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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

Artificial Intelligence – Threat to the World

Anant
Anant Last Updated : 30 Mar, 2021
4 min read
This article was published as a part of the Data Science Blogathon.

” We create our own demons “

The destructions caused by Natural Disasters, Harmful effects from rising in earth’s temperature and Depletion of the Ozone layer. Everyone is concerned about it nowadays. Some might be contributing to help in the cause while some are still not awakening. But have you ever thought is it worth saving the world by supporting these causes while we are trying to build another threat for us in the form of AI?

Let’s think about the advancements that AIML can bring to us. It can help us by providing so much in the field of Healthcare, Army, Logistics, Automation Industry, Interact with Nuclear radiations, Providing Automated Analytics reports, and many other marvelous applications. But, at what cost. Did the people think while they were extracting oil, natural gas, and other natural resources about the harmful results of their applications? Nobody thought of it because we were all interested in the immediate results which landed us to the part where moving forward is another destructing step and taking a step back is landing mankind to the reverse evolution.

“Artificial Intelligence is the new Electricity”

Mankind is vulnerable to threats from even the very small irregularities like a virus. We might be very excited about the wonderful results of AI. But, we should also prepare for the threats it will bring to the world. The damage it is causing and the damage it has already caused.

Let’s take a very simple scenario :

Y=mX+c

In the above equation, we are all aware Y is a linearly dependent variable on the term X. But, let us think of m and c. In the equation, if m and c are dependent on the time then this equation after every moment will have a different form. Those forms sometimes can be so random that we might be aware of the process going on but we won’t be able to get those results that our ML model can have. In this scenario, the main point to understand is that even a small modification in an approach can take us beyond the scope of human predictions. Machine Learning is a science-based on Mathematical approach which will someday be hard to understand and conquer.

 

Challenges in AI

AI along with the applications bring few unanswered questions :

 Job Automation 

 With the introduction of AI-based bots the very fundamental question raised was the void it will create in the job sector. With advancements in the monitoring systems in the industries, we are creating a large gap in the job sector by replacing the people who work for specific jobs. For example – Most of the companies are trying to build automated ticket resolution which might be very effective but creating a risk for the job of an individual. The important question is – Is AIML sufficient to provide enough opportunities to accommodate everyone?

 Data Privacy and Safety 

 The very fundamental aspect of a successful ML model is to have enormous useful data. But, providing all the information to the solution brings another concern which is how much our data is secure and safe. Is it vulnerable to hacker attacks or Is it sufficient to safeguard the personal information of people? The safety concerns are not just limited to data privacy with advancement in automobiles the most important question is  – Who is responsible for damage caused by the driverless cars due to the complex real-life situation?  

 Biased Decisions

The AI-based system cannot understand emotions, so when it comes to using your heart along with the brain the system might not be considered very successful. Along with the lack of emotions, there is a various factor which might result in not so efficient decisions like a system biased towards masculine factor will eliminate the success that females can bring in that sector. Due to the various minorities present our system might ignore the importance of those minorities resulting in a biased system. The biggest question it raises is – Who is capable and Who is not can a system decide effectively?

Wars and Destructions

With AI’s most Advanced Application there is a very important threat associated with it as well. AI-based systems might provide the most advanced self capable weapons. But as we are divided with the borders across the world these weapons give an upper hand to few developed Nations which can run the more advanced weapon system compared to others. This not only brings the advantage to few privileged ones but also brings the possibility of World War and other threats of causing mankind destructions. Also with the enormous spread of Nuclear Power in the world, it arises a most important question – Can we trust a machine to handle the enormous Nuclear Power?

” The powerful people are not the ones who have enormous powers, but they are the ones who know how to control it ” 

AI Threat Image

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

Anant
Anant

I am a Data Science expert with excellent understanding of NLP and its applications. I work in Finance domain supporting Data Science use cases and mentor people who are interested in Data Science and Analytics

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CLIFFORD STANFIELD
CLIFFORD STANFIELD

Since a business is driven for profit, not to provide employment, and employee costs, like any other factor of production, should be minimized, the paradigm has changed. Rather than ask "How can machines, tools, and AIML improve employee productivity?", now the question is "How can machines, tools, and employees improve AIML productivity?".

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