Supervised Machine Learning All You Need To Know ML CD

Slide 1: This slide introduces Supervised Machine Learning: All You Need to Know. State your company name and begin.
Slide 2: This is our Agenda slide. State your agenda here.
Slide 3: The slide displays Table of contents for the presentation.
Slide 4: The slide renders Title of contents further.
Slide 5: This slide defines the concept of machine learning with a focus on the principles and applications of supervised machine learning.
Slide 6: This slide describes the process of how supervised learning works by using an example of teaching a model to classify shapes.
Slide 7: This slide outlines the systematic process of training a supervised learning model, from data collection to real-world deployment.
Slide 8: This slide highlights the advantages of supervised learning that includes utilizing historical data, defined objectives, ease of evaluation, etc.
Slide 9: This slide provides a comparative analysis of supervised and unsupervised learning, highlighting differences in input data, computational complexity, etc.
Slide 10: This slide details the challenges faced in supervised learning, from the intricacies of complex problem-solving to the essentiality, etc.
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Slide 12: This slide breaks down the two main types of supervised learning, classification and regression, highlighting their key features.
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Slide 14: This slide outlines the key classification techniques in supervised learning, emphasizing on logistic regression, naïve bayes, k-nearest neighbor etc.
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Slide 16: This slide presents Logistic Regression as a tool for predicting binary outcomes, highlighting its use in classification through probabilistic outputs.
Slide 17: This slide elaborates on key terminology essential for comprehending Logistic Regression, highlighting the roles and implications of various model components.
Slide 18: This slide provides an overview of the three types of logistic regression: binomial, multinomial, and ordinal, each tailored to different kinds of classification problems.
Slide 19: This slide details the process of implementing Logistic Regression, highlighting steps from data prep to model assessment and result visualization.
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Slide 21: This slide introduces the Naïve Bayes algorithm, highlighting its foundation, suitability for high-dimensional data, efficiency etc.
Slide 22: This slide delineates the various Naïve Bayes classifiers, outlining their unique attributes and typical applications.
Slide 23: This slide presents a streamlined overview of implementing the Naive Bayes algorithm from scratch.
Slide 24: This slide outlines the key limitations of the Naive Bayes classifier.
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Slide 26: This slide introduces the K-Nearest Neighbors (KNN) algorithm, highlighting its method of finding data similarities.
Slide 27: This slide succinctly outlines the principal distance metrics employed in the K-Nearest Neighbors (KNN) algorithm.
Slide 28: This slide mentions the K-Nearest Neighbors (KNN) algorithm's workflow.
Slide 29: This slide explains the primary disadvantages of the KNN algorithm, including its computational demands, vulnerability to the curse of dimensionality, etc.
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Slide 31: This slide introduces the concept of a decision tree algorithm as part of classification technique that classifies data by identifying patterns.
Slide 32: This slide introduces key decision tree terminologies, including root and leaf nodes, splitting, branching, pruning, etc.
Slide 33: This slide highlights attribute selection measures in decision trees: Information Gain, Gain Ratio, and Gini Index.
Slide 34: This slide outlines the operational flow of the Decision Tree algorithm, highlighting its systematic process.
Slide 35: This slide discusses the challenges associated with Decision Trees, including their complexity, overfitting risks, and increased computational load in scenarios.
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Slide 37: This slide highlights the concept and objectives of Support Vector Machines (SVMs), highlighting their versatility and application diversity.
Slide 38: This slide mentions essential SVM terminologies that include hyperplane, support vectors, margin, kernel, hard margin, soft margin etc.
Slide 39: This slide highlights the primary SVM categories, emphasizing the distinction between linear and non-linear SVMs in handling data separability.
Slide 40: This slide outlines the structured approach to implementing the SVM algorithm, focusing on pre-processing, model fitting, prediction, evaluation, etc.
Slide 41: This slide highlights the primary challenges faced when implementing SVM algorithms.
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Slide 43: This slide mentions key regression techniques in supervised learning and includes linear regression, ridge, lasso, and support vector regression.
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Slide 45: This slide introduces the concept of linear regression, a pivotal predictive analysis tool, and outlines its core assumptions.
Slide 46: This slide methodically categorizes linear regression into simple and multiple variants, focusing on their key characteristics and applicational nuances.
Slide 47: This slide elucidates the concept of a linear regression line and distinguishes between positive and negative linear relationships.
Slide 48: This slide outlines the systematic process of building a Linear Regression model, from data preparation to result visualization.
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Slide 50: This slide introduces the concept of ridge regression, a pivotal predictive analysis tool, and outlines its core assumptions for optimal model performance.
Slide 51: This slide outlines the streamlined process of implementing ridge regression from data loading, preprocess data, initializing model, training and evaluation.
Slide 52: This slide highlights the core advantages of ridge regression, emphasizing its capability to manage multicollinearity, resist outliers, etc.
Slide 53: This slide outlines the primary limitations of ridge regression, including its all-inclusive approach to feature utilization, etc.
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Slide 55: This slide provides an overview of Lasso Regression, a technique designed to improve model accuracy and understanding the concept of Shrinking.
Slide 56: This slide highlights the working of LASSO regression.
Slide 57: This slide presents comparison between Ridge and Lasso regression, highlighting their distinctions in regularization, objectives, etc.
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Slide 59: This slide concisely introduces Support Vector Regression, outlining its methodology for fitting data within a continuous space and its key features.
Slide 60: This slide mentions the essential concepts behind Support Vector Regression, including its SVM foundations, kernel strategies for non-linearities, etc.
Slide 61: This slide details the process of implementing Support Vector Regression, emphasizing the importance of preliminary steps.
Slide 62: This slide encapsulates the key strengths of support vector regression, highlighting its resilience to outliers, adaptability, etc.
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Slide 64: This slide outlines how supervised learning technologies are transforming the retail industry and mention use cases and key players in the space.
Slide 65: This slide again shows how supervised learning technologies are transforming the finance industry and mention use cases and key players in the space.
Slide 66: This slide highlights the streamlined process of using supervised learning for efficient spam detection.
Slide 67: This slide provides an overview of supervised learning in image classification, emphasizing its application in healthcare, security, etc.
Slide 68: This slide outlines the strategic application of supervised learning for churn prediction, distinguishing between classification.
Slide 69: This slide shows how supervised learning is revolutionizing health management, with specific emphasis on its applications in predicting health outcomes.
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Slide 71: This slide presents key metrics for evaluating classification-based supervised learning models, offering insights into their accuracy, precision, etc.
Slide 72: This slide delves into the critical metrics for evaluating regression-based supervised learning models, highlighting the significance of MSE, RMSE, etc.
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Slide 74: This slide highlights the promising future of supervised learning, emphasizing advancements in transfer learning, the use of pre-trained models, etc.
Slide 75: The slide depicts Title of contents further.
Slide 76: This slide highlights the promising future of supervised learning, emphasizing advancements in transfer learning, the use of pre-trained models, etc.
Slide 77: This slide shows all the icons included in the presentation.
Slide 78: This slide is titled as Additional Slides for moving forward.
Slide 79: The slide provides Understanding the concept of semi supervised learning.
Slide 80: The slide depicts Training supervised machine learning model.
Slide 81: The slide shows Understanding various types of supervised learning.
Slide 82: The slide represents Workflow for supervised machine learning classification application.
Slide 83: The slide depicts Process of training supervised learning model.
Slide 84: This is a Thank You slide with address, contact numbers and email address.