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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

How AI can Improve your Life in the Next 5 Years?

chandana
chandana Last Updated : 20 Oct, 2022
5 min read

This article was published as a part of the Data Science Blogathon.

Introduction

Artificial intelligence is one of the most talked-about technologies in recent years. It has been a hot topic for many tech companies, investors, and entrepreneurs who are trying to find ways to use it to improve their businesses or lives. However, not everyone knows about the possibilities that AI can offer us yet. This article will discuss how artificial intelligence can improve your life in the next five years by automating your daily activities using different types of technology like smart home automation, television automation, etc.

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AI in Our Everyday Activities

AI can help us with our daily tasks. If you are busy, you can use AI to do some of your work. For example, suppose I am going to cook dinner, and I want my oven to preheat to a specific temperature. In that case, my oven will automatically set itself to the right temperature without me having to do anything. The same goes for many other things like lighting switches or even opening doors—it just takes some programming knowledge which is very easy these days! You don’t need special skills or knowledge now, so why not try?

This might seem like an obvious benefit if we talk about productivity. Still, until recently, automation was only available through human interaction, which meant that most tasks could only be completed by one employee at once (or maybe two). Nowadays, however, there are several different ways in which robots can help us become more effective and efficient; such as giving advice on what action should be taken next based on previous experience or past data collected from similar situations beforehand so that those involved won’t waste time repeating themselves unnecessarily again later downstream.”

Home Automation

The most obvious use of smart home technology is in home automation, which can control lighting and temperature settings. Smart thermostats can be programmed to adjust the temperature automatically based on a schedule or set point you create for your space. You can also set up automated lighting systems that will turn on at sunset and off at sunrise, so you don’t have to remember when it’s time for bed!

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Smart doorbells like Ring or Nest Hello allow you to see who’s at your door without having them ring the bell themselves—and if there’s no one there when someone presses their button, these devices will send an alert straight through their phone (or tablet) notification center. Hence, they know what happens when they’re not looking directly at their phone screen!

Television Automation

AI can help you find the right show for you.

Today’s AI is sophisticated enough to understand your preferences and recommend shows based on those preferences. For example, if you like comedy shows, you will find comedy shows for you. If you prefer action movies, you will find action movies. If your favorite genre is sci-fi TV shows or dramas with lots of twists and turns… the possibilities are endless! Likewise with sports: AI can help users discover which sports games are playing during their time zone, so they don’t miss anything important.

Automation in Healthcare

AI has the ability to improve how we deliver healthcare by increasing efficiency in diagnosis, treatment, and prevention. It’s also improving the quality of medical care by helping us better understand our patient’s needs based on their data—from their health history to genetic tests and lifestyle factors.

In addition to these benefits, AI may also reduce costs through increased productivity and automation of tasks that were previously done manually (such as ordering supplies).

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Financial Automation

AI can help you make better financial decisions. Artificial intelligence is already helping people with their money management and investing, but it’s not just about saving money; it’s also about getting a loan from your bank, paying bills, and even investing in the stock market. AI will be able to analyze how much you spend each month on certain activities or items, such as groceries or gas (e.g., “If I spent $100 on groceries today, then I should have enough left over for my car payment”).

AI also learns from past experiences so that it can predict what might happen next—like whether your credit card company will raise interest rates next month based on its history of similar increases in other customers’ accounts (e.g., “I’ve always paid my monthly bill on time…so I’m fairly confident they won’t raise my rate”).

Automation in Travel and Logistics

AI can help with navigation. The most common example is GPS, but other technologies have been developed to help us navigate our surroundings. For example, Google Maps uses AI to give directions based on what it knows about your location and how far you’re going (if you have never visited a specific city before). This allows users who don’t know much about their destination city or region to find an optimal route while still getting there safely.

Another great feature of using AI technology in travel is booking flights and hotels online. Many apps available do this automatically without requiring any human interaction whatsoever! If this sounds appealing but not necessary right now, consider applying some automation anyway; it’ll save time later if something goes wrong during your trip or after returning home.”

Entertainment Automation

Artificial intelligence can create new movies, games, and music. It can also be used to create virtual reality games, which have recently gained popularity.

As you might have guessed by now, AI is not just about technology—it’s about using data and algorithms to solve problems that humans could never solve on their own. So why should we care? Well…

Ease in Daily Life

AI can make our daily life easier, more efficient, and more pleasant.

AI can automate many of our daily activities and make them much more convenient. For example, we can book a flight instantly by using the same algorithm that makes airline reservation systems work so well today (and this technology will likely evolve even further in the next 5 years). In addition to automating routine tasks like booking flights or paying bills online, AI also allows us to find new ways of solving old problems – for example, by helping doctors diagnose diseases or suggesting treatments based on patient history rather than relying solely on their symptoms alone.

AI has already revolutionized many aspects of business management, including accounting, sales, and marketing; however, it’s only recently become possible due to advances in deep learning algorithms, which allow machines not only to learn but also improve over time according to one’s needs/wants/desires, etc…

Conclusion

Hopefully, this article has given you some ideas about how artificial intelligence can improve your life. However, I believe the best AI features are yet to come. These features will probably make the future sound very different from what it is today, which is why I hope you’ll stay updated on recent developments in this field. The future of AI looks a lot brighter than we might expect it to be. Fortunately, reality far exceeds our imagination, which makes us look forward to that amazing future when people’s lives change for the better due to artificial intelligence.

Here we have a few takeaways from AI:

  • AI has the ability to improve how we deliver healthcare by increasing efficiency in diagnosis, treatment, and prevention. It’s also improving the quality of medical care by helping us better understand our patient’s needs based on their data—from their health history to genetic tests and lifestyle factors.
  • Artificial intelligence can help you find the best advisor for you, and it will empower us to make better financial decisions, increasing our wealth.
  • The ultimate goal of AI is to make our lives easier by automating many of the tasks we do every day.

The media shown in this article is not owned by Analytics Vidhya and is used at the Author’s discretion.

chandana
chandana

An innovative and dedicated professional with a strong foundation in artificial intelligence (AI), machine learning (ML), deep learning (DL), and Python programming, currently working at Infosys. I am passionate about leveraging cutting-edge technologies to create transformative solutions that drive business growth and streamline operations.

Throughout my time at Infosys, I have honed my skills by collaborating on diverse projects, ranging from predictive analytics to natural language processing. My intermediate experience in AI, Machine Learning, and Deep Learning has enabled me to contribute effectively to the design, development, and implementation of advanced algorithms and models for various industry applications.

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Responses From Readers

Clear

Abdul Razzaq Ali
Abdul Razzaq Ali

Don't know about its improvement but it will lead to unemployment 😢

123

Surya chandana
Surya chandana

According to the World Economic Forum’s “The Future of Jobs Report 2020”, AI is expected to replace 85 million jobs worldwide by 2025. Though that sounds scary, the report goes on to say that it will also create 97 million new jobs in that same time frame.

123

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