| 2024-11-04 01:39:47,152 - app_logger - INFO - [Document(metadata={'source': 'dataset/Data Science from Scratch by Joel Grus.pdf', 'page': 202}, page_content='Chapter 11. Machine Learning\nI am always r eady to learn although I do not always like being taught.\n� W inston Churchill\nMany people imagine that data science is mostly machine learning and that\ndata scientists mostly build and train and tweak machine learning models all\nday long. (Then again, many of those people don� t actually know what\nmachine learning is .) In fact, data science is mostly turning business\nproblems into data problems and collecting data and understanding data and\ncleaning data and formatting data, after which machine learning is almost\nan afterthought. Even so, it� s an interesting and essential afterthought that\nyou pretty much have to know about in order to do data science.\nM o d e l i n g\nBefore we can talk about machine learning, we need to talk about models .\nWhat is a model? It� s simply a specification of a mathematical (or\nprobabilistic) relationship that exists between dif ferent variables.'), Document(metadata={'source': 'dataset/Data Science from Scratch by Joel Grus.pdf', 'page': 500}, page_content='Products\nk-nearest neighbors , k-Nearest Neighbors - For Further Exploration\nlogistic regression , Logistic Regression - Support V ector Machines\nmachine learning and , What Is Machine Learning?\nmultiple regression , Multiple Regression - For Further Exploration'), Document(metadata={'source': 'dataset/Data Science from Scratch by Joel Grus.pdf', 'page': 505}, page_content='reinforcement models , What Is Machine Learning?\nrelational databases , Databases and SQL\nrequests library , HTML and the Parsing Thereof\nrescaling data , Rescaling\nrobots.txt files , Example: Keeping T abs on Congress\nS\nscalar multiplication , V ectors\nscale , Rescaling\nscatterplot matrix , Many Dimensions\nscatterplots , Scatterplots - For Further Exploration\nscikit-learn , For Further Exploration , For Further Exploration , For Further\nExploration , For Further Exploration , For Further Investigation , For Further\nExploration , For Further Exploration , scikit-learn\nSciPy , For Further Exploration , For Further Exploration\nscipy .stats , For Further Exploration\nScrapy , For Further Exploration\nseaborn , For Further Exploration , V isualization\nSELECT statement , SELECT\nsemisupervised models , What Is Machine Learning?\nserialization , JSON and XML\nsets , Sets\nsigmoid function , Feed-Forward Neural Networks , Other Activation\nFunctions'), Document(metadata={'source': 'dataset/Data Science from Scratch by Joel Grus.pdf', 'page': 494}, page_content='logistic function , The Logistic Function\nmodel application , Applying the Model\nproblem example , The Problem\nsupport vector machines , Support V ector Machines\ntools for , For Further Investigation\nloss functions , Using Gradient Descent to Fit Models , Loss and\nOptimization , Softmaxes and Cross-Entropy\nLSTM (long short-term memory) , Recurrent Neural Networks\nM\nmachine learning\nbias-variance tradeof f , The Bias-V ariance T radeof f\ncorrectness , Correctness\ndefinition of term , What Is Machine Learning?\nfeature extraction and selection , Feature Extraction and Selection\nmodeling , Modeling\noverfitting and underfitting , Overfitting and Underfitting\nresources for learning about , For Further Exploration\nmagnitude, computing , V ectors\nmanipulating data , Manipulating Data\nMapReduce\nanalyzing status updates example , Example: Analyzing Status Updates\nbasic algorithm , MapReduce')] |
| 2024-11-04 01:40:36,256 - app_logger - INFO - [Document(metadata={'source': 'dataset/Data Science from Scratch by Joel Grus.pdf', 'page': 202}, page_content='Chapter 11. Machine Learning\nI am always r eady to learn although I do not always like being taught.\n� W inston Churchill\nMany people imagine that data science is mostly machine learning and that\ndata scientists mostly build and train and tweak machine learning models all\nday long. (Then again, many of those people don� t actually know what\nmachine learning is .) In fact, data science is mostly turning business\nproblems into data problems and collecting data and understanding data and\ncleaning data and formatting data, after which machine learning is almost\nan afterthought. Even so, it� s an interesting and essential afterthought that\nyou pretty much have to know about in order to do data science.\nM o d e l i n g\nBefore we can talk about machine learning, we need to talk about models .\nWhat is a model? It� s simply a specification of a mathematical (or\nprobabilistic) relationship that exists between dif ferent variables.'), Document(metadata={'source': 'dataset/Data Science from Scratch by Joel Grus.pdf', 'page': 500}, page_content='Products\nk-nearest neighbors , k-Nearest Neighbors - For Further Exploration\nlogistic regression , Logistic Regression - Support V ector Machines\nmachine learning and , What Is Machine Learning?\nmultiple regression , Multiple Regression - For Further Exploration'), Document(metadata={'source': 'dataset/Data Science from Scratch by Joel Grus.pdf', 'page': 505}, page_content='reinforcement models , What Is Machine Learning?\nrelational databases , Databases and SQL\nrequests library , HTML and the Parsing Thereof\nrescaling data , Rescaling\nrobots.txt files , Example: Keeping T abs on Congress\nS\nscalar multiplication , V ectors\nscale , Rescaling\nscatterplot matrix , Many Dimensions\nscatterplots , Scatterplots - For Further Exploration\nscikit-learn , For Further Exploration , For Further Exploration , For Further\nExploration , For Further Exploration , For Further Investigation , For Further\nExploration , For Further Exploration , scikit-learn\nSciPy , For Further Exploration , For Further Exploration\nscipy .stats , For Further Exploration\nScrapy , For Further Exploration\nseaborn , For Further Exploration , V isualization\nSELECT statement , SELECT\nsemisupervised models , What Is Machine Learning?\nserialization , JSON and XML\nsets , Sets\nsigmoid function , Feed-Forward Neural Networks , Other Activation\nFunctions'), Document(metadata={'source': 'dataset/Data Science from Scratch by Joel Grus.pdf', 'page': 494}, page_content='logistic function , The Logistic Function\nmodel application , Applying the Model\nproblem example , The Problem\nsupport vector machines , Support V ector Machines\ntools for , For Further Investigation\nloss functions , Using Gradient Descent to Fit Models , Loss and\nOptimization , Softmaxes and Cross-Entropy\nLSTM (long short-term memory) , Recurrent Neural Networks\nM\nmachine learning\nbias-variance tradeof f , The Bias-V ariance T radeof f\ncorrectness , Correctness\ndefinition of term , What Is Machine Learning?\nfeature extraction and selection , Feature Extraction and Selection\nmodeling , Modeling\noverfitting and underfitting , Overfitting and Underfitting\nresources for learning about , For Further Exploration\nmagnitude, computing , V ectors\nmanipulating data , Manipulating Data\nMapReduce\nanalyzing status updates example , Example: Analyzing Status Updates\nbasic algorithm , MapReduce')] |
