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<p>Machine Learning can be a solution to this as we can train an algorithm to automatically learn from data or from experience. It allows computers to learn and make predictions or decisions without being explicitly programmed.</p>
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<div class="paragraph">
<p>It involves applying mathematical algorithms on a dataset to recognize patterns and make informed predictions or decisions on new, unseen data. The result is a trained model that can be used in production settings for continued inferencing and automation.</p>
<p>It involves applying mathematical algorithms on a dataset to recognize patterns and make informed predictions or decisions on new, unseen data. The result is a trained model that can be used in production settings for continued inference and automation.</p>
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<p>Machine Learning has revolutionized various industries, including healthcare, finance and transportation.</p>
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</dd>
<dt class="hdlist1">Unsupervised learning</dt>
<dd>
<p>Learning from unlabeled data to discover patterns or relationships without explicit guidance as to what exists in the data. The model discovers hidden structures or insights within the data, for instance to identify different segments or groups of customers based on their spending habits, items that have a high possibility of being purchased together in a supermarket, or anomalous behavior. Examples of algorithms fitting into this category are K-means clustering, K-nearest neighbords (KNN), density-based spatial clustering of applications with noise (DBSCAN), Principal Component Analysis (PCA) and autoencoders.</p>
<p>Learning from unlabeled data to discover patterns or relationships without explicit guidance as to what exists in the data. The model discovers hidden structures or insights within the data, for instance to identify different segments or groups of customers based on their spending habits, items that have a high possibility of being purchased together in a supermarket, or anomalous behavior. Examples of algorithms fitting into this category are K-means clustering, K-nearest neighbors (KNN), density-based spatial clustering of applications with noise (DBSCAN), Principal Component Analysis (PCA) and auto-encoders.</p>
</dd>
<dt class="hdlist1">Semi-Supervised Learning</dt>
<dd>
<p>Semi-Supervised learning is a machine learning algorithm that works between the supervised and unsupervised learning. It uses a small portion of labelled and a large majority of unlabeled data. The labelled data is used to provide order to the learning problem while the unlabeled data is used to create a more generalized model by providing context. It allows us to create more accurate and resilient models by negating the caveats that exists for supervised and unsupervised learning. It is particularly useful when obtaining labelled data is costly, time-consuming, or resource-intensive. Semi-supervised learning is applied in image and speech analysis, for example.</p>
</dd>
<dt class="hdlist1">Reinforcement learning</dt>
<dd>
<p>In comparison to the previous groups of algorithms mentioned, reinforcement learning does not have the goal of grouping or classifying data. It is applied to find the optimal behavior of an agent for a given learning task. The agent learns to make decisions through trial and error interactions with an environment, and it receives feedback in the form of rewards or penalties for its actions, guiding it towards achieving a goal by maximizing the rewards. Noteworthy examples are autonomous driving or AlphaGo learning to play Go by playing against itself. Typical reinforcement learning algorithms are Monte Carlo, Deep Q Networks(DQN), State-Action-Reward-State-Acction(SARSA) and others.</p>
<p>In comparison to the previous groups of algorithms mentioned, reinforcement learning does not have the goal of grouping or classifying data. It is applied to find the optimal behavior of an agent for a given learning task. The agent learns to make decisions through trial and error interactions with an environment, and it receives feedback in the form of rewards or penalties for its actions, guiding it towards achieving a goal by maximizing the rewards. Noteworthy examples are autonomous driving or AlphaGo learning to play Go by playing against itself. Typical reinforcement learning algorithms are Monte Carlo, Deep Q Networks(DQN), State-Action-Reward-State-Action(SARSA) and others.</p>
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<h2 id="_choosing_your_algorithm"><a class="anchor" href="#_choosing_your_algorithm"></a>Choosing your algorithm</h2>
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<p>We have a plethora of mathematical algorithms that may, or may not, solve our problem and be applied on our data. But, how do you recognise the category of problem and the algorithm that can help you solve it?
<p>We have a plethora of mathematical algorithms that may, or may not, solve our problem and be applied on our data. But, how do you recognize the category of problem and the algorithm that can help you solve it?
The clue lies with your outcome variable.</p>
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<p>In a regression problem, the outcome variable is a continuous value and the goal is predicting a quantitative value. Common examples are predicting house prices, temperatures and sale revenues.</p>
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<p>Knowing the outcome variable you are able to identify if its supervised, unsupervised, semi-supervised or reinforcement learning. However, there is a lot of work left before the final algorithm is chosen. The data scientist will spend time exploring the data set, applying different visualization and data cleaning technqiues exploring the underlying data, and several different machine learning algorithms may be applied to test which one solves the problem. Solving the problem is multifacetted and there is no one algorithm to rule them all.</p>
<p>Knowing the outcome variable you are able to identify if its supervised, unsupervised, semi-supervised or reinforcement learning. However, there is a lot of work left before the final algorithm is chosen. The data scientist will spend time exploring the data set, applying different visualization and data cleaning techniques exploring the underlying data, and several different machine learning algorithms may be applied to test which one solves the problem. Solving the problem is multifaceted and there is no one algorithm to rule them all.</p>
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<dt class="hdlist1">Data Exploration and Preparation</dt>
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<p>Includes collecting, cleaning, and preprocessing the data. This means investigating, removing and/or substituting missing data points and outliers. Representation of occurances and labels is also investigated and visualized through different techinques.</p>
<p>Includes collecting, cleaning, and preprocessing the data. This means investigating, removing and/or substituting missing data points and outliers. Representation of occurrence and labels is also investigated and visualized through different techniques.</p>
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