Introduction to Machine Learning
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Machine Learning is an idea to learn from examples and experience, without being explicitly programmed. Instead of writing code, we feed data to the generic algorithm, and it builds logic based on the data given.

Machine Learning is about making predictions. Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it.

For example, one kind of algorithm is a classification algorithm. It can put data into different groups. The classification algorithm used to detect handwritten alphabets could also be used to classify emails into spam and not-spam.

“A computer program is said to learn from experience E with some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E.”

Consider playing checkers.

E = the experience of playing many games of checkers

T = the task of playing checkers.

P = the probability that the program will win the next game.

Machine Learning can best be understood through four progressive lenses.

  1. The Broad: Machine Learning is the process of predicting things, usually based on what they’ve done in the past.

  2. The Practical: Machine Learning tries to find relationships in your data that can help you predict what will happen next.

  3. The Technical: Machine Learning uses statistical methods to predict the value of a target variable using a set of input data.

  4. The Mathematical: Machine Learning attempts to predict the value of a variable Y given an input of feature set X.

There are many examples of machine learning. Here are a few examples of classification problems where the goal is to categorize objects into a fixed set of categories.

Face detection: Identify faces in images (or indicate if a face is present).

Email filtering: Classify emails into spam and not-spam.

Medical diagnosis: Diagnose a patient as a sufferer or non-sufferer of some disease.

Weather prediction: Predict, for instance, whether or not it will rain tomorrow.

Need of Machine Learning

Machine Learning is a field which is raised out of Artificial Intelligence(AI). Applying AI, we wanted to build better and intelligent machines. But except for few mere tasks such as finding the shortest path between point A and B, we were unable to program more complex and constantly evolving challenges. There was a realisation that the only way to be able to achieve this task was to let machine learn from itself. This sounds similar to a child learning from itself. So machine learning was developed as a new capability for computers. And now machine learning is present in so many segments of technology, that we don’t even realize it while using it.

Finding patterns in data on planet earth is possible only for human brains. The data being very massive, the time taken to compute is increased, and this is where Machine Learning comes into action, to help people with large data in minimum time.

If big data and cloud computing are gaining importance for their contributions, machine learning as technology helps analyse those big chunks of data, easing the task of data scientists in an automated process and gaining equal importance and recognition.

The techniques we use for data mining have been around for many years, but they were not effective as they did not have the competitive power to run the algorithms. If we run deep learning with access to better data, the output we get will lead to dramatic breakthroughs which is machine learning.

Machine Learning theory is a field that meets statistical, probabilistic, computer science and algorithmic aspects arising from learning iteratively from data which can be used to build intelligent applications.

There are various reasons why the mathematics of Machine Learning is necessary, and some of them are highlighted below:

Selecting the appropriate algorithm for the problem includes considerations of accuracy, training time, model complexity, the number of parameters and number of characteristics.

  • Identifying underfitting and overfitting by following the Bias-Variance tradeoff.
  • Choosing parameter settings and validation strategies.
  • Estimating the right determination period and uncertainty.

Level of Maths Needed

The foremost question when trying to understand a field such as Machine Learning is the amount of maths necessary and the complexity of maths required to understand these systems.

The answer to this question is multidimensional and depends on the level and interest of the individual.

Here is the minimum level of mathematics that is needed for Machine Learning Engineers / Data Scientists.

  1. Linear Algebra (Matrix Operations, Projections, Factorisation, Symmetric Matrices, Orthogonalisation)
  2. Probability Theory and Statistics (Probability Rules & Axioms, Bayes’ Theorem, Random Variables, Variance and Expectation, Conditional and Joint Distributions, Standard Distributions.)
  3. Calculus (Differential and Integral Calculus, Partial Derivatives)
  4. Algorithms and Complex Optimizations (Binary Trees, Hashing, Heap, Stack)
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