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Fake News Detection Through Machine Learning ML CD

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Content of this Powerpoint Presentation

Slide 1: This slide introduces FAKE NEWS Detection Through Machine Learning. State your company name and begin.
Slide 2: This is an Agenda slide. State your agendas here.
Slide 3: This slide shows Table of Content for the presentation.
Slide 4: This slide shows title for topics that are to be covered next in the template.
Slide 5: This slide highlights various statistics related to fake news. It shows figures related to internet news, fake news economic impact, social media fake news etc.
Slide 6: This slide showcases impact of fake news in different sectors such as stock market, reputation management, political spending, brand safety etc.
Slide 7: This slides presents percentage of fake news shared on different media platforms such as Twitter, Instagram, Facebook, Traditional media, WhatsApp etc.
Slide 8: This slide shows title for topics that are to be covered next in the template.
Slide 9: This slides showcases machine learning technology overview that can help to determine hidden patterns in datasets. It also highlights need of machine learning in different areas.
Slide 10: This slide presents various types of machine learning algorithms the can help to analyze and generate insights from the data. Its key elements are supervised learning, clustering etc.
Slide 11: This slide showcases steps for machine learning based fake detection that are data collection, data preprocessing, feature extraction, model training, hyperparameter tuning etc.
Slide 12: This slide shows title for topics that are to be covered next in the template.
Slide 13: This slides showcases data collection that can help to train and deploy machine learning mode. It also shows process of data collection in machine learning.
Slide 14: This slide displays latest trends used for collecting data. Various trends mentioned are synthetic data generation, active learning and open source datasets.
Slide 15: This slide showcases comparison of data that can help in fake news detection. Its key elements are news domain, content type size, media platform etc.
Slide 16: This slide presents comparison of multimodal data that can help in fake news detection. Its key elements are data type, modality, context, topics diversity etc.
Slide 17: This slide shows title for topics that are to be covered next in the template.
Slide 18: This slide showcases data preprocessing that can help to convert raw into usable data. It also highlights various needs of data preprocessing such as improving data quality and more.
Slide 19: This slide presents process that can help in data preprocessing. Various steps involved are raw data collected, remove numerical figures, eliminate punctuations, lowercasing etc.
Slide 20: This slides showcases data preprocessing techniques that can help in fake news detection. Its key elements are preprocessing tasks, techniques used and results.
Slide 21: This slide shows title for topics that are to be covered next in the template.
Slide 22: This slide showcase features extraction that can help in fake news detection. It also highlight need of feature extraction such as eliminate redundant data, improve model accuracy etc.
Slide 23: This slide displays various techniques for feature extraction such as autoencoders, principal component analysis, bag of words, term frequency-inverse document frequency etc.
Slide 24: This slide presents features that can be extracted for fake news detection in machine learning. Key features are numerical. Categorical, ordinal and binary.
Slide 25: This slide showcases extraction of features from different news articles for machine learning model deployment. Its key elements are feature name and data type
Slide 26: This slide shows title for topics that are to be covered next in the template.
Slide 27: This slide showcases overview of decision tree that is used for regression and classification tasks. Key elements of decision tree are root node, decision node and leaf node.
Slide 28: This slide presents decision tree that can help in fake news detection by dividing dataset into smaller groups. It can help to classify news into real and fake.
Slide 29: This slide shows title for topics that are to be covered next in the template.
Slide 30: This slide showcases overview of logistic regression that analyze relation between different variables. It also shows different types of logistic regression.
Slide 31: This slide presents logistic regression model that can help in fake news detection. It also highlights various steps such as data collection, cleaning, feature extraction and train model.
Slide 32: This slide shows title for topics that are to be covered next in the template.
Slide 33: This slide showcases overview of random forest that compile output of multiple decision tress for reaching output.
Slide 34: This slide presents usage of random forest for detecting fake news. Various steps are features extraction, splinter point calculation, node splitting etc.
Slide 35: This slide shows title for topics that are to be covered next in the template.
Slide 36: This slide covers various elements of model training for fake news detection such as feeding engineered data, parametrized ML algorithm, model with optimal trained parameters etc.
Slide 37: This slide showcases various best practices such as small datasets, correctly labeled datasets etc. that can enhance the machine learning model training.
Slide 38: This slide shows title for topics that are to be covered next in the template.
Slide 39: This slide showcases overview of hypermeter tuning for optimizing machine learning model performance. It also highlights various benefits of hypermeter tuning.
Slide 40: This slide presents various methods such as grid search, random search, Bayesian optimization and hyperband that can be used for fake news detection
Slide 41: This slide shows title for topics that are to be covered next in the template.
Slide 42: This slide showcases process for machine learning model deployment. Key steps are prepare mode. Design API and deploy model, monitor performance etc.
Slide 43: This slide showcases solutions that can help to tackle various challenges such as scalability, security, infrastructure compatibility during model deployment.
Slide 44: This slide shows title for topics that are to be covered next in the template.
Slide 45: This slide showcases metrics that can help to evaluate the performance of machine learning classifiers. Various KPIs are accuracy, precision, recall and F1 score
Slide 46: This slide presents confusion matrix that can help to analyze the machine learning classifier model performance and optimize accordingly.
Slide 47: This slide shows all the icons included in the presentation.
Slide 48: This slide is titled as Additional Slides for moving forward.
Slide 49: This is Our Vision, Mission & Goal slide. Post your Visions, Missions, and Goals here.
Slide 50: This slide presents Bar Graph with two products comparison.
Slide 51: This is Our Team slide with names and designation.
Slide 52: This is Our Target slide. State your targets here.
Slide 53: This slide shows Post It Notes for reminders and deadlines. Post your important notes here.
Slide 54: This slide depicts Venn diagram with text boxes.
Slide 55: This is a Thank You slide with address, contact numbers and email address.

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