Data Science Machine Learning
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Questions and Answers

What is the primary focus of machine learning?

  • Manually programming solutions to problems
  • Designing systems that can visualize data
  • Creating algorithms that can learn from data (correct)
  • Discovering unknown patterns in large data sets

    • How does data mining differ from machine learning?

  • Data mining is focused on creating algorithms
  • Machine learning only analyzes historical data
  • Machine learning discovers patterns in data
  • Data mining aims to discover properties of data sets (correct)

    • Which of the following tasks is typically associated with machine learning?

  • Making decisions based on pre-defined rules
  • Generating random data
  • Spam detection (correct)
  • Sorting data into categories

    • What principle distinguishes machine learning from a traditional rule-based approach?

    <p>Machine learning learns decision rules from examples (D)</p> Signup and view all the answers

    What is the goal of machine learning in data science?

    <p>To allow computers to predict future behaviors (D)</p> Signup and view all the answers

    In machine learning, how are complex rules defined?

    <p>Through machine analysis of data without explicit definitions (A)</p> Signup and view all the answers

    Which of these examples is not a typical application of machine learning?

    <p>Manual data entry (D)</p> Signup and view all the answers

    What is a primary characteristic of the outputs from machine learning algorithms?

    <p>Outputs are complex and can vary based on data (A)</p> Signup and view all the answers

    What is the primary objective when starting a data mining project?

    <p>Identify your business goals (A)</p> Signup and view all the answers

    Which question is an example of using classification algorithms?

    <p>Will this tire fail in the next 1000 miles? (A)</p> Signup and view all the answers

    What type of algorithms are used to detect anomalous activities?

    <p>Anomaly Detection algorithms (B)</p> Signup and view all the answers

    How do regression algorithms serve in the context of data science?

    <p>To make numerical predictions (B)</p> Signup and view all the answers

    Which situation would best utilize clustering algorithms?

    <p>Grouping customers by their purchasing behavior (C)</p> Signup and view all the answers

    Which question cannot typically be answered with a precise name or number?

    <p>What can my data tell me about my business? (D)</p> Signup and view all the answers

    What are the two essential parts of each example in supervised learning?

    <p>Features and Labels (B)</p> Signup and view all the answers

    What is the role of data in machine learning?

    <p>Machine learning requires data for processing (A)</p> Signup and view all the answers

    What is a typical question answered by clustering algorithms?

    <p>Who is likely to respond to a marketing campaign? (A)</p> Signup and view all the answers

    Which of the following is NOT one of the five key questions data science can answer?

    <p>What is the market trend? (C)</p> Signup and view all the answers

    When is machine learning particularly beneficial?

    <p>When there is no existing formula or equation (A)</p> Signup and view all the answers

    What defines the success of a machine learning model?

    <p>An evaluation function aligned with business goals (C)</p> Signup and view all the answers

    What do good features in machine learning typically result in?

    <p>Improved model performance (B)</p> Signup and view all the answers

    In the context of sentiment analysis, what might features represent?

    <p>Keywords and phrases from reviews (B)</p> Signup and view all the answers

    Which statement best captures the essence of machine learning problems?

    <p>Machine learning is great for complex tasks without clear solutions. (A)</p> Signup and view all the answers

    In sentiment analysis, which of the following labels would classify a review with a score of 1-2 stars?

    <p>Negative (A)</p> Signup and view all the answers

    What is a primary consideration for determining the need for machine learning?

    <p>The task has complex rules and unstructured data. (C)</p> Signup and view all the answers

    How can a problem be clearly formulated for machine learning?

    <p>By determining the relationship between input and output. (D)</p> Signup and view all the answers

    What is an important factor when considering the application of machine learning?

    <p>There should be sufficient examples to train a model. (A)</p> Signup and view all the answers

    Which scenario exemplifies the use of reinforcement learning algorithms?

    <p>A robot vacuum deciding whether to continue cleaning or recharge. (C)</p> Signup and view all the answers

    What does sentiment analysis involve in a machine learning context?

    <p>Assessing customer review texts to predict sentiment. (A)</p> Signup and view all the answers

    Which statement best represents the definition of success in the context of machine learning?

    <p>Success includes achieving specific, predetermined outcomes. (D)</p> Signup and view all the answers

    In what situation would machine learning not be appropriate?

    <p>When dealing with low-volume, highly structured data. (C)</p> Signup and view all the answers

    Which of the following best describes a crucial aspect of finding meaningful representations of data?

    <p>Using visualizations and transformations to enhance data insight. (A)</p> Signup and view all the answers

    What is the primary goal of supervised learning?

    <p>To map input variables to corresponding output variables (C)</p> Signup and view all the answers

    Which of the following is NOT a type of machine learning mentioned?

    <p>Graphic learning (D)</p> Signup and view all the answers

    In supervised learning, what kind of data is used for training?

    <p>Labeled data (C)</p> Signup and view all the answers

    Which application is considered a preferred approach for machine learning?

    <p>Speech recognition (A)</p> Signup and view all the answers

    What is an example of a scenario where machine learning might be applied?

    <p>Robot control (C)</p> Signup and view all the answers

    Which statement about unsupervised learning is incorrect?

    <p>It relies on mapping input to specific output. (A)</p> Signup and view all the answers

    What factor is driving the acceleration in machine learning's growth?

    <p>Improved data capture and faster computers (D)</p> Signup and view all the answers

    Which learning type uses labeled data for training and prediction?

    <p>Supervised learning (B)</p> Signup and view all the answers

    Which of the following best defines classification in supervised learning?

    <p>Drawing conclusions from observed values to categorize new observations (A)</p> Signup and view all the answers

    What is the primary focus of regression analysis in machine learning?

    <p>Estimating the relationship among one dependent variable and several independent variables (D)</p> Signup and view all the answers

    What distinguishes unsupervised learning from supervised learning?

    <p>There is no supervision or labeled data provided to the model (D)</p> Signup and view all the answers

    Which scenario illustrates the use of clustering in unsupervised learning?

    <p>Grouping images of fruit based on color and shape (D)</p> Signup and view all the answers

    Which statement accurately describes semi-supervised learning?

    <p>It combines aspects of both supervised and unsupervised learning (A)</p> Signup and view all the answers

    In the context of machine learning, what does forecasting primarily involve?

    <p>Making predictions based on historical and current data (C)</p> Signup and view all the answers

    What is the main goal of the unsupervised learning algorithm?

    <p>To find hidden patterns and similarities within the data (B)</p> Signup and view all the answers

    Which of the following tasks is NOT typically associated with supervised learning?

    <p>Customer segmentation (C)</p> Signup and view all the answers

    Flashcards

    Machine Learning

    A technique to teach computers to predict future behavior, outcomes, and trends from historical data.

    Data Mining

    Discovering hidden patterns and relationships in data to reveal useful information.

    Machine Learning vs. Data Mining

    Machine learning uses learned knowledge to predict, while data mining focuses on discovering patterns in data.

    Machine Learning Application Examples

    Self-driving cars, spam detection, fraud detection, voice recognition, face recognition, anomaly detection, sales forecasting, and robotics.

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    Traditional Approach

    Using explicitly programmed rules to solve problems.

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    Machine Learning Approach

    Learning from examples to solve problems; machines create rules, not humans.

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    Computer Uses Historical Data

    Machine learning uses historical data to estimate future outcomes.

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    Machine Learning Definition

    Computers acting without explicit instructions, by learning from data.

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    Business Data Mining Goals

    Specific objectives for using data analysis to improve business outcomes

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    Data Mining Project Plan

    Detailed strategy for conducting the data analysis project

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    Data Mining Questions

    Questions answerable using data; need to be specific to be useful.

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    Classification Algorithm

    Data analysis technique for identifying categories or classes.

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    Anomaly Detection Algorithm

    Identifies unusual or outlier data points.

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    Regression Algorithm

    Predicting numeric values.

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    Clustering Algorithm

    Groups similar data points together.

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    Sharp Business Questions

    Specific questions that can be answered using data names or numbers

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    What is reinforcement learning used for?

    Reinforcement learning algorithms are used to determine the best action to take in a given situation, usually by machines or robots. This is done by learning from past experiences and rewards.

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    Why might machine learning be needed?

    Machine learning is useful for tasks that involve high volumes of complex, unstructured data that are difficult to program explicitly.

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    Can you formulate your problem clearly?

    Before applying machine learning, make sure you can define your problem clearly by specifying what you want to predict (output) given which input data.

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    What is sufficient data in machine learning?

    Machine learning models need a large and diverse amount of examples (data) to learn effectively. Make sure you have enough data to train your model.

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    What is a regular pattern in machine learning?

    Machine learning works best when there's a discernible pattern or predictable relationship between the input data and the desired output.

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    What are meaningful representations of data?

    Transforming your data into a format that the machine learning model can understand and use efficiently.

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    How do you define success in machine learning?

    Clearly define what constitutes success for your machine learning model, which can vary depending on the problem.

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    How do you determine if machine learning is right for your problem?

    Consider these key aspects: automation needs, problem clarity, sufficient data, regular patterns, data representation, and success definition.

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    Machine Learning with Data

    Machine learning algorithms need data to learn patterns and make predictions. More data generally leads to better performance.

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    Supervised Learning: Features and Labels

    In supervised learning, each data example has two parts: features (attributes describing the example) and a label (the answer you want to predict).

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    Sentiment Analysis Example

    Sentiment analysis uses machine learning to understand the emotional tone of text, often based on customer reviews and ratings.

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    Regular Patterns for Learning

    Machine learning works best when there are regular, recurring patterns in the data. It struggles with rare or irregular events.

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    Meaningful Data Representations

    Machine learning algorithms often use numerical representations (feature vectors) of data. Effective features are crucial for success.

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    Sentiment Analysis Features

    For sentiment analysis, customer reviews are often represented as vectors of word frequencies, where common words are features.

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    Success in Machine Learning

    Machine learning aims to optimize a training criteria (evaluation function) that aligns with business goals.

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    When to Use Machine Learning

    Consider machine learning for complex tasks with tons of data and variables, where traditional formula-based approaches fail.

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    Supervised Learning

    A type of machine learning where the algorithm learns from labeled data, meaning each input has a corresponding output. The algorithm then uses this mapping to predict outputs for new, unseen inputs.

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    Supervised Learning Goal

    The goal of supervised learning is to establish a relationship between input variables (X) and output variables (Y). The algorithm seeks to learn this mapping and use it for accurate predictions.

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    Classification (Supervised)

    A type of supervised learning where the goal is to categorize data points into predefined classes or groups. For example, identifying email as spam or not spam.

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    Regression (Supervised)

    A type of supervised learning used for predicting continuous values, such as predicting house prices or stock prices based on influencing factors.

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    Unsupervised Learning

    A type of machine learning where the algorithm learns patterns from unlabeled data. It doesn't have predefined outputs, focusing on finding inherent structures in the data.

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    Clustering (Unsupervised)

    A type of unsupervised learning where the goal is to group similar data points together based on their characteristics. For example, grouping customers based on their purchasing habits.

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    Semi-supervised Learning

    A type of machine learning that combines both supervised and unsupervised learning techniques. It uses a mix of labeled and unlabeled data to improve learning efficiency.

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    Reinforcement Learning

    A type of machine learning where the algorithm learns through trial and error. It receives rewards for making correct actions and penalties for incorrect ones, constantly improving its decisions.

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    Classification

    A supervised learning task where the algorithm learns to categorize data into predefined classes. For example, classifying emails as 'spam' or 'not spam'.

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    Regression

    A supervised learning task where the algorithm learns the relationship between input variables and a continuous output variable. Used for prediction and forecasting.

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    Forecasting

    Predicting future trends or values based on historical data. Commonly used in business, finance, and weather analysis. Often used as part of regression tasks.

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    What are some real-world applications of supervised learning?

    Supervised learning can be used for tasks like risk assessment, fraud detection, spam filtering, and image recognition.

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    Study Notes

    Big Data Analytics

    • Big data analytics is a field focused on analyzing large datasets.
    • Machine learning and data mining are techniques used for big data analytics.

    Machine Learning vs. Data Mining

    • There is no single, universally agreed-upon definition of machine learning versus data mining.
    • Machine learning focuses on creating algorithms that learn from historical data to make predictions.
    • Data mining aims to discover properties and useful information within datasets.
    • Machine learning can be used as a method in data mining.

    Machine Learning Example Applications

    • Self-driving cars
    • Spam detection
    • Fraud detection
    • Voice recognition
    • Face recognition
    • Anomaly detection
    • Sales forecasting
    • Robotics

    What is Machine Learning?

    • Machine learning is a data science technique where computers learn from existing data to anticipate future behaviors, outcomes, and trends.
    • Machine learning involves learning from historical data, recognizing patterns and trends, and making predictions.

    How Machine Learning Works

    • Data is divided into training, validation, and test sets.
    • The training set is used to build the model.
    • The validation set is used to assess the model's performance.
    • The test set is used to evaluate the final model's performance.
    • The model is tuned using more data, different features, or adjusted parameters.
    • Trained models are used to predict new data.

    An Example of a Machine Learning Task (Car Rental)

    • The task is to forecast car rental demand.
    • Steps include: getting data, preparing data, training the model, evaluating the model, and predicting future demand.

    Difference Between Traditional and Machine Learning

    • Rule-based approach
      • Explicitly programmed to solve problems
      • Decision rules are clearly defined by humans
    • Machine learning approach
      • Trained from examples
      • Decision rules are complex and fuzzy
      • Rules are learned by machines from data

    Summary

    • Machine learning uses historical data for predictions.
    • Similar to data mining, but focuses on applying prior knowledge to make decisions.
    • Machines approximate complex functions and learn rules from data.

    The Data Science Process

    • Ask an interesting question: Understand the scientific goal, what to predict.
    • Get the data: How was data sampled? Are there privacy issues?
    • Explore the data: Visualize, look for anomalies, find patterns.
    • Model the data: Build and fit the model. Validate the model.
    • Communicate and visualize the results: What was learned? Were the results useful?

    How to Start a Data Science Project

    • Identify business goals
    • Assess the current situation
    • Identify data mining goals
    • Create a project plan

    Sharp vs. Vague Questions

    • Sharp questions can be answered with data (e.g., stock price).
    • Vague questions can't (e.g., how to increase profits).

    The 5 Questions Data Science Can Answer

    • Is this A or B? (Classification)
    • Is this weird? (Anomaly Detection)
    • How much or how many? (Regression)
    • How is this organized? (Clustering)
    • What should I do now? (Reinforcement Learning)

    Q1: Is This A or B?

    • Use Classification algorithms
    • Example: Will this tire fail in the next 1000 miles? (Yes/No)
    • Another Example: Which brings in more customers? ($5 coupon or 25% discount?)

    Q2: Is this Weird?

    • Use Anomaly Detection algorithms
    • Example: Your credit card company identifying unusual transactions.

    Q3: How Much? or How Many?

    • Use Regression algorithms
    • Example: Predicting the temperature next Tuesday.
    • Example: Predicting fourth quarter sales.

    Q4: How is This Organized?

    • Use Clustering algorithms
    • Examples: Clustering viewers with similar movie tastes.
    • Examples: Clustering printer models that fail the same way.

    Q5: What Should I Do Now?

    • Use Reinforcement Learning algorithms
    • Examples: Self-driving car deciding to brake or accelerate at a yellow light.
    • Examples: Robot vacuum deciding whether to keep cleaning or return to charging station.

    So, What Do You Want to Find Out?

    • Regression: Forecast future outcomes by estimating the relationship between variables.
    • Anomaly Detection: Identify and predict unusual data points.
    • Clustering: Separate similar data points into groups.
    • Classification: Assign new data points to categories or classes.

    When to Use Machine Learning

    • To automate tasks.
    • To deal with high-volume tasks involving complex rules and unstructured data.
    • When sufficient examples are available to train a model.
    • If the problem has a discernible pattern that can be recognized by the model.
    • When you can create meaningful representations of the data.
    • Define what success means for the outcome

    Summary (Machine Learning)

    • Use machine learning when there's a complex task involving large amounts of data and no existing formula, for cases such as speech recognition.

    Machine Learning Types

    • Supervised learning (classification, regression)
    • Unsupervised learning (clustering)
    • Semi-supervised learning
    • Reinforcement learning

    Growth of Machine Learning

    • Increasing use for natural language processing, computer vision, medical analysis, and robotics.
    • Improved algorithms and increased computing power.

    Supervised Learning

    • Goal: Map input variables with output variables.
    • Learning method using labeled data.
    • Example categories include Risk Assessment, Fraud Detection, Spam filtering, etc.

    Supervised Learning Applications

    • Classification
    • Regression
    • Forecasting

    Unsupervised Learning

    • Learning with unlabeled data.
    • Goal: Classifies data points based on similarities, differences, and patterns.
    • Clustering is a common unsupervised learning technique

    Semi-Supervised Learning

    • Combines labeled and unlabeled data for learning.

    Reinforcement Learning

    • Agent learns from experiences (without labeled data), with reward mechanisms.
    • Common examples include game theory, operation research, and multi-agent systems
    • Ambari
    • Avro
    • Cassandra
    • Chukwa
    • HBase
    • Hive
    • Mahout
    • Pig
    • Spark
    • Tez
    • ZooKeeper

    Key Components of Mahout

    • Collaborative filtering
    • Classification
    • Clustering

    Mahout Reference Book

    • Chapter content in the Mahout reference book by Owen, Anil, Dunning, and Friedman.

    Mahout Overview

    • Mahout's move away from MapReduce to a DSL for linear algebraic operations.

    Clustering

    • Given a dataset, find clusters of similar data points.
    • Similarity (distance) measures (like Euclidean distance) are used to group data points (in 2D,3D, or higher dimensional space)
    • Clustering needs an algorithm, a notion of similarity and a stop condition to identify clusters.

    k-means Clustering

    • Algorithm for partitioning datasets into clusters.
    • Iterative process of assigning data points to the nearest centroid.
    • Steps involved in k-means clustering: selecting the number of clusters, randomly selecting initial centroids, measuring distance, and assigning each point to the nearest centroid.
    • Steps involved (continuation): recalculating centroids, repeating steps 2 and 3 until there's no change in centroids, or a maximum number of iterations is reached.
    • evaluating the result by comparing initial and final centroids locations.

    Questions

    • Determining a good value for k.
    • Handling data in various dimensions

    The Elbow Method for Determining k

    • Plot of F vs k, looking for an elbow in the graph identifying a good value for k.

    Question 2: What if the Data is 2-Dimensional, 3 Dimensional...?

    • Methods for calculating distances in multi-dimensional space are needed in addition to calculating distances in 2D or 3D.

    Hadoop k-means Clustering Jobs

    • In Mahout, the MapReduce version of the k-means algorithm runs using the KMeansDriver class.

    K-means Clustering Running as MapReduce Job

    • Parallelization of tasks to speed up clustering on large datasets using MapReduce.

    HelloWorld Clustering Scenario

    HelloWorld Clustering Scenario (Part II)

    • Detailed code for setting up k-means clustering using Hadoop in Mahout.

    HelloWorld Clustering Scenario (Part III)

    • Executing k-means clustering on Hadoop using the Java KMeansDriver framework.

    HelloWorld Clustering Scenario Result

    • Output generated from running the KMeansDriver using the defined method.

    Testing Distance Measures

    • Different ways to measure the distance between data points.

    Manhattan Distances

    • Weighted distance is part of Mahout.

    Results Comparison

    • Comparing different methods for measuring distance and the number of iterations needed.

    Classification

    • Definition and an example using a classification table.

    How Does a Classification System Work?

    • Diagram outlining the process used to classify data by training the model.

    Process 1: Model Construction

    Process 2: Using the Model in Prediction

    When to Use Mahout for Classification

    • Guidelines for choosing Mahout for classification based on the size of the data.

    Advantage of Using Mahout for Classification

    • Diagram showing the improved performance of Mahout with large data sets.

    Key Terminology for Classification

    • Definitions for different classification terms that are needed for learning about machine learning.

    Workflow in a Typical Classification Project

    • Typical stages of a classification project.

    Choosing Algorithms via Mahout

    • Algorithm choice guidelines based on dataset size.

    Decision Tree

    • Basic Classification algorithm, using a divide-and-conquer method of splitting on training data by attributes until the final outcome is assigned to a tree leaf.

    Regression

    • Predicting a continuous variable based on other variables.

    Regression - Example

    • Worked examples involving linear plots, polynomial fits and calculations and visualizations.

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