{"id":4289,"date":"2023-08-01T07:31:30","date_gmt":"2023-08-01T07:31:30","guid":{"rendered":"https:\/\/pickl.ai\/blog\/?p=4289"},"modified":"2025-05-21T15:21:07","modified_gmt":"2025-05-21T09:51:07","slug":"how-to-build-a-machine-learning-model","status":"publish","type":"post","link":"https:\/\/www.pickl.ai\/blog\/how-to-build-a-machine-learning-model\/","title":{"rendered":"How to build a Machine Learning Model?"},"content":{"rendered":"<p><b>Summary<\/b><span style=\"font-weight: 400;\"> : Building a Machine Learning model involves several key steps: data collection, preprocessing, algorithm selection, training, and evaluation. By systematically following this process, you can create effective models that provide valuable insights and accurate predictions. Iteration and refinement are crucial for optimising model performance and ensuring successful deployment in real-world applications.<\/span><\/p>\n<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_82_2 counter-hierarchy ez-toc-counter ez-toc-grey ez-toc-container-direction\">\n<div class=\"ez-toc-title-container\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">Table of Contents<\/p>\n<span class=\"ez-toc-title-toggle\"><a href=\"#\" class=\"ez-toc-pull-right ez-toc-btn ez-toc-btn-xs ez-toc-btn-default ez-toc-toggle\" aria-label=\"Toggle Table of Content\"><span class=\"ez-toc-js-icon-con\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #999;color:#999\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewBox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #999;color:#999\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewBox=\"0 0 24 24\" version=\"1.2\" baseProfile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/span><\/a><\/span><\/div>\n<nav><ul class='ez-toc-list ez-toc-list-level-1 ' ><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/www.pickl.ai\/blog\/how-to-build-a-machine-learning-model\/#Introduction\" >Introduction<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/www.pickl.ai\/blog\/how-to-build-a-machine-learning-model\/#What_is_Machine_Learning\" >What is Machine Learning?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/www.pickl.ai\/blog\/how-to-build-a-machine-learning-model\/#Types_of_Machine_Learning_Model\" >Types of Machine Learning Model<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/www.pickl.ai\/blog\/how-to-build-a-machine-learning-model\/#Supervised_Learning_Models\" >Supervised Learning Models<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/www.pickl.ai\/blog\/how-to-build-a-machine-learning-model\/#Unsupervised_Learning_Models\" >Unsupervised Learning Models<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/www.pickl.ai\/blog\/how-to-build-a-machine-learning-model\/#Reinforcement_Learning_Models\" >Reinforcement Learning Models<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/www.pickl.ai\/blog\/how-to-build-a-machine-learning-model\/#_Deep_Learning_Models\" >\u00a0Deep Learning Models<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"https:\/\/www.pickl.ai\/blog\/how-to-build-a-machine-learning-model\/#Ensemble_Learning_Models\" >Ensemble Learning Models<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-9\" href=\"https:\/\/www.pickl.ai\/blog\/how-to-build-a-machine-learning-model\/#How_to_Build_a_Machine_Learning_Model\" >How to Build a Machine Learning Model?<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-10\" href=\"https:\/\/www.pickl.ai\/blog\/how-to-build-a-machine-learning-model\/#Define_the_Problem\" >Define the Problem<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-11\" href=\"https:\/\/www.pickl.ai\/blog\/how-to-build-a-machine-learning-model\/#Gather_Data\" >Gather Data<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-12\" href=\"https:\/\/www.pickl.ai\/blog\/how-to-build-a-machine-learning-model\/#Explore_and_Preprocess_Data\" >Explore and Preprocess Data<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-13\" href=\"https:\/\/www.pickl.ai\/blog\/how-to-build-a-machine-learning-model\/#Split_the_Data\" >Split the Data<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-14\" href=\"https:\/\/www.pickl.ai\/blog\/how-to-build-a-machine-learning-model\/#Choose_a_Model_Architecture\" >Choose a Model Architecture<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-15\" href=\"https:\/\/www.pickl.ai\/blog\/how-to-build-a-machine-learning-model\/#Feature_Engineering\" >Feature Engineering<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-16\" href=\"https:\/\/www.pickl.ai\/blog\/how-to-build-a-machine-learning-model\/#Train_the_Model\" >Train the Model<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-17\" href=\"https:\/\/www.pickl.ai\/blog\/how-to-build-a-machine-learning-model\/#Validate_and_Evaluate_the_Model\" >Validate and Evaluate the Model<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-18\" href=\"https:\/\/www.pickl.ai\/blog\/how-to-build-a-machine-learning-model\/#Hyperparameter_Tuning\" >Hyperparameter Tuning<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-19\" href=\"https:\/\/www.pickl.ai\/blog\/how-to-build-a-machine-learning-model\/#Fine-tuning_and_Iteration\" >Fine-tuning and Iteration<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-20\" href=\"https:\/\/www.pickl.ai\/blog\/how-to-build-a-machine-learning-model\/#Deploy_the_Model\" >Deploy the Model<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-21\" href=\"https:\/\/www.pickl.ai\/blog\/how-to-build-a-machine-learning-model\/#Monitor_and_Maintain_the_Model\" >Monitor and Maintain the Model<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-22\" href=\"https:\/\/www.pickl.ai\/blog\/how-to-build-a-machine-learning-model\/#What_is_Data_Normalisation_and_Why_is_it_Important\" >What is Data Normalisation, and Why is it Important?<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-23\" href=\"https:\/\/www.pickl.ai\/blog\/how-to-build-a-machine-learning-model\/#Improved_Convergence\" >Improved Convergence<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-24\" href=\"https:\/\/www.pickl.ai\/blog\/how-to-build-a-machine-learning-model\/#Equal_Treatment_of_Features\" >Equal Treatment of Features<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-25\" href=\"https:\/\/www.pickl.ai\/blog\/how-to-build-a-machine-learning-model\/#Robustness_to_Outliers\" >Robustness to Outliers<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-26\" href=\"https:\/\/www.pickl.ai\/blog\/how-to-build-a-machine-learning-model\/#Improved_Model_Performance\" >Improved Model Performance<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-27\" href=\"https:\/\/www.pickl.ai\/blog\/how-to-build-a-machine-learning-model\/#Interpretability\" >Interpretability<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-28\" href=\"https:\/\/www.pickl.ai\/blog\/how-to-build-a-machine-learning-model\/#Regularisation\" >Regularisation<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-29\" href=\"https:\/\/www.pickl.ai\/blog\/how-to-build-a-machine-learning-model\/#Computational_Efficiency\" >Computational Efficiency<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-30\" href=\"https:\/\/www.pickl.ai\/blog\/how-to-build-a-machine-learning-model\/#Conclusion\" >Conclusion<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-31\" href=\"https:\/\/www.pickl.ai\/blog\/how-to-build-a-machine-learning-model\/#Begin_Your_Learning_Journey_with_PicklAI\" >Begin Your Learning Journey with Pickl.AI\u00a0<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-32\" href=\"https:\/\/www.pickl.ai\/blog\/how-to-build-a-machine-learning-model\/#Frequently_Asked_Questions\" >Frequently Asked Questions<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-33\" href=\"https:\/\/www.pickl.ai\/blog\/how-to-build-a-machine-learning-model\/#Why_is_Data_Normalisation_Necessary_for_Machine_Learning_Models\" >Why is Data Normalisation Necessary for Machine Learning Models?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-34\" href=\"https:\/\/www.pickl.ai\/blog\/how-to-build-a-machine-learning-model\/#What_Makes_a_Machine_Learning_model\" >What Makes a Machine Learning model?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-35\" href=\"https:\/\/www.pickl.ai\/blog\/how-to-build-a-machine-learning-model\/#How_Do_I_Choose_the_Right_Machine_Learning_Model\" >How Do I Choose the Right Machine Learning Model?<\/a><\/li><\/ul><\/li><\/ul><\/nav><\/div>\n<h2 id=\"introduction\"><span class=\"ez-toc-section\" id=\"Introduction\"><\/span><b>Introduction<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">As technology continues to impact how machines operate, Machine Learning has emerged as a powerful tool enabling computers to learn and improve from experience without explicit programming.<\/span><\/p>\n<p><a href=\"https:\/\/pickl.ai\/blog\/unsupervised-machine-learning-models-types-applications\/\"><span style=\"font-weight: 400;\">Machine Learning models <\/span><\/a><span style=\"font-weight: 400;\">play a crucial role in this process, serving as the backbone for various applications, from image recognition to natural language processing. In this blog, we will delve into the fundamental concepts of data model for Machine Learning, exploring their types.\u00a0<\/span><\/p>\n<h2 id=\"what-is-machine-learning\"><span class=\"ez-toc-section\" id=\"What_is_Machine_Learning\"><\/span><b>What is Machine Learning?<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><img fetchpriority=\"high\" decoding=\"async\" class=\"alignnone size-full wp-image-12459\" src=\"https:\/\/pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image2-3.jpg\" alt=\"How to Build a Machine Learning Model\" width=\"1000\" height=\"333\" srcset=\"https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image2-3.jpg 1000w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image2-3-300x100.jpg 300w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image2-3-768x256.jpg 768w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image2-3-110x37.jpg 110w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image2-3-200x67.jpg 200w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image2-3-380x127.jpg 380w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image2-3-255x85.jpg 255w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image2-3-550x183.jpg 550w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image2-3-800x266.jpg 800w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image2-3-150x50.jpg 150w\" sizes=\"(max-width: 1000px) 100vw, 1000px\" \/><\/p>\n<p><span style=\"font-weight: 400;\">A data model for Machine Learning is a mathematical representation or algorithm that learns patterns and relationships from data to make predictions or decisions without being explicitly programmed. It is trained on a dataset comprising input features and corresponding target outputs, to generalise its knowledge to unseen data.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">The model\u2019s learning process involves adjusting its internal parameters based on the input data and the desired outcomes, iteratively refining its ability to make accurate predictions. The success of a Machine Learning model depends on various factors, including the quality and quantity of the training data, the model architecture, and hyperparameters\u2019 tuning.\u00a0<\/span><\/p>\n<h2 id=\"types-of-machine-learning-model\"><span class=\"ez-toc-section\" id=\"Types_of_Machine_Learning_Model\"><\/span><b>Types of Machine Learning Model<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><img decoding=\"async\" class=\"alignnone size-full wp-image-12461\" src=\"https:\/\/pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image3-4.jpg\" alt=\"How to Build a Machine Learning Model\" width=\"1000\" height=\"333\" srcset=\"https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image3-4.jpg 1000w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image3-4-300x100.jpg 300w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image3-4-768x256.jpg 768w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image3-4-110x37.jpg 110w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image3-4-200x67.jpg 200w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image3-4-380x127.jpg 380w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image3-4-255x85.jpg 255w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image3-4-550x183.jpg 550w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image3-4-800x266.jpg 800w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image3-4-150x50.jpg 150w\" sizes=\"(max-width: 1000px) 100vw, 1000px\" \/><\/p>\n<p><span style=\"font-weight: 400;\">Machine Learning encompasses a diverse range of models, each designed to tackle specific types of problems. Broadly categorised into supervised, unsupervised, and reinforcement learning, these models utilise various algorithms to analyse data and make predictions. Here is the detailed overview of the same:<\/span><\/p>\n<h3 id=\"supervised-learning-models\"><span class=\"ez-toc-section\" id=\"Supervised_Learning_Models\"><\/span><b>Supervised Learning Models<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Supervised learning involves training a model on labelled data, where the input features and corresponding target outputs are provided. The model learns to map input features to the correct output by minimising the error between its predictions and the actual target values.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Examples of supervised learning models include linear regression, decision trees, support vector machines, and neural networks. These models are widely used for regression and classification tasks. Common examples include:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Linear Regression:<\/b><span style=\"font-weight: 400;\"> It is the best Machine Learning model and is used for predicting continuous numerical values based on input features.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><a href=\"https:\/\/pickl.ai\/blog\/what-is-logistic-regression\/\"><b>Logistic Regression<\/b><\/a><b>:<\/b><span style=\"font-weight: 400;\"> Used for binary classification problems where the output is either 0 or 1.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Support Vector Machines (SVM):<\/b><span style=\"font-weight: 400;\"> Effective for both regression and classification tasks, using a hyperplane to separate data points.<\/span><\/li>\n<\/ul>\n<p><b>Read Blog:<\/b> <a href=\"https:\/\/pickl.ai\/blog\/supervised-learning-vs-unsupervised-learning\/\"><span style=\"font-weight: 400;\">Supervised learning vs Unsupervised learning<\/span><\/a><\/p>\n<h3 id=\"unsupervised-learning-models\"><span class=\"ez-toc-section\" id=\"Unsupervised_Learning_Models\"><\/span><b>Unsupervised Learning Models<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">It deals with unlabelled data, where the model seeks to identify underlying patterns or groupings within the input data. These models do not have a predefined target output but aim to find meaningful structures or representations.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Clustering algorithms like k-means, hierarchical clustering, and dimensionality reduction techniques like Principal Component Analysis (PCA) are typical examples of<\/span><a href=\"https:\/\/pickl.ai\/blog\/unsupervised-machine-learning-models-types-applications\/\"><span style=\"font-weight: 400;\"> unsupervised learning models<\/span><\/a><span style=\"font-weight: 400;\">.<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>K-Means Clustering:<\/b><span style=\"font-weight: 400;\"> Used to partition data into \u2018k\u2019 clusters based on similarity.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Hierarchical Clustering:<\/b><span style=\"font-weight: 400;\"> Organises data into a tree-like structure of clusters, revealing hierarchical relationships.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Principal Component Analysis (PCA):<\/b><span style=\"font-weight: 400;\"> Reduces the dimensionality of data while retaining essential information.\u00a0<\/span><\/li>\n<\/ul>\n<h3 id=\"reinforcement-learning-models\"><span class=\"ez-toc-section\" id=\"Reinforcement_Learning_Models\"><\/span><b>Reinforcement Learning Models<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Reinforcement learning models are designed to interact with an environment and learn from feedback in the form of rewards or penalties. The model aims to maximise the cumulative reward over time by taking appropriate actions. Reinforcement learning has found significant applications in gaming, robotics, and autonomous systems.\u00a0<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">\u00a0<\/span><b>Value-based:<\/b><span style=\"font-weight: 400;\"> Learns the expected future reward (Q-learning).<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Policy-based:<\/b><span style=\"font-weight: 400;\"> Directly learns the optimal policy (policy gradient).<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Actor-Critic:<\/b><span style=\"font-weight: 400;\"> Combines value and policy-based approaches.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Model-Based:<\/b><span style=\"font-weight: 400;\"> Learns a model of the environment for planning.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Model-Free:<\/b><span style=\"font-weight: 400;\"> Learns directly from interaction with the environment.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Deep RL:<\/b><span style=\"font-weight: 400;\"> Combines <\/span><a href=\"https:\/\/pickl.ai\/blog\/unlocking-deep-learnings-potential-with-multi-task-learning\/\"><span style=\"font-weight: 400;\">Deep Learning<\/span><\/a><span style=\"font-weight: 400;\"> with RL for complex tasks.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Hierarchical RL:<\/b><span style=\"font-weight: 400;\"> Breaks down tasks into sub-tasks for efficiency.<\/span><\/li>\n<\/ul>\n<h3 id=\"deep-learning-models\"><span class=\"ez-toc-section\" id=\"_Deep_Learning_Models\"><\/span><b>\u00a0Deep Learning Models<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Deep Learning models are a subset of neural networks with multiple layers (deep architectures). These models have shown remarkable success in various tasks, especially in computer vision, natural language processing, and speech recognition. Common types of Deep Learning models include:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Convolutional Neural Networks (CNN):<\/b><span style=\"font-weight: 400;\"> Ideal for image and video analysis, capturing spatial patterns through convolutional layers.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Recurrent Neural Networks (RNN):<\/b><span style=\"font-weight: 400;\"> Designed for sequential data, like time series and natural language, capable of retaining memory.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Long Short-Term Memory Networks (LSTM):<\/b><span style=\"font-weight: 400;\"> A specialised type of RNN, effective in capturing long-term dependencies in sequential data.<\/span><\/li>\n<\/ul>\n<h3 id=\"ensemble-learning-models\"><span class=\"ez-toc-section\" id=\"Ensemble_Learning_Models\"><\/span><b>Ensemble Learning Models<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Ensemble learning combines multiple individual models to improve overall performance and robustness. By aggregating the predictions of several base models, ensemble methods reduce overfitting and enhance generalisation. Common ensemble learning models include:\u00a0<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Random Forest:<\/b><span style=\"font-weight: 400;\"> A combination of <\/span><a href=\"https:\/\/pickl.ai\/blog\/decision-tree-classification-a-guide-to-machine-learning-algorithm\/\"><span style=\"font-weight: 400;\">decision trees<\/span><\/a><span style=\"font-weight: 400;\">, where each tree contributes to the final prediction through voting.\u00a0<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><a href=\"https:\/\/pickl.ai\/blog\/introduction-to-the-gradient-boosting-algorithm\/\"><b>Gradient Boosting Machines<\/b><\/a><b> (GBM):<\/b><span style=\"font-weight: 400;\"> Builds weak learners sequentially, with each new learner focusing on correcting errors made by its predecessors.\u00a0<\/span><\/li>\n<\/ul>\n<h2 id=\"how-to-build-a-machine-learning-model\"><span class=\"ez-toc-section\" id=\"How_to_Build_a_Machine_Learning_Model\"><\/span><b>How to Build a Machine Learning Model?<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><img decoding=\"async\" class=\"alignnone size-full wp-image-12464\" src=\"https:\/\/pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image1-8.jpg\" alt=\"How to Build a Machine Learning Model\" width=\"1000\" height=\"333\" srcset=\"https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image1-8.jpg 1000w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image1-8-300x100.jpg 300w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image1-8-768x256.jpg 768w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image1-8-110x37.jpg 110w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image1-8-200x67.jpg 200w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image1-8-380x127.jpg 380w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image1-8-255x85.jpg 255w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image1-8-550x183.jpg 550w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image1-8-800x266.jpg 800w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/08\/image1-8-150x50.jpg 150w\" sizes=\"(max-width: 1000px) 100vw, 1000px\" \/><\/p>\n<p><span style=\"font-weight: 400;\">Building a <\/span><a href=\"https:\/\/pickl.ai\/blog\/introduction-to-model-validation-in-python\/\"><span style=\"font-weight: 400;\">Machine Learning model<\/span><\/a><span style=\"font-weight: 400;\"> involves several key steps. Building a Machine Learning model involves several key steps: data collection and preprocessing, selecting the right algorithm, training the model, and evaluating its performance.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">By following this structured approach, you can create effective models that provide valuable insights and accurate predictions for various applications.<\/span><\/p>\n<h3 id=\"define-the-problem\"><span class=\"ez-toc-section\" id=\"Define_the_Problem\"><\/span><b>Define the Problem<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Clearly define the problem you want the Machine Learning model to solve. Understand the objectives and the specific outcome you expect from the model. A well-defined problem will guide all the subsequent steps in the development process.<\/span><\/p>\n<h3 id=\"gather-data\"><span class=\"ez-toc-section\" id=\"Gather_Data\"><\/span><b>Gather Data<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Acquire a relevant and diverse dataset that represents the problem domain. The dataset should contain input features and corresponding target outputs for supervised learning tasks. Ensure the data is clean, free from errors, and appropriately labelled.<\/span><\/p>\n<h3 id=\"explore-and-preprocess-data\"><span class=\"ez-toc-section\" id=\"Explore_and_Preprocess_Data\"><\/span><b>Explore and Preprocess Data<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Explore the dataset to gain insights into the data distribution, missing values, and potential outliers. Handle missing data and outliers appropriately. Perform data preprocessing tasks such as Data Normalisation, feature scaling, and one-hot encoding for categorical variables.<\/span><\/p>\n<h3 id=\"split-the-data\"><span class=\"ez-toc-section\" id=\"Split_the_Data\"><\/span><b>Split the Data<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Divide the dataset into two subsets: a training set and a testing\/validation set. The training set is used to train the model, while the testing\/validation set is used to evaluate its performance on unseen data. Common splits include 80\/20 or 70\/30 ratios.<\/span><\/p>\n<h3 id=\"choose-a-model-architecture\"><span class=\"ez-toc-section\" id=\"Choose_a_Model_Architecture\"><\/span><b>Choose a Model Architecture<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Select a suitable Machine Learning model architecture based on the nature of your problem (e.g., regression, classification, clustering). Consider factors like the complexity of the model, interpretability, and the amount of available data. Popular model architectures include linear regression, decision trees, support vector machines, and neural networks.<\/span><\/p>\n<h3 id=\"feature-engineering\"><span class=\"ez-toc-section\" id=\"Feature_Engineering\"><\/span><b>Feature Engineering<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">It involves selecting and transforming relevant features from the dataset. Extract features that contribute most to the model\u2019s performance and remove redundant or irrelevant ones. Proper feature engineering can significantly improve the model\u2019s accuracy and efficiency.<\/span><\/p>\n<h3 id=\"train-the-model\"><span class=\"ez-toc-section\" id=\"Train_the_Model\"><\/span><b>Train the Model<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">During the training phase, the model learns from the training data by adjusting its internal parameters. This process involves feeding the input data through the model, computing the predictions, comparing them to the true labels. The training process may involve iterative optimization algorithms such as gradient descent.<\/span><\/p>\n<h3 id=\"validate-and-evaluate-the-model\"><span class=\"ez-toc-section\" id=\"Validate_and_Evaluate_the_Model\"><\/span><b>Validate and Evaluate the Model<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Use the testing\/validation set to assess the model\u2019s performance. Calculate relevant evaluation metrics such as accuracy, precision, recall, F1 score, and mean squared error, depending on the problem type (classification or regression). Validation helps to identify potential overfitting or underfitting issues.<\/span><\/p>\n<h3 id=\"hyperparameter-tuning\"><span class=\"ez-toc-section\" id=\"Hyperparameter_Tuning\"><\/span><b>Hyperparameter Tuning<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Machine Learning models often have hyperparameters that control the learning process (e.g., learning rate, number of hidden layers, regularisation strength). Use techniques like grid or random search to find the best combination of hyperparameters for optimal model performance.<\/span><\/p>\n<h3 id=\"fine-tuning-and-iteration\"><span class=\"ez-toc-section\" id=\"Fine-tuning_and_Iteration\"><\/span><b>Fine-tuning and Iteration<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Fine-tuning the model as needed based on the evaluation results. You may revisit earlier steps, such as feature engineering or hyperparameter tuning, to improve the model\u2019s performance further. Iterate through this process until you achieve satisfactory results.<\/span><\/p>\n<h3 id=\"deploy-the-model\"><span class=\"ez-toc-section\" id=\"Deploy_the_Model\"><\/span><b>Deploy the Model<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Once you are satisfied with the model\u2019s performance, deploy it in the target environment to make predictions on new, unseen data. This could involve integrating the model into a web application, mobile app, or other platform suitable for the specific use case.<\/span><\/p>\n<h3 id=\"monitor-and-maintain-the-model\"><span class=\"ez-toc-section\" id=\"Monitor_and_Maintain_the_Model\"><\/span><b>Monitor and Maintain the Model<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Machine Learning models may require periodic monitoring and maintenance, especially if deployed in production environments. Monitor the model\u2019s performance over time and update it if necessary to adapt to changes in the data or requirements.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Following these steps, you can successfully develop and deploy a Machine Learning model to tackle various real-world challenges and make data-driven decisions.\u00a0<\/span><\/p>\n<h2 id=\"what-is-data-normalisation-and-why-is-it-important\"><span class=\"ez-toc-section\" id=\"What_is_Data_Normalisation_and_Why_is_it_Important\"><\/span><b>What is Data Normalisation, and Why is it Important?<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">Data normalisation is the process of scaling and transforming data to ensure consistency and comparability across datasets. This technique is crucial in Machine Learning, as it enhances model performance, reduces bias, and improves convergence rates, ultimately leading to more accurate predictions and better insights from the data.<\/span><\/p>\n<h3 id=\"improved-convergence\"><span class=\"ez-toc-section\" id=\"Improved_Convergence\"><\/span><b>Improved Convergence<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Normalisation brings all the input features to a similar scale. This helps the optimization algorithms, such as gradient descent, converge faster during model training. When features have vastly different ranges, it can lead to slow convergence or cause the learning process to get stuck in local minima.<\/span><\/p>\n<h3 id=\"equal-treatment-of-features\"><span class=\"ez-toc-section\" id=\"Equal_Treatment_of_Features\"><\/span><b>Equal Treatment of Features<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Without normalisation, features with larger magnitudes can dominate the learning process. As a result, the model might give excessive importance to these features, leading to biased predictions. Normalisation ensures that all features are treated equally and contribute proportionally to the model\u2019s decision-making process.<\/span><\/p>\n<h3 id=\"robustness-to-outliers\"><span class=\"ez-toc-section\" id=\"Robustness_to_Outliers\"><\/span><b>Robustness to Outliers<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Outliers, extreme values in the data, can significantly affect the performance of a model. Normalising the data minimises the impact of outliers, making the model more robust and less sensitive to extreme values.<\/span><\/p>\n<h3 id=\"improved-model-performance\"><span class=\"ez-toc-section\" id=\"Improved_Model_Performance\"><\/span><b>Improved Model Performance<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Machine Learning models often use distance-based algorithms, such as k-nearest neighbours or support vector machines. These algorithms are sensitive to the scale of the features. Normalising the data ensures that the model\u2019s performance is not influenced by the choice of units or scales used for measurement.<\/span><\/p>\n<h3 id=\"interpretability\"><span class=\"ez-toc-section\" id=\"Interpretability\"><\/span><b>Interpretability<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">In some cases, the interpretability of the model is crucial. When features are on different scales, it becomes challenging to interpret the impact of each feature on the model\u2019s predictions. Normalisation helps maintain the interpretability of the model by ensuring that the coefficients or feature weights are comparable.<\/span><\/p>\n<h3 id=\"regularisation\"><span class=\"ez-toc-section\" id=\"Regularisation\"><\/span><b>Regularisation<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">In some Machine Learning models, like <\/span><a href=\"https:\/\/pickl.ai\/blog\/l1-and-l2-regularization-in-machine-learning\/\"><span style=\"font-weight: 400;\">Ridge Regression or Lasso Regression<\/span><\/a><span style=\"font-weight: 400;\">, regularisation terms are used to prevent overfitting. These regularisation terms are sensitive to the scale of the features. Normalising the data ensures that the regularisation acts uniformly across all features.<\/span><\/p>\n<h3 id=\"computational-efficiency\"><span class=\"ez-toc-section\" id=\"Computational_Efficiency\"><\/span><b>Computational Efficiency<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Normalisation can also improve the computational efficiency of certain algorithms, especially those that rely on matrix operations. Operations on data with smaller ranges tend to be more computationally efficient.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">It\u2019s important to note that not all <\/span><a href=\"https:\/\/pickl.ai\/blog\/machine-learning-algorithms-that-every-ml-engineer-should-know\/\"><span style=\"font-weight: 400;\">Machine Learning algorithms<\/span><\/a><span style=\"font-weight: 400;\"> require Data Normalisation. For instance, tree-based algorithms like decision trees and random forests are generally unaffected by the scale of features since they split nodes based on the data distribution without using distance metrics.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Data Normalisation is a crucial preprocessing step that helps Machine Learning models perform better, converge faster, and generalise well on unseen data. It enables fair treatment of features and ensures that the model is more robust and reliable, ultimately improving overall performance.\u00a0<\/span><\/p>\n<h2 id=\"conclusion\"><span class=\"ez-toc-section\" id=\"Conclusion\"><\/span><b>Conclusion<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">Data models for Machine Learning have revolutionised how we approach complex problems and make data-driven decisions. With an understanding of the different types of data models for Machine Learning you can embark on your journey to develop powerful and intelligent applications.\u00a0<\/span><\/p>\n<h2 id=\"begin-your-learning-journey-with-pickl-ai\"><span class=\"ez-toc-section\" id=\"Begin_Your_Learning_Journey_with_PicklAI\"><\/span><b>Begin Your Learning Journey with Pickl.AI\u00a0<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">As the career opportunities in the data domain continue to expand, having expertise in technologies like Machine Learning will enhance the growth prospects. For individuals eyeing a prospective career in this field, can enrol with Pickl.AI.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">The e-learning platform offers a host of Data Science courses and a free<\/span><span style=\"font-weight: 400;\"> Machine Learning course<\/span><span style=\"font-weight: 400;\"> that will introduce you to the concepts of Machine Learning and its fundamentals. For more information you can check out the courses on the official website of <\/span><a href=\"https:\/\/pickl.ai\/\"><span style=\"font-weight: 400;\">Pickl.AI<\/span><\/a><span style=\"font-weight: 400;\">.<\/span><\/p>\n<h2 id=\"frequently-asked-questions\"><span class=\"ez-toc-section\" id=\"Frequently_Asked_Questions\"><\/span><b>Frequently Asked Questions<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<h3 id=\"why-is-data-normalisation-necessary-for-machine-learning-models\"><span class=\"ez-toc-section\" id=\"Why_is_Data_Normalisation_Necessary_for_Machine_Learning_Models\"><\/span><b>Why is Data Normalisation Necessary for Machine Learning Models?<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Data Normalisation is a crucial pre-processing step in Machine Learning. It ensures that all input features are on a similar scale, preventing certain features from dominating the learning process due to their larger magnitude. Normalisation helps the model converge faster during training and improves its generalisation to unseen data.\u00a0<\/span><\/p>\n<h3 id=\"what-makes-a-machine-learning-model\"><span class=\"ez-toc-section\" id=\"What_Makes_a_Machine_Learning_model\"><\/span><b>What Makes a Machine Learning model?<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">A Machine Learning model comprises two main components: the architecture and the learned parameters. The architecture defines the model\u2019s structure, including the number of layers and nodes in neural networks or the rules in decision trees. The learned parameters are the internal weights and biases the model adjusts during training to make accurate predictions.\u00a0<\/span><\/p>\n<h3 id=\"how-do-i-choose-the-right-machine-learning-model\"><span class=\"ez-toc-section\" id=\"How_Do_I_Choose_the_Right_Machine_Learning_Model\"><\/span><b>How Do I Choose the Right Machine Learning Model?<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Choosing the right Machine Learning model depends on the nature of your data and the problem you&#8217;re solving. Consider factors such as the type of data (labelled or unlabelled), the complexity of the task, and performance metrics. Experimenting with different models and evaluating their results can help identify the best fit.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"Learn the essential steps to build effective Machine Learning models for accurate predictions and insights.\n","protected":false},"author":26,"featured_media":12458,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"om_disable_all_campaigns":false,"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":""},"categories":[2],"tags":[1420,1418,1416,1419,1422,1417,1424,1423,1421],"ppma_author":[2216,2179],"class_list":{"0":"post-4289","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-machine-learning","8":"tag-best-machine-learning-models","9":"tag-custom-machine-learning-models","10":"tag-data-model-for-machine-learning","11":"tag-how-to-build-a-machine-learning-model","12":"tag-how-to-create-machine-learning-algorithm","13":"tag-how-to-design-a-machine-learning-model","14":"tag-types-of-machine-learning-model","15":"tag-what-is-data-normalization","16":"tag-why-data-normalization-is-necessary-for-machine-learning-models"},"yoast_head":"<!-- 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