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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

Mathematics as a Monarch

H
Harsha Vardhan Last Updated : 26 Aug, 2021
5 min read
This article was published as a part of the Data Science Blogathon.

 

Introduction

Mathematics is a very crucial part of our day-to-day lives. From the day we born to death, we use Mathematics knowingly or unknowingly in various types of applications we use and in many scenarios. In childhood, we begin our learning Numbers, tables, equations, etc., to solving complex equations and generating new formulas, and inventing new techniques & tricks in solving a particular mathematical problem.

Mathematics data science

Image Source: towardsdatascience.com

Mathematics has gained a greater significance in the field of the latest technologies like Machine Learning, Artificial Intelligence, Data Science, Deep Learning, and many more technologies. Every new technology in today’s world is directly or indirectly related to Mathematics, to develop smart & simple solutions for the problems. At present, Mathematics is ruling the world like a monarch through its applications in diversified fields. Mathematics is helping in many industries like software, medical, automobile, designing, robotic industry, and in many more workspaces with its different math functions, new techniques, theorems, algorithms, etc.,

How is math related to Latest Technologies………??

We have discussed that mathematics is playing an essential role in the latest technologies like Machine Learning, Artificial Intelligence, Data Science and Deep Learning, etc., It is because every algorithm built in the latest technologies has a mathematical function behind it. Mathematics helps to build logic behind every problem that we encounter in our day-to-day applications.

We might have a small doubt in our minds, how the mathematics has gained such a huge and utmost importance in the latest technologies………??

Mathematics data science pie chart

Image Source: towardsdatascience.com

The main reason for a greater significance of mathematics is because of its various concepts like: –

· Linear Algebra

· Probability

· Calculus

· Statistics

Those are the 4 main concepts used in developing any type of new technology or solving any complex problem or discovering a new algorithm. Moreover, we can say these concepts are the foundation pillars for the enhancement of any latest technology or any new application in the real world.

 

Linear Algebra

It has a faster computation speed in dealing with any type of problem. It helps to understand the insights and background behind the various types of algorithms used in the latest technologies. It helps us in decision making and mostly deals with matrices and vectors and be accessed with a Numpy Library in python language.

Mathematics Data Science - Scalar

Image Source: towardsdatascience.com

· A scalar is a single number.

· A vector is an array of numbers represented either in a row or a column.

· A matrix is a 2D array, represented in rows and columns.

· A tensor is an N-dimensional array, with more n value more than 2.

 

Probability

It deals with the likelihood of an event in a sample space and the certainty of that event. This approach is mainly used in the latest technologies, to produce better results for future prediction. It helps in the decision-making aspect also, to bring out reliable results of a particular problem. In general terms, the probability is calculated as

P(E) = No of Favorable Outcomes / Total No of Possible Outcomes

Image Source: towardsdatascience.com

 

In the terms of probability, P(E) is called the probability of the event, and E is called the Event. It tells about an event that may occur or may not occur in a sample space. The probability always lies in the range of 0 and 1 for any application.

Most of the real-world applications use the probability in decision man making aspect and finding solutions for the complex problems that we encounter while developing a new algorithm or application. Probability helps us in likely outcomes of the future and mainly used in stock markets and in various industries.

 

Calculus

A branch of mathematics deals with the study of continuous changes and optimizing the results at the end. Without having good knowledge in the field of calculus it is difficult to compute the probabilities and we cannot generate better outcomes to the problems. It is focused on derivatives, integrals, and limits.

calculus

Image Source: towardsdatascience.com

Calculus is aimed at optimizing the algorithms of the latest technologies. Helps us to build efficient solutions to the problems accurately. It is mainly of two kinds namely Differential Calculus and Inferential Calculus.

 

Statistics

It helps us to draw insights from the data. It deals with the collection, presentation, analysis, and Interpretation of Numerical data. Statistics plays an important role in the field of the latest technologies to work on more complex data and acts as the major key factor behind the development and growth of an organization.

It is divided mainly dived into two types namely Descriptive Statistics and Inferential Statistics. The frequently used concept in the field of mathematics and has a greater significance in developing a new type of applications and algorithms that leads to enhancement of new technologies. Statistics help us to bring a summary picture of the process flow in an industry or in a workspace.

Image Source: kdnuggets.com

Resources Available

a) Khan Academy

b) medium

c) towardsdatascience

d) coursera

e) kdNuggets

 

Youtube Channels

a) siraj Raval

b) 3Blue1Brown

c) sentdex

 

Conclusion

There are many jobs by just knowing the concepts of Mathematics and some concepts in the field of latest technologies will have a better pay to a person. The jobs in this domain are Data Scientists, Data Analysts, etc., One needs to have a better understanding and skill over the area of Mathematics to draw better insights into a problem and find a suitable solution to it.

Author Section

Author: – Harsha Vardhan Garlapati

I am Machine Learning Engineer Enthusiast and loves working with data. I am a final-year undergraduate student and passionate about Machine Learning. I am a smart worker, a passionate learner, an Icebreaker, and loves to participate in Hackathons to work on real-time projects. I am a Toastmaster Member at S.R.K.R Toastmasters Club, a Public Speaker, a good Innovator, and a problem solver.

LinkedIn: – https://www.linkedin.com/in/ghvardhan569/

GitHub: – https://github.com/ghvardhan569

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

 

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Harsha Vardhan

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