What is Applied Machine Learning and How It Works

Applied machine learning is a type of machine learning that's focused on real-world problems. It involves using machine learning algorithms to solve specific business or social issues.

This type of machine learning is different from traditional machine learning in that it's not just about building and training models, but also about deploying and maintaining them in production environments. It requires a deep understanding of the problem domain and the ability to translate business needs into technical requirements.

Applied machine learning involves working with large datasets and using various techniques such as data preprocessing, feature engineering, and model selection to identify patterns and make predictions. According to the article, machine learning algorithms can be used to classify images, predict customer churn, and optimize supply chain logistics.

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Applied machine learning is a field that's disrupting multiple industries today, transforming the way businesses operate and making it possible to solve complex problems. It's a powerful tool that's being used in various sectors, from healthcare to finance, to improve decision-making and drive innovation.

Machine learning models are built using algorithms like Linear Regression, Logistic Regression, Decision Trees, and Random Forest, which are used to solve Classification and Regression problems. These algorithms are the backbone of machine learning, and understanding how they work is crucial for building effective models.

Classification problems involve predicting a categorical outcome, while Regression problems involve predicting a continuous value. For instance, predicting whether a customer will churn or not is a classic example of a Classification problem.

Some popular Ensemble Modeling techniques used in machine learning include Bagging, Boosting, Support Vector Machines (SVM), and Kernel Tricks. These techniques help improve the accuracy of machine learning models by combining the predictions of multiple models.

Machine learning models can be improved through Feature Engineering, which involves selecting and transforming the right features to use in the model. This can include techniques like Principal Component Analysis (PCA) and t-SNE, which help reduce the dimensionality of the data.

Here are some common types of data used in machine learning problems:

By understanding how to work with different types of data and using techniques like Feature Engineering, you can improve the accuracy of your machine learning models and make better predictions.

Machine learning is a broad field with various types, each with its own strengths and applications. Supervised learning, for instance, involves training models on labeled data to make predictions on new, unseen data, as seen in the Google AdWords paper on predicting advertiser churn.

Unsupervised learning, on the other hand, involves training models on unlabeled data to identify patterns or relationships, such as in the DoorDash paper on uncovering online delivery menu best practices. Ensemble modeling, which combines multiple models to improve accuracy, is also a key type of machine learning, as demonstrated in the Walmart paper on large-scale classification using machine learning, rules, and crowdsourcing.

Some notable types of machine learning include classification, regression, and clustering, which are used in various applications such as product categorization, item categorization, and menu item tagging. These types of machine learning are further explored in the following table:

Classification is a fundamental concept in machine learning, and it's used to predict a categorical outcome based on input data. This can be seen in various applications such as predicting advertiser churn for Google AdWords.

Classification problems can be solved using a range of algorithms, including Logistic Regression and Decision Trees. These algorithms are often used in conjunction with ensemble methods like Bagging and Boosting to improve accuracy.

The use of classification can be seen in various industries, including e-commerce, where it's used for large-scale item categorization. For example, NAVER used multiple recurrent neural networks for item categorization in 2016.

Some notable examples of classification include:

Sequence Modelling is a type of machine learning that deals with sequential data, such as text, speech, or time series data. It's used to predict future events or behaviors based on past patterns.

This type of modelling is particularly useful in applications like healthcare, where doctors can use recurrent neural networks to predict clinical events, such as the onset of heart failure.

In the context of e-commerce, sequence modelling can be used to predict user behavior, such as click-through rates, based on their browsing history.

For example, Alibaba used recurrent neural network models for early detection of heart failure onset, and also used search-based user interest modeling with sequential behavior data for CTR prediction.

Some common applications of sequence modelling include:

These applications demonstrate the potential of sequence modelling in various industries, from healthcare to e-commerce.

Information Extraction is a crucial aspect of Machine Learning that involves automatically extracting relevant information from unstructured data such as text, images, and documents.

Researchers have made significant progress in this area, with one notable example being the unsupervised extraction of attributes and their values from product descriptions using machine learning algorithms, as demonstrated in the Rakuten2013 paper.

Here are some examples of Information Extraction techniques and their applications:

These techniques have been developed and applied in various industries, including e-commerce, finance, and healthcare, to name a few.

Weak supervision is a machine learning technique that allows for the deployment of models without requiring large amounts of labeled data.

In 2019, Google developed Snorkel DryBell, a case study in deploying weak supervision at industrial scale. This innovation has enabled the development of machine learning models with limited labeled data.

One notable example of weak supervision is the Osprey system, developed by Intel in 2019. Osprey uses weak supervision to tackle imbalanced extraction problems without the need for code.

Another significant contribution to the field of weak supervision is Overton, a data system developed by Apple in 2019. Overton monitors and improves machine-learned products, showcasing the potential of weak supervision in real-world applications.

Bootstrapping conversational agents with weak supervision is also a promising area of research. IBM's 2019 paper demonstrates the effectiveness of this approach in developing conversational agents with limited labeled data.

Here are some key players in the field of weak supervision: