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What is machine learning?

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Machine learning is a branch of artificial intelligence (AI) focused on building applications that learn from data and improve their accuracy over time without being programmed to do so.
In data science, an algorithm is a sequence of statistical processing steps. In machine learning, algorithms are 'trained' to find patterns and features in massive amounts of data in order to make decisions and predictions based on new data. The better the algorithm, the more accurate the decisions and predictions will become as it processes more data. 

Machine learning algorithms

Machine learning depends on a number of algorithms for turning a data set into a model. Which algorithm works best depends on the kind of problem you’re solving, the computing resources available, and the nature of the data. No matter what algorithm or algorithms you use, you’ll first need to clean and condition the data.
Let’s discuss the most common algorithms for each kind of problem. 

Classification algorithms

A classification problem is a supervised learning problem that asks for a choice between two or more classes, usually providing probabilities for each class. Leaving out neural networks and deep learning, which require a much higher level of computing resources, the most common algorithms are Naive Bayes, Decision Tree, Logistic Regression, K-Nearest Neighbors, and Support Vector Machine (SVM). You can also use ensemble methods (combinations of models), such as Random Forest, other Bagging methods, and boosting methods such as AdaBoost and XGBoost. 

Regression algorithms

A regression problem is a supervised learning problem that asks the model to predict a number. The simplest and fastest algorithm is linear (least squares) regression, but you shouldn’t stop there, because it often gives you a mediocre result. Other common machine learning regression algorithms (short of neural networks) include Naive Bayes, Decision Tree, K-Nearest Neighbors, LVQ (Learning Vector Quantization), LARS Lasso, Elastic Net, Random Forest, AdaBoost, and XGBoost. You’ll notice that there is some overlap between machine learning algorithms for regression and classification.

Clustering algorithms

A clustering problem is an unsupervised learning problem that asks the model to find groups of similar data points. The most popular algorithm is K-Means Clustering; others include Mean-Shift Clustering, DBSCAN (Density-Based Spatial Clustering of Applications with Noise), GMM (Gaussian Mixture Models), and HAC (Hierarchical Agglomerative Clustering). 

Dimensionality reduction algorithms

Dimensionality reduction is an unsupervised learning problem that asks the model to drop or combine variables that have little or no effect on the result. This is often used in combination with classification or regression. Dimensionality reduction algorithms include removing variables with many missing values, removing variables with low variance, Decision Tree, Random Forest, removing or combining variables with high correlation, Backward Feature Elimination, Forward Feature Selection, Factor Analysis, and PCA (Principal Component Analysis).

Machine Learning Training in Noida

Machine Learning is considered a part of Artificial Intelligence in the field of Computer Science. Machine Learning has wide applications as it is used in various industries like Banking, Retail, Publishing, Financial Sector etc. APTRON is one of the top Machine Learning Training in Noida providing Machine Learning Course. The training provided is at par with current industry standards which helps one to achieve their goals and their dream jobs in top companies of the world. APTRON is a well-renowned training company with experience in training services.