Intro to Machine Learning

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Questions and Answers

Match the following Machine Learning concepts with their descriptions:

Supervised Learning = Algorithm trained on labeled data to make predictions or classifications. Unsupervised Learning = Algorithm identifies patterns in unlabeled data without human guidance. Reinforcement Learning = Algorithm learns to make decisions by receiving rewards or punishments. Traditional Programming = Utilizes predefined rules to process data and produce answers.

Match each term related to Machine Learning with its correct definition:

Algorithm = A set of rules or steps that a computer follows to solve a problem or make a decision. Model = A representation of patterns learned from data, used for predictions or classifications. Data Set = A collection of related sets of information that is composed of separate elements but can be manipulated as a unit by a computer. Features = The individual measurable properties or characteristics of a phenomenon being observed.

Match the following phases of a Machine Learning project with their respective actions:

Data Collection = Gathering raw data from relevant sources for analysis. Model Training = Using collected data to teach the algorithm to recognize patterns. Evaluation = Assessing the model's accuracy and performance on unseen data. Deployment = Integrating the trained model into an application or system for practical use.

Match the tasks with the appropriate machine learning approach:

<p>Classification = Assigning data points to predefined categories. Regression = Predicting a continuous numerical value. Clustering = Grouping similar data points together. Dimensionality Reduction = Reducing the number of variables of a data set while preserving its information.</p>
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Match the following terms with their application in machine learning:

<p>Data preprocessing = Cleaning and transforming raw data into a suitable format. Feature engineering = Selecting, modifying, and creating features to improve model performance. Cross-validation = Evaluating model performance using multiple subsets of the data. Hyperparameter tuning = Optimizing the settings of a learning algorithm.</p>
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Match the following machine learning evaluation metrics with their definitions:

<p>Accuracy = The proportion of correctly classified instances. Precision = The proportion of true positives out of all predicted positives. Recall = The proportion of true positives out of all actual positives. F1-Score = The harmonic mean of precision and recall.</p>
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Match the descriptions with the appropriate types of Machine Learning algorithms:

<p>Linear Regression = Algorithm that finds the best linear relationship between input and output variables for prediction. Decision Tree = Algorithm that models decisions based on a tree-like structure of if-then-else conditions. K-Means Clustering = Algorithm that groups data points into clusters based on their proximity to cluster centers. Neural Network = Algorithm that simulates the structure of the human brain to recognize patterns.</p>
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Match the data input parameter type with whether or not it is necessary to determine heart failure:

<p>BPM = Beats per minute, important input for the model to determine heart failure BMI = Body Mass Index, value input to the model to determine heart failure Age = Age input to model to help predict heart failure Sex = Sex of patient, input to help predict heart failure</p>
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Match the following data pre-processing techniques with their descriptions:

<p>Normalization = Scaling data to a standard range (e.g., 0 to 1). Standardization = Transforming data to have a mean of 0 and a standard deviation of 1. Handling Missing Values = Imputing or removing incomplete data entries. Encoding Categorical Variables = Converting text data into numerical form for model compatibility.</p>
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Match the key concept of machine learning with its definition:

<p>Feature extraction = Process of transforming raw data into numerical or categorical features that are suitable for modeling; Overfitting = Describes the phenomenon where a machine learning model learns the training data too well, capturing noise and outliers that do not generalize to new data. Regularization = Technique used to prevent overfitting by adding a penalty term to the loss function of a machine learning model, discouraging it from learning overly complex or specific patterns in the training data. Gradient Descent = Optimization algorithm used to find the minimum of a function. It is commonly used in machine learning to train models</p>
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Match the scenario with the appropriate machine learning technique:

<p>Spam Detection = Uses labeled data to classify emails into spam and non-spam categories. Customer Segmentation = Groups customers based on purchasing behavior. Predicting Stock Prices = Analyzes historical data to predict future stock trends. Recommendation Systems = Suggests products or content based on user preferences and behavior.</p>
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Match the following steps with the reasons to use them in ML algorithms:

<p>Evaluate the current machine learning model = Essential to assess the performance of the model on unseen data and ensure it generalizes well. Choosing the appropraite model = Affects the speed, accuracy, and complexity of the ML predictions. Data Selection and Preparation = Choosing the data appropriate for inputs, and cleaning and labeling them. Deploy model = Makes trained mode available for real-time predictions and decision-making.</p>
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Match traditional programming approches to ML approaches:

<p>Traditional Approach = We can continuously train the model to be used in the future to predict values. Machine Learning = Takes data and answers to create the rules. Inputs for traditional algorigthm = Algorithm is determined, can't be adjusted based on inputs.</p>
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Match the following limitations of ML based on the type of model that is incorrect:

<p>Overfitting = Model learns the training data too well, making predictions that are too accurate and difficult to generalize to new data. Underfitting = Model is overly simplified so it does not effectively learn the underlying patterns in the training data. Bias = Systematic error in ML model that results in unfair or skewed predictions and outcomes. Variance = Model is highly sensitive to the noise in the training data which affects performance on new external data.</p>
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Match the following statements with Supervised Learning or Unsupervised Learning:

<p>Supervised Learning = Algorithm ingests unlabeled data, draws inferences, and finds patterns. Unsupervised Learning = Algorithm is trained on human-labeled data.</p>
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Match the following inputs with how they help in determining success in ML:

<p>Inputs Quality = Accurate and relevant, resulting in better model performance. Algorithm Selection = Impacts the speed, accuracy, and complexity of the analysis. Data Preprocessing = Enhances the reliability and consistency of results. Evaluation Metrics = Quantifiable, used to find the current ML performance.</p>
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Match the following challenges in ML with how to resolve them:

<p>Overfitting = Apply cross-validation techniques to assess generalization performance. Underfitting = Increase features to get more data. Lack of Labeled Data = Use semi-supervised learning or active learning strategies to label data. Bias = Improve data or add more data.</p>
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Match the following components of Reinforcement Learning with their definitions:

<p>State = Represents the current situation or configuration of the environment that the agent perceives. Action = Represents a decision made by the agent based on its current state. Reward = Scalar feedback signal that quantifies the immediate desirability or undesirability. Policy = Maps states to actions.</p>
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Match the following approaches with potential ethical considerations:

<p>Bias = Model might discriminate or make unfair decisions. Transparency = Make sure to allow stakeholders to understand or validate the algorithm's decisions. Data Privacy = Protect user infomration from potential risks in model training. Compliance = Follow local standards.</p>
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Match the following steps with what they improve in ML algorithms:

<p>Data Cleaning = Removes errors, duplicates, or inconsistencies leading to more reliable and credible results. Data Integration = Combining data from multiple sources. Data Transformation = Scales, normalizes, or aggregates data to be more suitable for analytical tasks. Data Reduction = Reduces complexity and mitigates the curse of dimensionality.</p>
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Flashcards

What is Machine Learning?

A subset of AI that uses computer algorithms to analyze data and make intelligent decisions based on what it has learned.

How is ML different?

ML builds models to classify and make predictions from data instead of following rule-based algorithms.

Traditional vs. Machine Learning

Traditional methods take data and rules to develop an algorithm. ML takes data and answers to create the algorithm.

What is Supervised Learning?

An algorithm is trained on human-labeled data to classify new data.

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What is Unsupervised Learning?

The algorithm is given unlabeled data and finds patterns by itself.

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What is Reinforcement Learning?

The model figures out how to achieve a goal by trial and error, with rewards or punishments based on the decision made.

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What is a Machine Learning Model?

An algorithm used to find patterns in data without the programmer explicitly programming these patterns.

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Study Notes

  • Machine learning is a subset of AI
  • It uses computer algorithms to analyze data
  • Algorithms make intelligent decisions based on prior learnings
  • Machine learning builds models to classify and make predictions from data, instead of following rules-based algorithms

Example

  • Machine learning can determine whether a heart may fail
  • Inputs can include beats per minute (BPM), body mass index (BMI), age, sex, and result
  • The dataset is used to learn and create a model to predict results

Traditional vs ML

  • Traditional methods use data and rules to develop an algorithm for an answer
  • Machine learning creates the algorithm with data and answers
  • The model determines the rules and the if-then-else statement when inputs are received for machine learning
  • Continuously training a model allows predictions to be made in the future

Common Patterns

  • Machine learning relies on defining behavioral rules by examining and comparing large datasets to find common patterns

Supervised Learning

  • Supervised learning algorithms are trained on human-labeled data
  • The more samples provided, the more precise in classifying new data a supervised learning algorithm becomes

Unsupervised Learning

  • Unsupervised learning provides the algorithm with unlabeled data, allowing it to find patterns independently
  • The machine infers qualities from unlabeled data, drawing inferences and finding patterns
  • Data can be clustered by how similar it is to its neighbors and how dissimilar it is to everything else with this type of learning
  • Post-clustering, different techniques can be used to explore the data and identify patterns

Reinforcement Learning

  • Reinforcement learning provides a machine learning algorithm with rules and constraints to achieve goals
  • The state, desired goal, allowed actions, and constraints are defined
  • The algorithm attempts to achieve the goal by testing different actions and is rewarded or punished based on the decision's outcome
  • The algorithm maximizes rewards within constraints

Summary

  • Machine learning models are algorithms that find patterns in data without explicit programming of those patterns

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