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Machine learning ppt download
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Application Furthermore, there are more and more techniques apply machine learning as a solution. In the future, machine learning will play an important role in our daily life. Conclusion You just clipped your first slide! Clipping is a handy way to collect important slides you want to go back to later. Now customize the name of a clipboard to store your clips.
Visibility Others can see my Clipboard. Cancel Save. Get SlideShare without ads Enjoy access to millions of presentations, documents, ebooks, audiobooks, magazines, and more ad-free. Read free for 60 days. NipulPatel20 Dec. VedantiKangane Dec. TirthLoliyani Dec. PriyaRani Nov. Show More. Total views. Slide 16 : The following slide illustrates the role of AI in the Supply Chain that includes logistics, procurement, manufacturing, customers, and service.
Slide 17 : This slide introduces you to all the AI Chatbots in Healthcare such as search engines, social platforms, smartphones, health bots, artificial intelligence, messenger apps, and the app ecosystem. Slide 18 : This slide discusses the reason for Why AI is booming now, with proper logistics and statistics.
Slide 19 : This slide goes on to exhibit the top 10 AI trends in Slide 20 : This slide mentions the burning questions related to Machine Learning like What is ML, 7 steps of machine learning, machine learning vs traditional programming, How does machine learning work, machine learning algorithms, machine learning usecases, how to choose ML algorithm, why to use decision tree algorithm learning, challenges and limitations of machine learning, applications of machine learning, and Why is machine learning important?
Slide 21 : The following slide is designed to display the working mechanism of Machine Learning and its input as well as output data. Slide 22 : This next slide defines the key seven Steps of Machine Learning that are gathering data, choosing a model, preparing the data, evaluation, prediction, hyperparameter tuning, training.
Slide 23 : This slide draws a comparison between machine learning and traditional programming. Slide 24 : The following slide describes how Machine Learning Work includes – defining Objectives, preparing data, train Model, integrate Model, Collecting data, Selecting algorithm, and test Model. Slide 25 : This slide visually represents the Machine Learning Algorithms including supervised, unsupervised, and reinforcement in an organized format. Slide 27 : This slide educates you on How to Choose a Machine Learning Algorithm, algorithm cheat sheet, and additional requirements like accuracy, training time, linearity, parameters, and the number of features.
Slide 28 : This slide goes on to mention the reasons for using Decision Tree Machine Learning Algorithm like to classify or to predict, and further their uses. Slide 29 : This slide highlights to Challenges and Limitations of Machine learning.
Slide 30 : This slide showcases the essential components in the Application of Machine Learning like Automatic Language Translation, Medical Diagnosis, Stock market trading, online fraud detection, Virtual Personal Assistant, email spam and malware fittering, self driving cars.
Slide 31 : The purpose of this slide is to explain the importance of Machine Learning along with the key phases of learning and prediction. Slide 32 : This slide is curated to address all the critical questions with regards to the concept of Deep Learning like what is deep learning, deep learning process, classification of neural networks, types of deep learning networks, feed-forward neural networks, recurrent neural networks, convolutional neural networks, reinforcement learning, examples of deep learning applications, why is deep learning important, and limitations of deep learning.
Slide 34 : This slide gives you a glimpse of the complex Deep Learning Process which includes understanding the problem, identifying data, selecting deep learning algorithms, training the model, and testing the model. Slide 37 : This slide elaborates on the Feed-forward Neural Networks and their input layer, hidden layer, and output layer.
Slide 38 : This slide elucidates the Recurrent Neural Networks thoroughly. Slide 39 : This slide gives a detailed explanation of the Convolutional Neural Networks.
Slide 40 : This slide explains how Reinforcement Learning goes on to maximize the rewards. Slide 46 : This slide provides you detailed information about Artificial Intelligence.
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Machine Learning Structure. Outline 3. History of ML 4. Why ML 8. Training set observed Universal set unobserve d Testing set unobserved Data acquisition Practical usage Algorithm Supervised learning Unsupervised learning Semi-supervised learning Unsupervised learning Contd.
Application Furthermore, there are more and more techniques apply machine learning as a solution. In the future, machine learning will play an important role in our daily life. Conclusion Slide 8 : The following slides provide you detailed information about Artificial Intelligence and the various elements associated with it. Slide 9 : This slide titled – Machine Learning, provides you with deep insights into the concept of AI and its types.
Slide 10 : The following slide showcases the information on Deep Learning and its key functions known as artificial neural networks. Slide 14 : This slide focuses on the uses of Artificial Intelligence in the Human Resource department that includes learning, Selection, Recruitment, engagement, and onboarding. Slide 16 : The following slide illustrates the role of AI in the Supply Chain that includes logistics, procurement, manufacturing, customers, and service.
Slide 17 : This slide introduces you to all the AI Chatbots in Healthcare such as search engines, social platforms, smartphones, health bots, artificial intelligence, messenger apps, and the app ecosystem. Slide 18 : This slide discusses the reason for Why AI is booming now, with proper logistics and statistics.
Slide 19 : This slide goes on to exhibit the top 10 AI trends in Slide 20 : This slide mentions the burning questions related to Machine Learning like What is ML, 7 steps of machine learning, machine learning vs traditional programming, How does machine learning work, machine learning algorithms, machine learning usecases, how to choose ML algorithm, why to use decision tree algorithm learning, challenges and limitations of machine learning, applications of machine learning, and Why is machine learning important?
Slide 21 : The following slide is designed to display the working mechanism of Machine Learning and its input as well as output data. Slide 22 : This next slide defines the key seven Steps of Machine Learning that are gathering data, choosing a model, preparing the data, evaluation, prediction, hyperparameter tuning, training.
Slide 23 : This slide draws a comparison between machine learning and traditional programming. Slide 24 : The following slide describes how Machine Learning Work includes – defining Objectives, preparing data, train Model, integrate Model, Collecting data, Selecting algorithm, and test Model.
Slide 25 : This slide visually represents the Machine Learning Algorithms including supervised, unsupervised, and reinforcement in an organized format. Slide 27 : This slide educates you on How to Choose a Machine Learning Algorithm, algorithm cheat sheet, and additional requirements like accuracy, training time, linearity, parameters, and the number of features. Slide 28 : This slide goes on to mention the reasons for using Decision Tree Machine Learning Algorithm like to classify or to predict, and further their uses.
Slide 29 : This slide highlights to Challenges and Limitations of Machine learning. Slide 30 : This slide showcases the essential components in the Application of Machine Learning like Automatic Language Translation, Medical Diagnosis, Stock market trading, online fraud detection, Virtual Personal Assistant, email spam and malware fittering, self driving cars. Slide 31 : The purpose of this slide is to explain the importance of Machine Learning along with the key phases of learning and prediction.
Slide 32 : This slide is curated to address all the critical questions with regards to the concept of Deep Learning like what is deep learning, deep learning process, classification of neural networks, types of deep learning networks, feed-forward neural networks, recurrent neural networks, convolutional neural networks, reinforcement learning, examples of deep learning applications, why is deep learning important, and limitations of deep learning.
Slide 34 : This slide gives you a glimpse of the complex Deep Learning Process which includes understanding the problem, identifying data, selecting deep learning algorithms, training the model, and testing the model. Slide 37 : This slide elaborates on the Feed-forward Neural Networks and their input layer, hidden layer, and output layer. Slide 38 : This slide elucidates the Recurrent Neural Networks thoroughly. Slide 39 : This slide gives a detailed explanation of the Convolutional Neural Networks.
Slide 40 : This slide explains how Reinforcement Learning goes on to maximize the rewards. Slide 46 : This slide provides you detailed information about Artificial Intelligence. Slide 47 : The current slide gives you an introduction to the Machine Learning and how it learns, predicts, and improves the ordinary system. Slide 48 : This slide will take you through the concept of Deep Learning in detail.
Slide 51 : The purpose of this slide is to highlight the difference between Machine Learning and Deep Learning. Slide 53 : This slide titled Supervised Machine Learning focuses on explaining the concept by addressing questions like types of machine learning, what is supervised machine learning, gow supervised learning works, types of supervised machine learning algorithms, supervised vs unsupervised learning techniques, advantages of supervised learning, and disadvantages of supervised learning.
Slide 54 : The following slide provides you with various types of Machine Learning like supervised learning, unsupervised learning, and reinforcement learning. Slide 56 : This slide mentions the mechanism of How Supervised Machine Learning works and all the steps it entails like classification and regression.
Slide 57 : This slide brings various Types of Supervised Machine Learning Algorithms to the fore like classification that includes fraud detection, email spam detection, diagnostics, and image classification. Also, regression, that includes risk assessment and score prediction. Slide 58 : This slide calls attention to Supervised classification, regression vs.
Unsupervised Machine Learning Techniques clustering, association. Slide 59 : This slide emphasizes the Advantages of Supervised Learning.
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