta Learning or Algorithms.md └── Random Forest.md ├── Hyperparameter Tuning └── README.md ├── Linear Regression ├── Blogs.md ├── Questions.md └── Regularization.md ├── Operations Research ├── Multi Arm Bandit.md └── README.md ├── POC Case └── README.md ├── Principal Component Analysis └── Readme.md ├── Probabilistic Graphical Models └── Readme.md ├── Probability Distribution ├── Distribution Types.md └── README.md ├── README.md ├── Reddit └── README.md ├── Support Vector Machine ├── Readme.md └── Thoery.md ├── Time Series Forecasting ├── README.md └── Video.md └── Unsupervised Learning ├── Latent Dirchlet Process.md └── Topic Modeling.md /Adhoc Analytics/Bias and Variance.md: -------------------------------------------------------------------------------- 1 | https://cscherrer.github.io/post/bias-variance/ 2 | https://towardsdatascience.com/end-your-bias-about-bias-and-variance-67b16f0eb1e6 3 | https://www.learnopencv.com/bias-variance-tradeoff-in-machine-learning/ 4 | http://www.cs.upc.edu/~belanche/Docencia/dm2/MATERIAL/1.%20ParamEst%20-%20Bias%20&%20Var/DMII-Class1.pdf 5 | https://towardsdatascience.com/understanding-the-bias-variance-tradeoff-165e6942b229 6 | https://medium.com/@rgotesman1/learning-machine-learning-part-6-bias-variance-and-error-metrics-51321efc71e8 7 | -------------------------------------------------------------------------------- /Adhoc Analytics/Clickstream.md: -------------------------------------------------------------------------------- 1 | https://github.com/ceteri/clksim 2 | https://github.com/IBM-DSE/ClickStreamAnalysis/tree/master/data 3 | https://github.com/whugue/clickstream-analysis/blob/master/clickstream-analysis-writeup.pdf 4 | https://github.com/balasubram/ClickStream/blob/master/ClickStream.ipynb 5 | -------------------------------------------------------------------------------- /Adhoc Analytics/Customer Analytics.md: -------------------------------------------------------------------------------- 1 | https://www.datameer.com/blog/unlocking-customers-mind-big-data-retail-analytics/ 2 | https://www.datameer.com/blog/six-ways-create-better-customer-behavior-analytics/ 3 | https://towardsdatascience.com/whats-a-customer-worth-8daf183f8a4f 4 | https://becominghuman.ai/predicting-buying-behavior-using-machine-learning-a-case-study-on-sales-prospecting-part-i-3bf455486e5d 5 | -------------------------------------------------------------------------------- /Adhoc Analytics/Finance.md: -------------------------------------------------------------------------------- 1 | http://theautomatic.net/2018/01/25/coding-yahoo_fin-package/ 2 | http://theautomatic.net/2018/07/31/how-to-get-live-stock-prices-with-python/ 3 | -------------------------------------------------------------------------------- /Adhoc Analytics/Flower Image Classification Keras, Tensorflow and Flask.md: -------------------------------------------------------------------------------- 1 | https://medium.com/@ferrygunawan/create-flower-image-classification-with-tensorflow-keras-and-flask-4fd6efd4c5ac 2 | -------------------------------------------------------------------------------- /Adhoc Analytics/Lift Analysis.md: -------------------------------------------------------------------------------- 1 | * http://info.localytics.com/blog/lift-analysis-for-app-marketing-campaigns 2 | -------------------------------------------------------------------------------- /Adhoc Analytics/Predictive Maintenance.md: -------------------------------------------------------------------------------- 1 | * https://github.com/ashishpatel26/Predictive_Maintenance_using_Machine-Learning_Microsoft_Casestudy 2 | * https://github.com/Azure/MachineLearningSamples-PredictiveMaintenance 3 | 4 | ## Three Part Series 5 | * https://bayesianquest.com/2017/09/30/applied-data-science-series-solving-a-predictive-maintenance-business-problem/ 6 | * https://bayesianquest.com/2018/02/01/applied-data-science-series-solving-a-predictive-maintenance-business-problem-part-ii/ 7 | * https://bayesianquest.com/2018/10/07/applied-data-science-series-solving-a-predictive-maintenance-business-problem-part-iii/ 8 | -------------------------------------------------------------------------------- /Adhoc Analytics/Price Optimization.md: -------------------------------------------------------------------------------- 1 | https://tryolabs.com/blog/price-optimization-machine-learning/ 2 | -------------------------------------------------------------------------------- /Adhoc Analytics/Price Prediction.md: -------------------------------------------------------------------------------- 1 | ##### https://github.com/topics/price-prediction 2 | -------------------------------------------------------------------------------- /Adhoc Analytics/Viterbi Algorithm.md: -------------------------------------------------------------------------------- 1 | ### Link 2 | * https://jarrodkahn.com/posts/49/viterbi-algorithm/ 3 | -------------------------------------------------------------------------------- /Association Rule Mining/Readme.md: -------------------------------------------------------------------------------- 1 | ### Definitions 2 | 3 | #### Association Rule Mining/Market Basket Analysis 4 | 5 | 1. Suitable for non numeric categorical data. 6 | 2. Simple counting required. 7 | 3. Observe Frequenctly occurring patterns, correlations and associations. 8 | 4. Association rule has two parts: 9 | 4.1. an antecedent(if) - Found in data 10 | 4.2. a consequent(then) - Found in combination with antecedent 11 | 5. Three parameters for observing important relationships/controlling the number of rules 12 | 5.1. Support - how frequently itemset appears in the dataset 13 | 5.2. Confidence - how often rule is found to be true 14 | 5.3. Lift - 15 | 6. Applications 16 | 6.1. Market Basket Analysis:- Adjust store layout and product catalogue. 17 | 6.2. Medical Diagnosis:- Defining Relationships between new signs and corresponding diseases. 18 | 6.3. Census Data 19 | 6.4. Protein Sequence:- Associations between different Amino Acids that are present in proteins. 20 | 21 | #### R Implementation 22 | libraries - arules and arulesViz 23 | 24 | #### FP-Growth 25 | 26 | ### Links: 27 | >> https://www.upgrad.com/blog/association-rule-mining-an-overview-and-its-applications/ 28 | >> https://towardsdatascience.com/association-rule-mining-in-r-ddf2d044ae50 29 | 30 | ##### Association Rule mining - Kaggle 31 | >> https://www.kaggle.com/datatheque/association-rules-mining-market-basket-analysis (understand the code) 32 | >> https://medium.com/data-science-group-iitr/association-rule-mining-deciphered-d818f1215b06 33 | -------------------------------------------------------------------------------- /Boosting/Algorithms.md: -------------------------------------------------------------------------------- 1 | 2 | > https://towardsdatascience.com/catboost-vs-light-gbm-vs-xgboost-5f93620723db 3 | > https://hackernoon.com/boosting-algorithms-adaboost-gradient-boosting-and-xgboost-f74991cad38c 4 | 5 | #### Light GBM 6 | https://medium.com/@pushkarmandot/https-medium-com-pushkarmandot-what-is-lightgbm-how-to-implement-it-how-to-fine-tune-the-parameters-60347819b7fc 7 | 8 | -------------------------------------------------------------------------------- /Conditional Random Fields/ReadMe.md: -------------------------------------------------------------------------------- 1 | * https://medium.com/@felixmohr/using-python-and-conditional-random-fields-for-latin-word-segmentation-416ca7a9e513 2 | * http://homepages.inf.ed.ac.uk/csutton/publications/crftut-fnt.pdf 3 | * https://www.analyticsvidhya.com/blog/2018/08/nlp-guide-conditional-random-fields-text-classification/?utm_source=feedburner&utm_medium=email&utm_campaign=Feed%3A+AnalyticsVidhya+%28Analytics+Vidhya%29 4 | * http://sandeep-aparajit.blogspot.com/2013/06/how-does-conditional-random-field-crf.html 5 | 6 | 7 | ### Hidden Markov Model 8 | https://medium.com/acing-ai/what-is-hidden-in-the-hidden-markov-models-eee7bab45ac3 9 | https://people.csail.mit.edu/rameshvs/content/hmms.pdf 10 | -------------------------------------------------------------------------------- /Conditional Random Fields/Terms.md: -------------------------------------------------------------------------------- 1 | Newton Raphson 2 | Hessian 3 | Invertible Matrix - AB=BA=I 4 | Baum Welch 5 | Forward and Backward Algorithm 6 | Viterbi 7 | Taylor Series 8 | -------------------------------------------------------------------------------- /Ensemble Learning/Definitions.md: -------------------------------------------------------------------------------- 1 | ### Ensemble Learning 2 | > Combine Several Base models to produde optimal predictive model. 3 | Comprehensive guide on Ensemble Learning 4 | >> https://www.analyticsvidhya.com/blog/2018/06/comprehensive-guide-for-ensemble-models/ 5 | 6 | Primer for Ensemble Learning Bagging and Boosting 7 | >> https://analyticsindiamag.com/primer-ensemble-learning-bagging-boosting/ 8 | 9 | >> Meta Algorithms:- Learning how to learn 10 | >> MAML:- Model Agnostic Meta Learning 11 | 12 | 1. Bagging 13 | 2. Boosting 14 | 3. Stacking 15 | 16 | #### Ensemble Methods: 17 | 1. Sequential Ensemble Methods:- AdaBoost, Exploit the dependence between the base Learners. Weighing previousy mislabeled examples with higher weight. 18 | 2. Parallel Ensemble Methods:- Exploit the Independence Between Base Learners. Error Reduced by averaging. 19 | 20 | ### Bagging Note: 21 | 1. Stable Learners are not fit as they don't include generlization. 22 | 2. Random Forest:- sampling with replacement and feature sampling. Bias increases but variance decreases because of averaing less correlated trees. 23 | 3. Extremely Randomized Trees: Splitting Threshold are randomized. 24 | 25 | #### Boosting Note: 26 | 1. Fit a sequence of weak Learners. 27 | 2. Base LEarners are trained in a sequence on weighted version of data. 28 | 29 | #### Stacking Note: 30 | 1.Combining multiple classifier or regressor models via a meta classifier or regressor 31 | 32 | #### Ensemble R ML 33 | >> Superlearner Package 34 | >> https://www.datacamp.com/community/tutorials/ensemble-r-machine-learning 35 | ### Links 36 | > https://towardsdatascience.com/ensemble-methods-in-machine-learning-what-are-they-and-why-use-them-68ec3f9fef5f 37 | > https://blog.statsbot.co/ensemble-learning-d1dcd548e936 38 | -------------------------------------------------------------------------------- /Ensemble Learning/Meta Learning or Algorithms.md: -------------------------------------------------------------------------------- 1 | ## Links 2 | 3 | Meta Learning:- 4 | Two Strategies for meta learning 5 | 1. Learning Prior 6 | 2. Learning Strategy 7 | One shot Learning 8 | 9 | ### Learning about ALgorithms that learn to learn 10 | >> https://towardsdatascience.com/learning-about-algorithms-that-learn-to-learn-9022f2fa3dd5 11 | 12 | https://blog.openai.com/reptile/ 13 | ### Reptile:- a meta-learning algorithm designed to perform over a wide array of tasks. 14 | >> https://analyticsindiamag.com/understanding-reptile-a-scalable-meta-learning-algorithm-by-openai/ 15 | 16 | #### The Meta Algorithm of AI 17 | >> https://medium.com/datadriveninvestor/the-meta-algorithm-of-ai-449cb8c039ee 18 | 19 | ### Model Agnostic Meta Learning 20 | >> https://towardsdatascience.com/model-agnostic-meta-learning-maml-8a245d9bc4ac 21 | 22 | -------------------------------------------------------------------------------- /Ensemble Learning/Random Forest.md: -------------------------------------------------------------------------------- 1 | * https://towardsdatascience.com/the-random-forest-algorithm-d457d499ffcd 2 | -------------------------------------------------------------------------------- /Hyperparameter Tuning/README.md: -------------------------------------------------------------------------------- 1 | 2 | ### Hyperparameter tuning in Python 3 | Medium Page:- https://towardsdatascience.com/automated-machine-learning-hyperparameter-tuning-in-python-dfda59b72f8a 4 | Github Page:- https://github.com/WillKoehrsen/hyperparameter-optimization 5 | 6 | -------------------------------------------------------------------------------- /Linear Regression/Blogs.md: -------------------------------------------------------------------------------- 1 | https://towardsdatascience.com/linear-regression-modeling-and-assumptions-dcd7a201502a 2 | -------------------------------------------------------------------------------- /Linear Regression/Questions.md: -------------------------------------------------------------------------------- 1 | #### Linear Regression 2 | > Predicting the value of Y(numerical) from X(numerical or categorical). 3 | 4 | Q . How to Decide Best fit line 5 | A. Least Squares. Minimize sum of squared errors 6 | 7 | #### Linear Regression Assumptions 8 | Link: http://statisticsbyjim.com/regression/ols-linear-regression-assumptions/ 9 | 10 | Assumption 1: The regression model is linear in the coefficients and the error term. 11 | 12 | Assumption 2: The error term has the population mean of zero. 13 | >> Error term Accounts for the variation in Y not explained by X. 14 | 15 | Assumption 3: All independent variables are uncorrelated with the error term. 16 | 17 | Assumption 4: Observations of the error term are uncorrelated with each other.(no autocorrelation) 18 | 19 | Assumption 5: The error term has constant variance(no heteroescdasticity). Residuals vs fitted value 20 | 21 | Note: If condition 4 and 5 exists then error tehrm is IID(Independent and Identically Distributed) 22 | 23 | Assumption 6: No independent variable is perfect linear function of other explanatory variables.(No Multicollinearity) 24 | 25 | Assumption 7: The error term is normally distributed(optional) 26 | 27 | 28 | Negative R2: 29 | If the regressors do not include a constant but (as some regression software packages do) you nevertheless calculate R2 by the formula 30 | 31 | R2=1−∑ni=1e2i∑ni=1(yi−y¯)2 32 | then the R2 can be negative. This is because, without the benefit of an intercept, the regression could do worse than the sample mean in terms of tracking the dependent variable (i.e., the numerator could be greater than the denominator). 33 | -------------------------------------------------------------------------------- /Linear Regression/Regularization.md: -------------------------------------------------------------------------------- 1 | * https://letslearnai.com/2018/03/10/what-is-regularization-in-machine-learning.html 2 | * https://towardsdatascience.com/regularization-in-machine-learning-76441ddcf99a 3 | * https://www.analyticsvidhya.com/blog/2018/04/fundamentals-deep-learning-regularization-techniques/ 4 | -------------------------------------------------------------------------------- /Operations Research/Multi Arm Bandit.md: -------------------------------------------------------------------------------- 1 | ## Dynamic Price Optimization using Multi-Arm Bandit Problem 2 | 3 | ### Multiarm bandit 4 | > Problems solved by MAB algorithms don't have states but actions and rewards. 5 | 6 | > For the problem we are considering, an action or decision is the price of an item and reward is the profit achieved for that price. To summarize, an MAB algorithm involves the follwing steps 7 | 8 | 1. Trial and error decision making:- In a trial and error setting, any Multi Arm Bandit algorithm has 9 | to carefully balance exploration of the serach space while leveraging the better decisions leanred 10 | so far to maximize long term reward. 11 | 2. Receiving reward and using that to influence future decisions 12 | 13 | #### Various MAB algorithms available are essentially charaterized by how exploration and exploitation is balanced. Here is a list of some of the popular algorithms. 14 | 15 | 1. Random greedy 16 | 2. Upper confidence bound one 17 | 3. Upper confidence bound two 18 | 4. Softmax 19 | 5. Interval estimate 20 | 6. Thompson sampling 21 | 7. Reward comparison 22 | 8. Action pursuit 23 | 9. Exponential weight 24 | 25 | ## Links 26 | https://www.linkedin.com/pulse/dynamic-price-optimization-multi-arm-bandit-pranab-ghosh/ 27 | https://github.com/pranab/avenir 28 | 29 | -------------------------------------------------------------------------------- /Operations Research/README.md: -------------------------------------------------------------------------------- 1 | GEKKO 2 | PULP 3 | ORTOOLS 4 | 5 | ### supply chain 6 | https://github.com/KevinFasusi/supplychainpy 7 | https://github.com/KevinFasusi/supplychainpy_notebooks 8 | 9 | ### google ortools 10 | https://github.com/google/or-tools/tree/master/examples/python 11 | https://github.com/google/or-tools/tree/master/examples/notebook 12 | 13 | 14 | ### optimizations in python 15 | https://github.com/rouseguy/Optimization_in_Python/tree/master/notebooks 16 | 17 | ### Decision optimization gihub catalogue IBM 18 | https://github.com/IBMDecisionOptimization 19 | 20 | ### Linear optimization model for distributed energy systems 21 | https://github.com/tum-ens/urbs 22 | 23 | 24 | http://www.authorstream.com/Presentation/ravikeshari-1419781-optimization-techniques-3/ 25 | -------------------------------------------------------------------------------- /POC Case/README.md: -------------------------------------------------------------------------------- 1 | https://medium.com/@wilamelima/analysing-bike-sharing-trends-with-python-a9f574c596b9 2 | -------------------------------------------------------------------------------- /Principal Component Analysis/Readme.md: -------------------------------------------------------------------------------- 1 | ### Links 2 | 3 | * https://towardsdatascience.com/a-one-stop-shop-for-principal-component-analysis-5582fb7e0a9c 4 | * https://medium.com/the-andela-way/foundations-of-machine-learning-singular-value-decomposition-svd-162ac796c27d 5 | * https://medium.com/jungle-book/automatic-feature-extraction-with-t-sne-62826ce09268 6 | * https://medium.com/@aptrishu/understanding-principle-component-analysis-e32be0253ef0 7 | -------------------------------------------------------------------------------- /Probabilistic Graphical Models/Readme.md: -------------------------------------------------------------------------------- 1 | > https://cedar.buffalo.edu/~srihari/CSE674/ 2 | > https://github.com/Zhenye-Na/cs446 3 | > https://www.oreilly.com/learning/probabilistic-programming-from-scratch 4 | -------------------------------------------------------------------------------- /Probability Distribution/Distribution Types.md: -------------------------------------------------------------------------------- 1 | Bernoulli Distribution: 2 | 3 | Binomial 4 | 5 | Hypergeometric 6 | 7 | Exponential 8 | 9 | Weibull 10 | 11 | Gamma 12 | 13 | Beta 14 | 15 | geometric 16 | 17 | normal 18 | 19 | log normal 20 | -------------------------------------------------------------------------------- /Probability Distribution/README.md: -------------------------------------------------------------------------------- 1 | 2 | https://medium.com/@srowen/common-probability-distributions-347e6b945ce4 3 | http://cmdlinetips.com/2018/03/probability-distributions-in-python/ 4 | 5 | http://doktormike.github.io/blog/On-the-apparent-success-of-the-maximum-likelihood-principle/ 6 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Awesome-MachineLearning 2 | Links to Machine Learning Blogs 3 | 4 | ##Super Machine Learning Revision 5 | https://createmomo.github.io/2018/01/23/Super-Machine-Learning-Revision-Notes/?utm_campaign=NLP%20News&utm_medium=email&utm_source=Revue%20newsletter#tableofcontents 6 | 7 | ## Statistical Concepts 8 | > https://www.datasciencecentral.com/profiles/blogs/29-statistical-concepts-explained-in-simple-english-part-1 9 | > https://www.datasciencecentral.com/profiles/blogs/25-statistical-concepts-explained-in-simple-english-part-2 10 | > https://www.datasciencecentral.com/profiles/blogs/29-statistical-concepts-explained-in-simple-english-part-2 11 | > https://www.datasciencecentral.com/profiles/blogs/29-statistical-concepts-explained-in-simple-english-part-4 12 | > https://www.datasciencecentral.com/profiles/blogs/27-statistical-concepts-explained-in-simple-english-part-5 13 | 14 | ## Research Papers 15 | > https://hybridize.ai/trending 16 | > http://www.arxiv-sanity.com/ 17 | 18 | ## Resume building 19 | > https://github.com/jankapunkt/latexcv 20 | > https://github.com/FavioVazquez/best-resume-ever 21 | > https://www.kaggle.com/sachin30 22 | 23 | ## DataQuest Blog 24 | > https://www.dataquest.io/blog/ 25 | 26 | 27 | ## elderresearch 28 | > https://www.elderresearch.com/blog 29 | 30 | ## Pinterest 31 | > https://in.pinterest.com/pin/830421618765097598/?lp=true 32 | 33 | ## Feature Tools 34 | https://github.com/Featuretools 35 | 36 | https://github.com/cs109 37 | https://www.kdnuggets.com/2018/07/building-data-science-product-10-days.html 38 | https://machinelearningmastery.com/spot-check-machine-learning-algorithms-in-python/ 39 | 40 | https://jermwatt.github.io/mlrefined/ 41 | 42 | #### AutoKeras 43 | 44 | 45 | #### Data Science and Artificial Intelligence 46 | -https://chrisalbon.com/#articles 47 | -https://machinelearningmastery.com/prepare-text-data-machine-learning-scikit-learn/ 48 | 49 | #### A gallery of interesting Jupyter Notebooks 50 | > https://github.com/jupyter/jupyter/wiki/A-gallery-of-interesting-Jupyter-Notebooks 51 | 52 | # ICML(International Conference on Machine Learning) 53 | * https://icml.cc/ 54 | 55 | ## Kaggle Deep Learning 56 | * https://www.kaggle.com/learn/deep-learning 57 | 58 | ## Coursera Deep Learning 59 | * https://www.coursera.org/specializations/deep-learning?action=enroll 60 | 61 | ## Machine Learning Yearning 62 | https://d2wvfoqc9gyqzf.cloudfront.net/content/uploads/2018/09/Ng-MLY01-13.pdf 63 | 64 | 65 | http://cs229.stanford.edu/syllabus.html 66 | https://github.com/RatulGhosh/awesome-machine-learning 67 | https://medium.mybridge.co/30-amazing-machine-learning-projects-for-the-past-year-v-2018-b853b8621ac7 68 | https://ml-showcase.com/ 69 | https://www.business-science.io/business/2018/06/19/business-science-problem-framework.html 70 | -------------------------------------------------------------------------------- /Reddit/README.md: -------------------------------------------------------------------------------- 1 | ##### 1. Handling class imbalance in Deep Learning models 2 | > https://www.reddit.com/r/MLQuestions/comments/a7mzuy/handling_class_imbalance_in_deep_learning_models/ 3 | 4 | ##### 2. 5 | -------------------------------------------------------------------------------- /Support Vector Machine/Readme.md: -------------------------------------------------------------------------------- 1 | ### Links 2 | * https://towardsdatascience.com/support-vector-machines-a-brief-overview-37e018ae310f 3 | * https://towardsdatascience.com/support-vector-machine-introduction-to-machine-learning-algorithms-934a444fca47 4 | * https://medium.com/@pushkarmandot/mathematical-view-of-support-vector-machine-power-of-machine-learning-and-artificial-intelligence-f68eb4a466d1 5 | * https://medium.com/deep-math-machine-learning-ai/chapter-3-support-vector-machine-with-math-47d6193c82be 6 | * https://www.analyticsvidhya.com/blog/2017/09/understaing-support-vector-machine-example-code/ 7 | * https://www.analyticsvidhya.com/blog/2017/10/svm-skilltest/ 8 | * https://yunhaocsblog.wordpress.com/2014/07/27/the-effects-of-hyperparameters-in-svm/ 9 | -------------------------------------------------------------------------------- /Support Vector Machine/Thoery.md: -------------------------------------------------------------------------------- 1 | * Hyperplane which maximizes the margin between classes. 2 | 3 | * Support Vectors are the data points closest to the hyperplane and influences the osition and orientation of the hyperplane. 4 | 5 | * The Loss function that helps to maximize the margin is hinge loss. 6 | 7 | * 8 | 9 | * Hyperparameter C 10 | - C Small, Less Missclassification errors. 11 | - C Large, High Missclassification errors. 12 | 13 | * Kernel Trick - Transforms data 14 | - Radial Basis 15 | - Polynomial 16 | - Linear 17 | 18 | -------------------------------------------------------------------------------- /Time Series Forecasting/README.md: -------------------------------------------------------------------------------- 1 | 2 | ### Time Series Analysis for Financial Data I— Stationarity, Autocorrelation and White Noise 3 | https://medium.com/auquan/time-series-analysis-for-financial-data-part-1-stationarity-autocorrelation-and-white-noise-1a1cc2fb23f2 4 | 5 | ### Time Series Analysis for Financial Data II — Auto-Regressive Models 6 | https://medium.com/auquan/time-series-analysis-ii-auto-regressive-models-d0cb1a8a7c43 7 | 8 | ### Time Series Analysis for Financial Data III— Moving Average Models 9 | https://medium.com/auquan/time-series-analysis-for-financial-data-iii-moving-average-models-cccf027f264e 10 | 11 | ### Time Series Analysis for Financial Data IV— ARMA Models 12 | https://medium.com/auquan/time-series-analysis-for-finance-arma-models-21695e14c999 13 | 14 | ### Time Series Analysis for Financial Data V — ARIMA Models 15 | https://medium.com/auquan/time-series-analysis-for-finance-arima-models-acb5e39999df 16 | 17 | ### Time Series Analysis for Financial Data VI— GARCH model and predicting SPX returns 18 | https://medium.com/auquan/time-series-analysis-for-finance-arch-garch-models-822f87f1d755 19 | 20 | ### 21 | * https://towardsdatascience.com/arima-forecasting-vocabulary-ec9b09d55be7 22 | * https://datascienceplus.com/time-series-analysis-using-arima-model-in-r/ 23 | 24 | ## NoteBook 25 | https://github.com/Auquan/Tutorials 26 | 27 | https://www.analyticsvidhya.com/blog/2018/10/predicting-stock-price-machine-learningnd-deep-learning-techniques-python/?utm_source=plus.google.com 28 | 29 | https://www.kaggle.com/abhishekmamidi/time-series-analysis-lstm 30 | -------------------------------------------------------------------------------- /Time Series Forecasting/Video.md: -------------------------------------------------------------------------------- 1 | ### ARIMA Forecasting in R Part 1 - Exploratory Data Analysis 2 | > https://www.youtube.com/watch?v=UekOBfpu8m8 3 | 4 | ### ARIMA Forecasting in R Part 2 - Decomposition of the Data 5 | > https://www.youtube.com/watch?v=-qVAeU-_t_4 6 | 7 | ### ARIMA Forecasting in R Part 3 - Autocorrelations and Choosing the Model Order 8 | > https://www.youtube.com/watch?v=qqAyMwYEIaA 9 | 10 | ### ARIMA Forecasting in R Part 4 - Fitting an ARIMA Model 11 | > https://www.youtube.com/watch?v=GpO89Hx088o 12 | 13 | ### ARIMA Forecasting in R Part 5 - Testing the ARIMA Model with a Holdout Set and Seasonality 14 | > https://www.youtube.com/watch?v=3I_NrpWsVWM 15 | 16 | ### ARIMA Forecasting in R Part 6 - Further Testing and Final Model Selection and Visualization 17 | > https://www.youtube.com/watch?v=k6aiuu9oiTY 18 | -------------------------------------------------------------------------------- /Unsupervised Learning/Latent Dirchlet Process.md: -------------------------------------------------------------------------------- 1 | https://www.youtube.com/watch?v=3mHy4OSyRf0 2 | -------------------------------------------------------------------------------- /Unsupervised Learning/Topic Modeling.md: -------------------------------------------------------------------------------- 1 | * https://medium.com/analytics-vidhya/an-nlp-approach-to-mining-online-reviews-using-topic-modeling-with-python-codes-9d766e783003 2 | * https://www.machinelearningplus.com/nlp/topic-modeling-gensim-python/ 3 | * https://opendatascience.com/nlp-extracting-the-main-topics-from-your-dataset-using-lda-in-minutes/?utm_campaign=Learning+Posts&utm_content=77253139&utm_medium=social&utm_source=twitter 4 | * https://github.com/priya-dwivedi/Deep-Learning/blob/master/topic_modeling/LDA_Newsgroup.ipynb 5 | * https://medium.freecodecamp.org/how-we-changed-unsupervised-lda-to-semi-supervised-guidedlda-e36a95f3a164 6 | * https://hackernoon.com/topic-modeling-and-data-visualization-with-python-flask-b93c236e22c6 7 | * https://medium.com/analytics-vidhya/text-mining-101-a-stepwise-introduction-to-topic-modeling-using-latent-semantic-analysis-using-add9c905efd9 8 | 9 | --------------------------------------------------------------------------------