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What is Machine Learning?

Machine Learning is undeniably one of the most influential and powerful technologies in today’s world. More importantly, we are far from seeing its full potential. There’s no doubt, it will continue to be making headlines for the foreseeable future. This article is designed as an introduction to the Machine Learning concepts, covering all the fundamental ideas without being too high level.

Machine learning is a tool for turning information into knowledge. In the past 50 years, there has been an explosion of data. This mass of data is useless unless we analyse it and find the patterns hidden within. Machine learning techniques are used to automatically find the valuable underlying patterns within complex data that we would otherwise struggle to discover. The hidden patterns and knowledge about a problem can be used to predict future events and perform all kinds of complex decision making.

Types of Learning
  • Supervised Learning

  • Unsupervised Learning

  • Reinforcement Learning

  • Semi-Supervised Learning

  • Self-Supervised Learning

  • Multi-Instance Learning

  • Inductive Learning

  • Deductive Inference

  • Transductive Learning

  • Multi-Task Learning

  • Active Learning

  • Online Learning

  • Transfer Learning

  • Ensemble Learning

​Now her e we know top and currently used machine learning types:

1. Supervised Learning: Applications in which the training data comprises examples of the input vectors along with their corresponding target vectors are known as supervised learning problems.

There are two main types of supervised learning problems: they are classification that involves predicting a class label and regression that involves predicting a numerical value.

  • Classification: Supervised learning problem that involves predicting a class label.

  • Regression: Supervised learning problem that involves predicting a numerical label.

Both classification and regression problems may have one or more input variables and input variables may be any data type, such as numerical or categorical.

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2. Unsupervised Learning: In unsupervised learning, there is no instructor or teacher, and the algorithm must learn to make sense of the data without this guide.

There are many types of unsupervised learning, although there are two main problems that are often encountered by a practitioner: they are clustering that involves finding groups in the data and density estimation that involves summarizing the distribution of data.

  • Clustering: Unsupervised learning problem that involves finding groups in data.

  • Density Estimation: Unsupervised learning problem that involves summarizing the distribution of data.

Clustering and density estimation may be performed to learn about the patterns in the data.

Additional unsupervised methods may also be used, such as visualization that involves graphing or plotting data in different ways and projection methods that involves reducing the dimensionality of the data.

  • Visualization: Unsupervised learning problem that involves creating plots of data.

  • Projection: Unsupervised learning problem that involves creating lower-dimensional representations of data.

The goal in such unsupervised learning problems may be to discover groups of similar examples within the data, where it is called clustering, or to determine the distribution of data within the input space, known as density estimation, or to project the data from a high-dimensional space down to two or three dimensions for the purpose of visualization.

3. Reinforcement Learning: Reinforcement learning is learning what to do — how to map situations to actions—so as to maximize a numerical reward signal. The learner is not told which actions to take, but instead must discover which actions yield the most reward by trying them.

Some machine learning algorithms do not just experience a fixed dataset. For example, reinforcement learning algorithms interact with an environment, so there is a feedback loop between the learning system and its experiences.

In many complex domains, reinforcement learning is the only feasible way to train a program to perform at high levels. For example, in game playing, it is very hard for a human to provide accurate and consistent evaluations of large numbers of positions, which would be needed to train an evaluation function directly from examples. Instead, the program can be told when it has won or lost, and it can use this information to learn an evaluation function that gives reasonably accurate estimates of the probability of winning from any given position.

Most common Machine Learning Services which is offered by <Realcode4you>

Here list of services which is offered by realcode4you.

  • Types of Learning – Supervised Learning

  • Types of Learning – Part 2

  • Supervised and Unsupervised learning

  • Reinforcement learning

  • Regression and Classification

  • Understanding Logistic Regression

  • Understanding Logistic Regression

  • Multivariate Regression

  • Confusion Matrix in Machine Learning

  • Linear Regression(Python Implementation)

  • Softmax Regression using TensorFlow

  • Linear Regression using PyTorch

  • Parameters for Feature Selection

  • Introduction to Dimensionality Reduction

  • Underfitting and Overfitting in Machine Learning

  • Handling Missing Values

  • Clustering in Machine Learning

  • Different Types of Clustering Algorithm

  • K means Clustering – Introduction

  • Analysis of test data using K-Means Clustering in Python

  • Gaussian Mixture Model

  • Decision Tree

  • Decision Tree Introduction with example

  • K-Nearest Neighbours

  • Implementation of K Nearest

  • Decision tree implementation using Python

  • Getting started with Classification

  • Understanding Data Processing

  • Data Cleansing | Introduction

  • Data Preprocessing for Machine learning in Python

  • Identifying handwritten digits using Logistic Regression in PyTorch

Terminology which is used by us

  • Dataset: A set of data examples, that contain features important to solving the problem.

  • Features: Important pieces of data that help us understand a problem. These are fed in to a Machine Learning algorithm to help it learn.

  • Model: The representation (internal model) of a phenomenon that a Machine Learning algorithm has learnt. It learns this from the data it is shown during training. The model is the output you get after training an algorithm. For example, a decision tree algorithm would be trained and produce a decision tree model.

Process to do the task

  1. Data Collection: Collect the data that the algorithm will learn from.

  2. Data Preparation: Format and engineer the data into the optimal format, extracting important features and performing dimensionaility reduction.

  3. Training: Also known as the fitting stage, this is where the Machine Learning algorithm actually learns by showing it the data that has been collected and prepared.

  4. Evaluation: Test the model to see how well it performs.

  5. Tuning: Fine tune the model to maximise it’s performance.

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