At the point when I was starting my way in data science, I frequently confronted the issue of picking the most proper calculation for my particular issue. In case you’re similar to me, when you open some article about machine learning calculations, you see many point by point portrayals. The oddity is that they don’t facilitate the decision.

All things considered, to not let you feel out of the track, I would recommend you to have a decent comprehension of the execution and numerical instinct behind a few managed and unaided 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**

Keep in mind, the rundown of Machine Learning Algorithms I referenced are the ones that are obligatory to have a decent information on , while you are a learner in Machine/Deep Learning !

Since we have some instinct about sorts of machine learning errands, we should investigate the most mainstream calculations with their applications, all things considered, in view of their difficult proclamations !

Attempt to chip away at every one of these issue articulations in the wake of getting to the furthest limit of this blog ! I can guarantee you would gain proficiency with a great deal, a damnation parcel!

## 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 trust that I could disclose to you normal impression of the most utilized machine learning calculations and give instinct on the best way to pick one for your particular issue.