Machine Learning(ML) is the field of study that gives computers the capability to learn without being explicitly programmed.
WHAT IS MACHINE LEARNING?
Machine learning(ML) is an application of AI that enables systems to learn and improve from experience without being explicitly programmed. ML focuses on developing comp uter programs that can access data and use it to learn for themselves.
HOW DOES MACHINE LEARNING WORK?
- Similar to how the human brain gains knowledge and understanding, ML relies on input, such as training data or knowledge graphs, to understand entities, domains and the connections between them. With entities defined, deep learning can begin.
- The ML process begins with observations or data, such as examples, direct experience or instruction. It looks for patterns in data so it can later make inferences based on the examples provided. The primary aim of ML is to allow computers to learn autonomously without human intervention or assistance and adjust actions accordingly.
IMPORTANCE OF MACHINE LEARNING (ML):
- ML explores the analysis and construction of algorithms that can learn from and make predictions on data.
- ML has proven valuable because it can solve problems at a speed and scale that cannot be duplicated by the human mind alone.
- With massive amounts of computational ability behind a single task or multiple specific tasks, machines can be trained to identify patterns in and relationships between input data and automate routine processes.
MACHINE LEARNING ALGORITHMS
- Machine learning algorithms build a model based on sample data, known as training data, in order to make predictions or decisions without being explicitly programmed to do so.
- ML algorithms are used in a wide variety of applications, such as in medicine, email filtering, speech recognition, and computer vision, where it is difficult or unfeasible to develop conventional algorithms to perform the needed tasks
TYPES OF MACHINE LEARNING
ML is divided into four types based on the methods and way of learning:
Machine Learning types
- Supervised Learning
- Unsupervised Learning
- Semi-Supervised Learning
- Reinforcement Learning
- In supervised learning, the machine is taught by example. The operator provides the ML algorithm with a known dataset that includes desired inputs and outputs, and the algorithm must find a method to determine how to arrive at those inputs and also outputs.
- While the operator knows the correct answers to the problem, the algorithm identifies patterns in data, learns from observations and makes predictions.
- Under the umbrella of supervised learning fall: Classification, Regression and Forecasting.
- Semi-supervised learning is similar to supervised learning, but instead uses both labelled and unlabelled data.
- Labelled data is essentially information that has meaningful tags so that the algorithm can understand the data, whilst unlabelled data lacks that information.
- By using this combination, machine learning algorithms can learn to label unlabelled data.
- Here, the ML algorithm studies data to identify patterns. There is no answer key or human operator to provide instruction.
- Instead, the machine determines the correlations and relationships by analysing available data. In an unsupervised learning process, the ML algorithm is left to interpret large data sets and also address that data accordingly.
- The algorithm tries to organise that data in some way to describe its structure. This might mean grouping the data into clusters or arranging it in a way that looks more organized.
- Under the umbrella of unsupervised learning, fall: Clustering & Dimension reduction
- Reinforcement learning focuses on regimented learning processes, where a ML algorithm is provided with a set of actions, parameters and end values.
- By defining the rules, the ML algorithm then tries to explore different options and possibilities, monitoring and evaluating each result to determine which one is optimal.
- Reinforcement learning teaches the machine trial and error. It learns from past experiences and begins to adapt its approach in response to the situation to achieve the best possible result.
MACHINE LEARNING LINEAR REGRESSION
- Linear Regression is a ML algorithm based on supervised learning. Linear regression is one of the easiest and most popular Machine Learning algorithms. It is a statistical method which is used for predictive analysis.
MACHINE LEARNING IN PYTHON
- The Python library provides base-level items, so developers do not have to write code from scratch every time. Machine learning requires continuous data processing, and Python libraries allow you to access, process, and transform your data. These are some of the most extensive libraries available for AI and ML
- Benefits that make Python the best fit for machine learning and AI-based projects include simplicity and consistency, access to great libraries and frameworks for AI and machine learning (ML), flexibility, platform independence, and a wide community
ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING
- While AI and ML are very closely connected, they are not the same. ML is considered a subset of AI.
- Artificial intelligence is the capability of a computer system to mimic human cognitive functions such as learning and problem-solving. Through AI, a computer system uses maths and logic to simulate the reasoning that people use to learn from new information and make decisions.
- ML is an application of AI. It’s the process of using mathematical models of data to help a computer learn without direct instruction. This enables a computer system to continue learning and also improving on its own, based on experience
MACHINE LEARNING EXAMPLES
1. Image recognition
Image recognition is a well-known and widespread example of ML in the real world. It can identify an object as a digital image, based on the intensity of the pixels in black and white images or colour images.
Examples of image recognition:
- Label an x-ray as cancerous or not
- Assign a name to a photographed face (aka “tagging” on social media)
- Recognise handwriting by segmenting a single letter into smaller images
- Machine learning is used for facial recognition within an image. Using a database of people, the system can identify commonalities and also match them to faces.
2. Speech recognition
ML can translate speech into text. Certain software applications can convert live voice and recorded speech into a text file.
Examples of speech recognition:
- Voice search
- Voice dialing
- Appliance control
- Some of the most common uses of speech recognition software are devices like Google Home or Amazon Alexa.
3. Medical diagnosis
ML can help with the diagnosis of diseases. Many physicians use chatbots with speech recognition capabilities to discern patterns in symptoms.
Examples for medical diagnosis:
- Assisting in formulating a diagnosis or recommends a treatment option
- Oncology and pathology use ML to recognise cancerous tissue
- Analyze bodily fluids
- In the case of rare diseases, the joint use of facial recognition software and machine learning helps scan patient photos and also identify phenotypes that correlate with rare genetic diseases.
4. Statistical arbitrage
Arbitrage is an automated trading strategy. The strategy uses a trading algorithm to analyze a set of securities using economic variables and also correlations.
Examples of statistical arbitrage:
- Algorithmic trading which analyses a market microstructure
- Analyze large data sets
- Identify real-time arbitrage opportunities
- ML optimizes the arbitrage strategy to enhance results.
5. Predictive analytics
ML will classify available data into groups. It is then defined by rules set by analysts. When the classification is complete, the analysts can calculate the probability of a fault.
Examples of predictive analytics:
- Predicting whether a transaction is fraudulent or legitimate
- Improve prediction systems to calculate the possibility of fault
- Predictive analytics is one of the most promising examples of machine learning. It’s applicable for everything; from product development to real estate pricing.
ML can extract structured information from unstructured data. Organizations amass huge volumes of data from customers. A machine learning algorithm automates the process of annotating datasets for predictive analytics tools.
Examples of extraction:
- Generate a model to predict vocal cord disorders
- Develop methods to prevent, diagnose, and also treat the disorders
- Help physicians diagnose and treat problems quickly
- Typically, these processes are tedious. But ML can track and extract information to obtain billions of data samples.
MACHINE LEARNING COURSE
Google Machine Learning Education offers free online machine learning courses
Google offers the foundational courses cover ML fundamentals and core concepts.
- Introduction to Machine Learning: A brief introduction to ML.
- Machine Learning Crash Course: A hands-on course to explore the critical basics of ML.
- Problem Framing: A course to help you map real-world problems to ML solutions.
- Data Preparation and Feature Engineering: An introduction to preparing your data for ML workflows.
- Testing and Debugging: Strategies for testing and debugging ML models and also pipelines.
Google offers the following Advanced machine learning course
These advanced courses teach tools and techniques for solving a variety of ML problems.
- Decision Forests: Decision forests are an alternative to neural networks.
- Recommendation Systems: Recommendation systems generate personalized suggestions.
- Clustering: Clustering is a key unsupervised ML strategy to associate related items.
- Reinforcement Learning: An introduction to reinforcement learning techniques.
- Generative Adversarial Networks: GANs create new data instances that resemble your training data.
- Image Classification: Is that a picture of a cat or is it a dog?
- Fairness in Perspective API: Hands-on practice debugging fairness issues.
FUTURE OF MACHINE LEARNING
Machine learning is a remarkable technology in the field of artificial intelligence. Even in its earliest uses, ML has already improved our daily lives and also the future.
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