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Reading list

Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

7 Tools to Create Engaging Presentations

D
Deepsandhya Shukla Last Updated : 28 Feb, 2024
4 min read

Introduction

Visual presentations are indispensable for effective communication. Whether you’re a student, professional, or entrepreneur, mastering the art of crafting engaging and informative presentations is vital. This article will explore seven tools to help you create stunning presentations that will captivate your audience and effectively convey your message.

Read on!

7 Tools to Create Engaging Presentations

Table of contents

Importance of Visual Presentations

Visual presentations are powerful tools for conveying information in a way that is easy to understand and remember. You can make your content more engaging and memorable by incorporating images, charts, graphs, and other visual elements into your presentations. Research has shown that people are more likely to retain information when presented visually, making visual presentations an effective way to communicate complex ideas and data.

Benefits of Using Presentation Tools

Using presentation tools can help you create professional-looking presentations quickly and easily. These tools often come with pre-designed templates, graphics, and animations that can help you create visually appealing slides with minimal effort. Additionally, it usually offers features such as collaboration, cloud storage, and analytics, making working on presentations with a team and tracking their performance easier.

7 Tools to Create Professional Presentations

Here are 7 presentation tools that you must know before pitching a client:

Beautiful.ai: Designing Beautiful Slides

Beautiful.ai: Designing Beautiful Slides

Beautiful.ai is a presentation tool that uses artificial intelligence to help you design beautiful slides quickly and easily. The tool offers a variety of pre-designed templates that you can customize to fit your brand and style. With Beautiful.ai, you can create professional-looking presentations in minutes, saving time and effort.

Prezi: Creating Dynamic Presentations

Prezi: Creating Dynamic Presentations

Prezi allows you to create dynamic and interactive presentations that captivate your audience. Unlike traditional slide-based presentations, Prezi uses a zooming interface that allows you to create a visual journey for your audience. With Prezi, you can create engaging presentations that tell a story and keep your audience engaged from start to finish.

Visme: Infographics and Interactive Content

Visme: Infographics and Interactive Content

Visme is a versatile tool that allows you to create infographics, interactive content, and presentations all in one platform. With Visme, you can easily create visually stunning slides, infographics, and interactive content that will impress your audience. The tool offers a wide range of templates, graphics, and animations that you can use to create engaging presentations that stand out.

Haiku Deck: Simplifying Presentation Creation

Haiku Deck: Simplifying Presentation Creation

Haiku Deck simplifies creating beautiful and engaging presentations. The tool offers a variety of pre-designed templates and layouts that you can use to create professional-looking slides easily. With Haiku Deck, you can focus on creating compelling content without worrying about design, making it a great tool for busy professionals and students.

SlidesAi.io

SlidesAi.io

SlidesAi.io uses artificial intelligence to help you create visually appealing slides quickly and easily. The tool offers a variety of design options, animations, and transitions that you can use to create dynamic presentations that will impress your audience. With SlidesAi.io, you can create professional-looking slides in minutes, making it a great tool for anyone looking to create stunning presentations with minimal effort.

Powtoon: Animated Presentations Made Easy

Powtoon: Animated Presentations Made Easy

Powtoon allows you to create animated presentations that will grab your audience’s attention. The tool offers a variety of pre-designed templates, characters, and animations that you can use to create engaging and dynamic presentations. With Powtoon, you can easily add animations, transitions, and effects to your slides, making your presentations come to life.

Also read: You can now Create Stunning Presentations using Tome AI

Emaze: 3D Presentations for Impact

Emaze: 3D Presentations for Impact

Emaze is a tool that allows you to create 3D presentations that will leave a lasting impression on your audience. The tool offers a variety of 3D templates, animations, and effects that you can use to create visually stunning presentations. With Emaze, you can create immersive presentations to captivate your audience and make your message stand out.

Also read: PowerPoint Presentations Made East With Gamma

Conclusion

In conclusion, visual presentations are a powerful tool for communicating ideas and data engagingly and memorably. Using tools such as Beautiful.ai, Prezi, Visme, Haiku Deck, SlidesAi.io, Powtoon, and Emaze, you can create stunning presentations that captivate your audience and effectively convey your message. Whether you are a student, a professional, or an entrepreneur, these tools can help you create professional-looking presentations quickly and easily. So why wait? Start creating stunning presentations today with these amazing tools!

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D
Deepsandhya Shukla

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