Necessity to learn Machine Learning Algorithms.

A Visit to Machine learning Algorithms:

Machine Learning Algorithms? What is it?

Machine learning algorithms are the ones which help the computer to learn for itself. In machine learning each algorithm has a specific function ,so there are many Algorithms.

Different Types of Algorithms:

Regression algorithm:

• Linear regression

• Logistic regression

• Multiple Adaptive Regression (MARS)

• Local scatter smoothing estimate (LOESS)

Instance-based learning algorithm:

• K — proximity algorithm (kNN)

• Learning vectorization (LVQ)

• Self-Organizing Mapping Algorithm (SOM)

• Local Weighted Learning Algorithm (LWL)

Regularization algorithm:

• Ridge Regression

• LASSO（Least Absolute Shrinkage and Selection Operator）

• Elastic Net

• Minimum Angle Regression (LARS)

Decision tree algorithm:

• Classification and Regression Tree (CART)

• ID3 algorithm (Iterative Dichotomiser 3)

• C4.5 and C5.0

• CHAID（Chi-squared Automatic Interaction Detection）

• Random Forest

• Multivariate Adaptive Regression Spline (MARS)

• Gradient Boosting Machine (GBM)

Bayesian algorithm:

• Naive Bayes

• Gaussian Bayes

• Polynomial naive Bayes

• AODE（Averaged One-Dependence Estimators）

• Bayesian Belief Network

Kernel-based algorithm:

• Support vector machine (SVM)

• Radial Basis Function (RBF)

• Linear Discriminant Analysis (LDA)

Clustering Algorithm:

• K — mean

• K — medium number

• EM algorithm

• Hierarchical clustering

Association rule learning:

• Apriori algorithm

• Eclat algorithm

Neural Networks:

• Sensor

• Backpropagation algorithm (BP)

• Hopfield network

• Radial Basis Function Network (RBFN)

Deep learning:

• Deep Boltzmann Machine (DBM)

• Convolutional Neural Network (CNN)

• Recurrent neural network (RNN, LSTM)

• Stacked Auto-Encoder

Dimensionality reduction algorithm:

• Principal Component Analysis (PCA)

• Principal component regression (PCR)

• Partial least squares regression (PLSR)

• Salmon map

• Multidimensional scaling analysis (MDS)

• Projection pursuit method (PP)

• Linear Discriminant Analysis (LDA)

• Mixed Discriminant Analysis (MDA)

• Quadratic Discriminant Analysis (QDA)

• Flexible Discriminant Analysis (FDA)

Integrated algorithm:

• Boosting

• Bagging

• AdaBoost

• Stack generalization (mixed)

• GBM algorithm

• GBRT algorithm

• Random forest

Other algorithms:

• Feature selection algorithm

• Performance evaluation algorithm

• Natural language processing

• Computer vision

• Recommended system

• Reinforcement learning

• Migration learning

These are the various algorithms of machine learning, each of them has its specific functions. The question is why do we need to learn so many algorithms? We don’t need to know these all algorithms, there are few algorithms we need to learn.

Why do we need to study these Algorithms?

It is necessary to learn machine learning algorithms if you want to build a career in machine learning. Machine learning has a very bright future so it’s a necessity to know the basic machine learning algorithms to build a career in this field. According to a research report, machine learning algorithms are expected to substitute 25% of jobs globally. This proves that machine learning is the shining star for the future. Studying machine learning opens new avenues of opportunities to develop leading edge machine learning applications in various verticals – like cyber security, image recognition, medicine, or face recognition. With several machine learning companies on the verge of hiring skilled ML engineers, it's becoming the brain behind business intelligence. Netflix announced a prize worth $1 million to the primary individual who could enhance the accuracy of its recommendation ML algorithm by 10%. This is transparent evidence on how significant even a small enhancement is within the accuracy of advice machine learning algorithms to enhance the profitability of Netflix. Every customer- centric organization is looking to adopt machine learning technology and is the next big thing paving opportunities for IT professionals. Machine learning algorithms became the darlings of business.

This shows that machine learning is the new big thing and it is necessary to know the machine learning algorithms. But the problem is there are many algorithms, so here are the most popular algorithms:

• Naïve Bayes Classifier Algorithm

• K Means Clustering Algorithm

• Support Vector Machine Algorithm

• Apriori Algorithm

• Linear Regression

• Logistic Regression

• Artificial Neural Networks

• Random Forests

• Decision Trees

• Nearest Neighbours

Learning these few machine learning algorithms will help one to embark towards the future generation jobs.