Machine learning refers to the process of enabling computer systems to learn with data using statistical techniques without being explicitly programmed. It is the process of active engagement with algorithms in order to enable them to learn from and make predictions on data. Machine learning is closely associated with computational statistics, mathematical optimization, and data learning. It is associated with predictive analysis, which allows producing reliable and fast results by learning from historical trends. There are basically two kinds of machine learning tasks:
Supervised learning: The computer is presented with some example inputs, based on which the desired outputs are to be formed. The computer is made to learn general rules of converting inputs to outputs.
Unsupervised learning: There are no labels given to learning algorithms, so it has to find its own structure to produce an output. Unsupervised learning involves discovering hidden patterns in data on its own. It involves feature learning, which relates to discovering means toward an end.
Machine learning can be an easy task if you are clear about what you want to know about machine learning. Though there are a number of machine learning online courses available, you need to be clear about which topic you want to explore before learning machine learning.
If you are keen to know the theory behind the algorithms and how they work, being well-versed in probability (and statistics), linear algebra, and calculus is vital. Knowing a programming language such as Python will make it easier for you to implement algorithms. It helps you know about the internal mechanics of machines.
Understanding the math and the application at the same time is necessary. Whichever method you choose, practice is essential to be well-versed in machine learning languages. You can either choose from offline methods or go for machine learning online training to build up your basics.
Having prior knowledge of the following is necessary before learning machine learning.
- Linear algebra
- Probability theory
- Optimization theory
Following are some of the most common machine learning tasks along with the possible machine learning methods that can be used to resolve these tasks that you need to know about before learning machine learning.
Regression mainly deals with the estimation of continuous or numerical variables. Estimations of housing price, stock price, product price, etc. are estimated using regression. The following ML methods are used to solve regression problems:
- Kernel regression (higher accuracy)
- Support vector regression
- Gaussian process regression (higher accuracy)
- Linear regression
- Regression trees
Classification is related to the prediction of discrete variables or a category of data. Whether an email is a spam or not, whether a person is suffering from a particular disease or not, whether a transaction is fraud are not — all such estimations are made using classification methods. The following methods can be applied to solve classification problems:
- Kernel discriminant analysis (higher accuracy)
- Artificial neural networks (ANN) (higher accuracy)
- K-nearest neighbors (higher accuracy)
- Boosted trees
- Random forests (higher accuracy)
- Logistic regression
- Support vector machine (SVM) (higher accuracy)
- Deep learning
- Naive Bayes.
- Decision trees
Clustering is related to the natural grouping of data and finding labels associated with each of the groupings. Product features identification, customer segmentation, etc. are some of the examples where clustering finds its use. Common ML methods used are as follows:
- Mean-shift (higher accuracy)
- Topic models
- Hierarchical clustering
Multivariate querying is about finding similar objects. The following methods are used to solve problems related to multivariate querying:
- Nearest neighbors
- Farthest neighbors
- Range search
Dimension reduction refers to the reduction of a number of random variables and is divided into feature extraction and feature selection. The following methods are used to solve dimension reduction-related problems:
- Manifold learning/KPCA (higher accuracy)
- Independent component analysis
- Principal component analysis
- Non-negative matrix factorization
- Compressed sensing
- Gaussian graphical models