Let’s Learn, How Machines are Trained? - ‘Machine Learning’ the Ultimate Sheen

Let’s Learn, How Machines are Trained?

– ‘Machine Learning’ the Ultimate Sheen

Introduction:

Machine learning (ML) is a category of algorithm that allows software applications to become more accurate in predicting outcomes without being explicitly programmed. The basic premise of ML is to build algorithms that can receive input data and use statistical analysis to predict an output. ML is an application of AI that provides systems the ability to automatically learn and train. It focuses on the development of computer programs that can access data and use it to learn for themselves.

The process of learning begins with observations or data, such as direct experience, or instruction, in order to look for patterns in data and make better decisions in the future based on the examples that we provide. The primary aim is to allow the computers to learn automatically without human intervention or assistance and adjust actions accordingly.

ML algorithms are used in a wide variety of applications, such as email filtering, and computer vision, where it is infeasible to develop an algorithm of specific instructions for performing the task. ML is closely related to computational statistics, which focuses on making predictions using computers. The study of mathematical optimization delivers methods, theory and application domains to the field of ML.

Data mining is a field of study within ML and focuses on exploratory data analysis through unsupervised learning. It allows computers to handle new situations via analysis, self-training, observation and experience.

How does it work?

The algorithm is trained using a training data set to create a model. When new input data is introduced to the ML algorithm, it makes a prediction on the basis of the model.

The prediction is evaluated for accuracy and if the accuracy is acceptable, then the algorithm is deployed. If the accuracy is not acceptable, then the algorithm is trained again and again with an augmented training data set. Our brain trains itself by identifying the features and patterns of knowledge/data received, thus enabling itself to successfully identify or distinguish between various things.

Similarly, we feed knowledge/data to the machine, this data is divided into two parts namely, training data and testing data. The machine learns the patterns and features from them and trains itself to take decisions like identifying, classifying or predicting new data

Classifications:

• Supervised ML: The program is trained on a pre-defined set of training examples, which then facilitate its ability to reach an accurate conclusion when given new data
• Unsupervised ML: Algorithms are used when the information used to train is neither classified nor labeled. No labels are given to the learning algorithm, leaving it on its own to find structure in its input. It is used for clustering population in different groups
• Semi-supervised Learning: In analyses where lots of unlabelled data is available alongside a few data points which have been labeled can be used to combine and learn from each of these
• Reinforcement Learning: Reinforcement ML algorithms is a learning method that interacts with its environment by producing actions and discovers errors or rewards. Trial and error search and delayed reward are the most relevant characteristics
• Classification: Inputs are divided into two or more classes, and the learner must produce a model that assigns unseen inputs to one or more (multi-label classification) of these classes. This is typically tackled in a supervised way. Classification involves taking data and assigning it to one of several categories
• Regression: In this type of problem the output is a continuous quantity. Regression problems can be solved by using Supervised ML algorithms like Linear Regression, Neural Networks, and Gaussian Processes. Regression analyses try to predict continuous quantities from input data
• Clustering: It mainly deals with finding a structure or pattern in a collection of un-categorized data. Clustering algorithms will process the data and find natural clusters (groups) if they exist in the data

Nitty-Gritty:

• Data: There are two main ways to get the data — manual and automatic. Manually collected data contains far fewer errors but takes more time to collect. The automatic approach is cheaper — you're gathering everything you can find and hope for the best. It's extremely tough to collect a good collection of data (usually called a dataset)
• Features: Also known as parameters or variables. In other words, these are the factors for a machine to look at
• Algorithms: It is a set of rules and statistical techniques used to learn patterns from data and draw significant information from it
• Model: After training the system, a model is created to make predictions. A model is a specific representation learned from data by applying some ML algorithm. A model is also called the hypothesis

• Target (Label): A target variable or label is the value to be predicted by our model
• Training: The idea is to give a set of inputs (features) and it’s expected outputs(labels), so after training, we will have a model (hypothesis) that will then map new data to one of the categories trained on. It is the process in which the patterns of a data set are detected
• Prediction: Once our model is ready, it can be fed a set of inputs to which it will provide a predicted output
• Representation: How to represent knowledge. Examples include decision trees, sets of rules, instances, graphical models, neural networks, and SVM’s
• Evaluation: The way to evaluate candidate programs (hypotheses). Examples include accuracy, prediction and recall, squared error, and likelihood
• Support Vector Machines: These are a set of related supervised learning methods used for classification and regression. An SVM training algorithm is a non-probabilistic, binary and linear classifier
• Conditional Probability: It is where an event can only happen if another event has happened. Therefore, the probability of P (A and B) = P (A|B) * P (B)
• Bayes Theorem: Bayes Theorem states that about the probability of events given prior knowledge of the events
• Artificial Neural Networks: It is a model based on a collection of connected units or nodes called artificial neurons, which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit information, a signal, from one artificial neuron to another. An artificial neuron that receives a signal can process it and the signal additional artificial neurons connected to it

• Bayesian Networks: A belief network or directed acyclic graphical model is the probabilistic graphical model that represents a set of random variables and their conditional independence with a directed acyclic graph (DAG)
• Genetic Algorithms: It is a search algorithm and heuristic technique that mimics the process of natural selection, using methods such as mutation and crossover to generate new genotypes in the hope of finding good solutions to a given problem
• Decision Trees: It makes classification models in the form of a tree structure. An associated decision tree incrementally developed and at the same time it breaks down a large data-set into smaller subsets. The final result is a tree with decision nodes and leaf nodes. A decision node has two or more branches. Leaf-node represents a classification or decision. The first decision node in a tree which corresponds to the best predictor called root node

Advantages:

• Easily identifies trends and patterns
• Handling multi-dimensional and multi-variety data
• It allows time cycle reduction and efficient utilization of resources
• Provides continuous quality improvement

Disadvantages:

• Bias, Time, Resources, Model assessments, and Ethics
• High error-susceptibility, Data Acquisition, and Interpretation of Results

Applications:

• Computer vision, Bioinformatics, and DNA sequence classification
• Banking, Insurance, Government, Internet fraud detection and Linguistics
• Natural language processing, Optimization and Computer Networks
• Sentiment analysis, Speech recognition and Syntactic pattern recognition
• Retail, Oil, Gas, Telecommunication and User behavior analytics
• Agriculture, Transportation, Augmentation, and Automation

Developer Take-A-Ways:

• R Programming, Python, SAS, C++, Java, and MATLAB
• KNIME, Amazon Machine Learning, CNTK, Deeplearning4j, ELKI, H2O, Keras, OpenNN, scikit-learn, Apache SystemML, TensorFlow, Torch / PyTorch, Weka / MOA, IBM Data Science Experience, Google Prediction API, IBM SPSS Modeler, Mathematica, MATLAB, Microsoft Azure ML and Data Miner
• https://developer.apple.com/machine-learning/
• https://github.com/josephmisiti/awesome-machine-learning
• https://www.sap.com/india/products/leonardo/machine-learning.html
• https://www.mathworks.com/discovery/machine-learning.html
• https://www.edx.org/course/machine-learning-1
• https://www.udacity.com/course/machine-learning--ud262

Conclusion:

It’s an incredibly powerful technology. In the coming years, it promises to help solve some of our most pressing problems, as well as open up whole new worlds of opportunity for data science firms. I hope this article helped you to get acquainted with the basics of ML

I’m going to share a bunch of tools for developers at the Developer Take-A-Ways Section of the story, but feel free to comment, share or send me any other interesting videos or links you might have found. ✌ It’s a massive opportunity to work on. I hope you found this article useful.

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