Bridging the Gap: A Guide to Machine Learning for Non-Machine Learning Engineers
By Matt Lane, Product Strategist and Conceptual Designer
From R&D to product, business team members play a vital role in setting the direction of AI model development. They need to understand the technical jargon of data science and translate it into actionable insights. This article provides businesspeople and product managers with the key concepts of machine learning that are needed to become a better translator.
One of the most important concepts in machine learning is the idea of algorithms. Algorithms are sets of instructions that computers can follow to make predictions. In the context of machine learning, algorithms are used to learn patterns from data and then use those patterns to make predictions about new data. For example, an algorithm could be used to learn patterns from data about past home sales in San Francisco and New York City. The algorithm could then use these patterns to predict the selling price of a new home in one of these cities.
There are there are three different approaches to machine learning, depending on the task. These include:
- Supervised learning: This involves training an algorithm on labeled data, where each data point has a corresponding label. For example, you could train a supervised learning algorithm to classify images as cats or dogs.
- Unsupervised learning: This involves training an algorithm on unlabeled data, where the data points do not have any labels. The algorithm must then find patterns in the data on its own. For example, you could use unsupervised learning to cluster customers into distinct groups based on their purchase history.
- Reinforcement learning: This involves training an algorithm through trial and error. The algorithm is given a reward for taking actions that lead to a desired outcome and is penalized for taking actions that lead to an undesired outcome. For example, you could use reinforcement to train a robot to walk.
Another important concept in machine learning is data. Data is the fuel that powers machine-learning algorithms. In general, the more data you have, the better the algorithm will be able to learn patterns and make accurate predictions. However, it is important to note that data quality is just as important as data quantity. If data is inaccurate or incomplete, the machine-learning model will not be able to learn accurate patterns.
Once data is collected, it needs to be pre-processed. Pre-processing involves cleaning and formatting data so that it can be used by a machine-learning algorithm. For example, you might need to remove missing values from your data or convert categorical variables into numerical variables.
After you have pre-processed your data, you can start to train your machine-learning algorithm. Training involves feeding data to the algorithm and letting it learn patterns from the data. The more data you feed into your model, the better it will be able to learn. However, it is important to avoid overfitting. Overfitting occurs when a model learns the patterns in the training data too well and is not able to generalize to new data.
Once your model is trained, you can start to evaluate it. Evaluation involves testing your model on new data that it has not seen before. This helps you to assess how well your model is generalizing to new data. If you are happy with the performance of your model, you can then deploy it. Deployment involves putting your model into production so that it can be used to make predictions on new data, ideally when used in the real world by customers.
Here are some other important things to keep in mind about machine learning:
- Feature engineering: This is the process of feature selection and creating new features from existing data. Feature engineering can be an especially important part of improving the performance of a machine learning model.
- Bias and variance: Bias occurs when a model is too basic and cannot then generalize accurately on new data. Variance is the opposite of bias. This occurs when the model is so fine-tuned to the training data that it again will not generalize accurately on new data. It is important to find a balance between bias and variance to create a good machine-learning model.
- Ethical considerations: There are many ethical considerations involved in using machine learning models. For example, it is important to make sure that your models are not biased against certain groups of people or misaligned with the intent of the larger context in which your business operates.
Building on the foundational concepts explored above, here we delve deeper into the nuances of machine learning through the lens of a product manager. Remember, the PM's role is pivotal in translating technical complexities into actionable business insights and fostering effective collaboration between data scientists and stakeholders to deliver customer value.
Beyond Algorithms: Unveiling the Model Landscape
While trained algorithms form the core of machine learning, understanding their intricacies within the broader model landscape is crucial. Some common ones include:
- Linear Regression: A versatile tool for predicting continuous values based on a linear relationship between variables. For example, forecasting sales based on historical data.
- Decision Trees: Imagine a series of "if-then" rules, splitting data into branches based on specific conditions. They are efficient for interpretability but prone to overfitting.
- Support Vector Machines (SVMs): These algorithms seek the optimal hyperplane to separate data points into distinct categories, excelling in classification tasks.
- K-Nearest Neighbors (KNN): This intuitive approach classifies new data points based on the majority vote of its K nearest neighbors in the training set.
- Neural Networks: Inspired by the human brain, these interconnected layers of neurons learn complex patterns and are particularly effective for image recognition and natural language processing.
No single algorithm reigns supreme. Choosing the right tool depends on your specific problem, data characteristics, and desired outcomes. With that in mind, the following is presented to help guide your decisions about the best approach.
Predicting Continuous Values:
Model: Linear Regression.
Use Cases: Sales forecasting, product demand prediction, and customer lifetime value estimations.
Classifying Data:
Models:
Decision Trees: Easy to interpret, good for exploring feature importance.
Support Vector Machines (SVM): Effective for high-dimensional data, good for clear class boundaries.
K-Nearest Neighbors (KNN): Simple and intuitive, good for small datasets.
Random Forests: Ensemble method combining multiple decision trees for improved accuracy.
Use Cases: Spam detection, image classification (e.g., cat vs. dog), sentiment analysis, and fraud detection.
Uncovering Hidden Patterns:
Models:
K-Means Clustering: Divides data into distinct groups based on similarities.
Principal Component Analysis (PCA): Reduces data dimensionality while preserving key information.
Use Cases: Customer segmentation, market research, and anomaly detection.
Making Decisions Through Trial and Error:
Model: Reinforcement Learning.
Use Cases: Robot control, game playing, dynamic pricing, and personalized recommendations.
Generating Text or Translating Languages:
Models: Recurrent Neural Networks (RNNs) and Transformers.
Use Cases: Chatbots, machine translation, text summarization, and content generation.
Analyzing Images or Videos:
Model: Convolutional Neural Networks (CNNs).
Use Cases: Image classification (e.g., medical imaging), object detection (e.g., self-driving cars), and video analysis.
Some Final Considerations
More Complex Models: In situations where there are intricate patterns or relationships in the data that simpler models cannot capture effectively, using more complex models may be beneficial. Examples include tasks where the data is highly nonlinear or contains subtle interactions between features.
Less Complex Models: Simpler models are preferred when interpretability is paramount or when there is limited data available. They are easier to understand and interpret, making them suitable for applications where transparency and explainability are essential, such as in regulated industries or when making critical decisions.
About the author
Matt Lane is an entrepreneurial and generalist product architect with expertise in designing software in zero-to-one, growth, and mature life-cycle stages.
Matt aims to discover the right solutions, be an editor in the design process, design and run experiments and research activities, act as the point person for enablement, feature, and integrated full-stack product teams to help make scope trade-offs, and coach teams to think big while continuously delivering value under the conditions of uncertainty.
Matt started his career as a founding member of a startup in NYC, where he worked on mixed reality spatial audio technologies and collaborated with industry leaders to advance content delivery platforms. During this time, Matt also launched AI-enabled products, like auto-mixing, that benefited Grammy, Pulitzer Prize, Emmy, and Academy Award winners.
More recently, Matt has been involved in developing and delivering software products across multiple industries and product categories, such as B2B/Prosumer/Consumer SaaS, fintech, media technologies, extended reality music technologies, low-latency machine learning-powered products, and logistics and supply chain marketplace solutions. Matt has a BA from the University of Pennsylvania with a concentration in economics.
By Matt Lane, Product Strategist and Conceptual Designer
From R&D to product, business team members play a vital role in setting the direction of AI model development. They need to understand the technical jargon of data science and translate it into actionable insights. This article provides businesspeople and product managers with the key concepts of machine learning that are needed to become a better translator.
One of the most important concepts in machine learning is the idea of algorithms. Algorithms are sets of instructions that computers can follow to make predictions. In the context of machine learning, algorithms are used to learn patterns from data and then use those patterns to make predictions about new data. For example, an algorithm could be used to learn patterns from data about past home sales in San Francisco and New York City. The algorithm could then use these patterns to predict the selling price of a new home in one of these cities.
There are there are three different approaches to machine learning, depending on the task. These include:
- Supervised learning: This involves training an algorithm on labeled data, where each data point has a corresponding label. For example, you could train a supervised learning algorithm to classify images as cats or dogs.
- Unsupervised learning: This involves training an algorithm on unlabeled data, where the data points do not have any labels. The algorithm must then find patterns in the data on its own. For example, you could use unsupervised learning to cluster customers into distinct groups based on their purchase history.
- Reinforcement learning: This involves training an algorithm through trial and error. The algorithm is given a reward for taking actions that lead to a desired outcome and is penalized for taking actions that lead to an undesired outcome. For example, you could use reinforcement to train a robot to walk.
Another important concept in machine learning is data. Data is the fuel that powers machine-learning algorithms. In general, the more data you have, the better the algorithm will be able to learn patterns and make accurate predictions. However, it is important to note that data quality is just as important as data quantity. If data is inaccurate or incomplete, the machine-learning model will not be able to learn accurate patterns.
Once data is collected, it needs to be pre-processed. Pre-processing involves cleaning and formatting data so that it can be used by a machine-learning algorithm. For example, you might need to remove missing values from your data or convert categorical variables into numerical variables.
After you have pre-processed your data, you can start to train your machine-learning algorithm. Training involves feeding data to the algorithm and letting it learn patterns from the data. The more data you feed into your model, the better it will be able to learn. However, it is important to avoid overfitting. Overfitting occurs when a model learns the patterns in the training data too well and is not able to generalize to new data.
Once your model is trained, you can start to evaluate it. Evaluation involves testing your model on new data that it has not seen before. This helps you to assess how well your model is generalizing to new data. If you are happy with the performance of your model, you can then deploy it. Deployment involves putting your model into production so that it can be used to make predictions on new data, ideally when used in the real world by customers.
Here are some other important things to keep in mind about machine learning:
- Feature engineering: This is the process of feature selection and creating new features from existing data. Feature engineering can be an especially important part of improving the performance of a machine learning model.
- Bias and variance: Bias occurs when a model is too basic and cannot then generalize accurately on new data. Variance is the opposite of bias. This occurs when the model is so fine-tuned to the training data that it again will not generalize accurately on new data. It is important to find a balance between bias and variance to create a good machine-learning model.
- Ethical considerations: There are many ethical considerations involved in using machine learning models. For example, it is important to make sure that your models are not biased against certain groups of people or misaligned with the intent of the larger context in which your business operates.
Building on the foundational concepts explored above, here we delve deeper into the nuances of machine learning through the lens of a product manager. Remember, the PM's role is pivotal in translating technical complexities into actionable business insights and fostering effective collaboration between data scientists and stakeholders to deliver customer value.
Beyond Algorithms: Unveiling the Model Landscape
While trained algorithms form the core of machine learning, understanding their intricacies within the broader model landscape is crucial. Some common ones include:
- Linear Regression: A versatile tool for predicting continuous values based on a linear relationship between variables. For example, forecasting sales based on historical data.
- Decision Trees: Imagine a series of "if-then" rules, splitting data into branches based on specific conditions. They are efficient for interpretability but prone to overfitting.
- Support Vector Machines (SVMs): These algorithms seek the optimal hyperplane to separate data points into distinct categories, excelling in classification tasks.
- K-Nearest Neighbors (KNN): This intuitive approach classifies new data points based on the majority vote of its K nearest neighbors in the training set.
- Neural Networks: Inspired by the human brain, these interconnected layers of neurons learn complex patterns and are particularly effective for image recognition and natural language processing.
No single algorithm reigns supreme. Choosing the right tool depends on your specific problem, data characteristics, and desired outcomes. With that in mind, the following is presented to help guide your decisions about the best approach.
Predicting Continuous Values:
Model: Linear Regression.
Use Cases: Sales forecasting, product demand prediction, and customer lifetime value estimations.
Classifying Data:
Models:
Decision Trees: Easy to interpret, good for exploring feature importance.
Support Vector Machines (SVM): Effective for high-dimensional data, good for clear class boundaries.
K-Nearest Neighbors (KNN): Simple and intuitive, good for small datasets.
Random Forests: Ensemble method combining multiple decision trees for improved accuracy.
Use Cases: Spam detection, image classification (e.g., cat vs. dog), sentiment analysis, and fraud detection.
Uncovering Hidden Patterns:
Models:
K-Means Clustering: Divides data into distinct groups based on similarities.
Principal Component Analysis (PCA): Reduces data dimensionality while preserving key information.
Use Cases: Customer segmentation, market research, and anomaly detection.
Making Decisions Through Trial and Error:
Model: Reinforcement Learning.
Use Cases: Robot control, game playing, dynamic pricing, and personalized recommendations.
Generating Text or Translating Languages:
Models: Recurrent Neural Networks (RNNs) and Transformers.
Use Cases: Chatbots, machine translation, text summarization, and content generation.
Analyzing Images or Videos:
Model: Convolutional Neural Networks (CNNs).
Use Cases: Image classification (e.g., medical imaging), object detection (e.g., self-driving cars), and video analysis.
Some Final Considerations
More Complex Models: In situations where there are intricate patterns or relationships in the data that simpler models cannot capture effectively, using more complex models may be beneficial. Examples include tasks where the data is highly nonlinear or contains subtle interactions between features.
Less Complex Models: Simpler models are preferred when interpretability is paramount or when there is limited data available. They are easier to understand and interpret, making them suitable for applications where transparency and explainability are essential, such as in regulated industries or when making critical decisions.