When I was beginning my way in data science, I **often faced the problem **of **choosing the most appropriate algorithm** for my specific problem. If you’re like me, when you open some article about machine learning algorithms, you see dozens of detailed descriptions. The **paradox** is that they **don’t ease the choice.**

Well, to not let you feel out of the track, I would suggest you to have a good understanding of the implementation and mathematical intuition behind several supervised and unsupervised Machine Learning Algorithms like -

**Linear regression****Logistic regression****Decision tree****Naive Bayes****Support vector machine****Random forest****AdaBoost****Gradient-boosting trees****Simple neural network****Hierarchical clustering****Gaussian mixture model****Convolutional neural network****Recurrent neural network****Recommender system**

Remember, the list of Machine Learning Algorithms I mentioned are the ones that are mandatory to have a good knowledge of , while you are a beginner in Machine/Deep Learning !

Now that we have some intuition about types of machine learning tasks, let’s explore the most popular algorithms with their applications in real life, based on their problem statements !

Try to work on each of these problem statements after getting to the end of this blog ! I can assure you would learn a lot, a hell lot!

## Problem Statement 1 -

**To Predict the Housing Prices**

Machine Learning Algorithm(s) to solve the problem **—**

**Advanced regression techniques like random forest and gradient boosting**

## Problem Statement 2 -

**Explore customer demographic data to identify patterns**

Machine Learning Algorithm(s) to solve the problem **—**

**Clustering (elbow method)**

## Problem Statement 3 -

**Predicitng Loan Repayment**

Machine Learning Algorithm(s) to solve the problem **—**

**Classification Algorithms for imbalanced dataset**

## Problem Statement 4 -

**Predict if a skin lesion is benign or malignant based on its characteristics (size, shape, color, etc)**

Machine Learning Algorithm(s) to solve the problem **—**

**Convolutional Neural Network ( U-Net being the best for segmentation stuffs)**

## Problem Statement 5 -

**Predict client churn**

Machine Learning Algorithm(s) to solve the problem **—**

**Linear discriminant analysis**(LDA) or**Quadratic discriminant analysis**(QDA)

( particularly popular because it is both a classifier and a dimensionality reduction technique)

## Problem Statement 6 -

**Provide a decision framework for hiring new employees**

Machine Learning Algorithm(s) to solve the problem **—**

**Decision Tree**is a pro gamer here

## Problem Statement 7 -

**Understand and predict product attributes that make a product most likely to be purchased**

Machine Learning Algorithm(s) to solve the problem **—**

**Logistic Regression****Decision Tree**

## Problem Statement 8 -

**Analyze sentiment to assess product perception in the market.**

Machine Learning Algorithm(s) to solve the problem **—**

**Naive Bayes**—**Support Vector Machines**(NBSVM)

## Problem Statement 9 -

**Create classification system to filter out spam emails**

Machine Learning Algorithm(s) to solve the problem **—**

**Classification Algorithms —**

**Naive Bayes, SVM , Multilayer Perceptron Neural Networks (MLPNNs)** and **Radial Base Function Neural Networks (RBFNN) suggested.**

## Problem Statement 10 -

**Predict how likely someone is to click on an online ad**

Machine Learning Algorithm(s) to solve the problem **—**

**Logistic Regression****Support Vector Machines**

## Problem Statement 11 -

**Detect fraudulent activity in credit-card transactions.**

Machine Learning Algorithm(s) to solve the problem **—**

**Adaboost****Isolation Forest****Random Forest**

## Problem Statement 12 -

**Predict the price of cars based on their characteristics**

Machine Learning Algorithm(s) to solve the problem **—**

**Gradient-boosting trees are best at this.**

## Problem Statement 13 -

**Predict the probability that a patient joins a healthcare program**

Machine Learning Algorithm(s) to solve the problem **—**

**Simple neural networks**

## Problem Statement 14 -

**Predict whether registered users will be willing or not to pay a particular price for a product.**

Machine Learning Algorithm(s) to solve the problem **—**

**Neural Networks**

## Problem Statement 15 -

**Segment customers into groups by distinct charateristics (eg, age group)**

Machine Learning Algorithm(s) to solve the problem **—**

**K-means clustering**

## Problem Statement 16 -

**Feature extraction from speech data for use in speech recognition systems**

Machine Learning Algorithm(s) to solve the problem **—**

**Gaussian mixture model**

## Problem Statement 17 -

**Object tracking of multiple objects, where the number of mixture components and their means predict object locations at each frame in a video sequence.**

Machine Learning Algorithm(s) to solve the problem **—**

**Gaussian mixture model**

## Problem Statement 18 -

**Organizing the genes and samples from a set of microarray experiments so as to reveal biologically interesting patterns.**

Machine Learning Algorithm(s) to solve the problem **—**

**Hierarchical clustering algorithms**

## Problem Statement 19 -

**Recommend what movies consumers should view based on preferences of other customers with similar attributes.**

Machine Learning Algorithm(s) to solve the problem **—**

**Recommender system**

## Problem Statement 20 -

**Recommend news articles a reader might want to read based on the article she or he is reading.**

Machine Learning Algorithm(s) to solve the problem **—**

**Recommender system**

## Problem Statement 21 -

**Recommend news articles a reader might want to read based on the article she or he is reading.**

Machine Learning Algorithm(s) to solve the problem **—**

**Recommender system**

## Problem Statement 22 -

**Optimize the driving behavior of self-driving cars**

Machine Learning Algorithm(s) to solve the problem **—**

**Reinforcement Learning**

## Problem Statement 23 -

**Diagnose health diseases from medical scans.**

Machine Learning Algorithm(s) to solve the problem **—**

**Convolutional Neural Networks**

## Problem Statement 24 -

**Balance the load of electricity grids in varying demand cycles**

Machine Learning Algorithm(s) to solve the problem **—**

**Reinforcement Learning**

## Problem Statement 25 -

**When you are working with time-series data or sequences (eg, audio recordings or text)**

Machine Learning Algorithm(s) to solve the problem **—**

**Recurrent neural network**- LSTM

## Problem Statement 26 -

**Provide language translation**

Machine Learning Algorithm(s) to solve the problem **—**

**Recurrent neural network**

## Problem Statement 27 -

**Generate captions for images**

Machine Learning Algorithm(s) to solve the problem **—**

**Recurrent neural network**

## Problem Statement 28 -

**Power chatbots that can address more nuanced customer needs and inquiries**

Machine Learning Algorithm(s) to solve the problem **—**

**Recurrent neural network**

I hope that I could explain to you common perceptions of the most used machine learning algorithms and give intuition on how to choose one for your specific problem.

**Happy Machine Learning ! :)**

Until next time..!

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