5 Things To Know About Machine Learning – UKTN

Consumers are increasingly being introduced to machine learning uses and applications, such as image recognition, self-driving cars, online fraud detection, traffic prediction, speech recognition, and product recommendations. But what is machine learning? How can people use this advanced technology in business and everyday living?

Below are the important things you need to know about machine learning:

1. Machine Learning Definition

What is machine learning (ML)? ML technology involves feeding a massive amount of data into a computer program, creating a model (through algorithms) to ‘fit’ the data. In this way, the machine or computer comes up with objective predictions based on input data and observations (variables) without your help.

Artificial intelligence (AI) gave birth to ML, focusing on creating applications that intuitively learn from data without programming. The algorithm of ML is self-trained. Just imagine the huge amount of data that it can process, helping corporations and enterprises.

Businesses embrace ML operational solutions to standardize workflow. With ML, employees and managers can focus more on business planning, implementation, and auditing than manual, repetitive tasks. Check our site to learn more about adopting ML solutions and how to build your machine learning pipeline to improve your workflow.

2. How Machine Learning Works

ML algorithm pertains to the way models are created by computers, ranging from a simple equation to a complex system of math and logic, allowing the computer to come up with the best predictions.

Foodtech startup Grocemania announces nationwide expansion

ML includes neural networks, a highly advanced application considered as the backbone of ML algorithms. A subset of ML is deep learning, which mimics human brain data processing to create decision making patterns.

Here’s how machine learning works:

• Core Building: Data science and artificial intelligence can be used to create algorithms for machine learning models. After choosing the ML model, you can make some adjustments to improve it. In return, the computer uses the ML model to learn data patterns.
• Data Science Skills Required: While ML uses learning algorithms, such as domain knowledge, math, communication, and computer science, data science is extracting knowledge from the database to solve certain problems and answer specific questions.
• Algorithm Testing: The ML algorithm is tested by using different variables until the desired results are achieved. You can input observations or new conditions, and the machine predicts the outcome.

3. Everyday Uses Of Machine Learning

ML is found in so many places, even more than people can imagine. For one, voice-activated commands in online searches, video or song recommendations, and digital assistants, like Alexa, Siri, and Cortana in smart speakers, smartphones, and mobile apps, employ ML. This technology makes people’s lives more convenient.

Here are the uses and applications of ML:

Language learning app Memrise scores $15.5m Series B

• Medicine: Medical image analysis systems use ML to help neurologists spot tumors.
• Automotive: The first self-driving cars in the market is a product of AI and ML.
• Transportation: ML helps prevent traffic by using velocities and current locations to map current traffic, estimating the congested regions based on daily experiences. Booking Uber or any online transportation services also use ML to estimate fare rates.
• Stock Market: Stock market trading uses ML to determine the shares’ risks by analyzing ups and down historical data. The short-term and long-term memory of ML’s neural network helps in predicting stock market trends.
• Automatic Language Translation: Google Neural Machine Translation (GNMT) was introduced in November 2016. This technology uses ML, increasing Google Translate’s accuracy and fluency.
• Sales And Marketing: ML makes it easier to improve dynamic pricing models and use regression methods for market predictions. Also, sales forecasting is possible with ML, optimizing your pricing structure according to consumer spending habits.
• Data Science Projects: ML is a major aspect of a data science project, used for clustering algorithms or exploratory analysis and discovery. It’s also used in supervised learning algorithms or building predictive models. ML enables creating conclusions from a set of data to solve a specific problem.

4. Machine Learning Versus Traditional Programming

Traditional programming involves formulating a set of rules, whereas anML algorithm utilizes data input and output to learn the model. ML gives computers learning abilities without being programmed.

Traditional programming uses a chain of rules, such as:

‘Marking the email as spam if it contains the word X and non-spam if the email contains the word Y.’

ML algorithms help formulate these rules. Supervised ML algorithms look at labeled emails dataset, creating rules from the data to separate them into two spam and non-spam emails.

South Yorkshire tech firm Metalysis closes £20m funding round

5. Supervised And Unsupervised Machine Learning

The two types of ML include supervised and unsupervised machine learning.

• If the data is ‘labeled’ into an ML model, it’s called supervised learning, where the outcome of the row of data or observation is known. For instance, if your ML model tries to predict whether your customers like your new product or not, the variables or labels you can input may include last purchase activity, feedback, ratings, etc. Check the following supervised learning models:
  • Logistic Regression: This supervised ML model is used if there’s a classification problem. Your target variable consists of categories, and an equation is used to create and use a data curve to guess the new observation outcome.
  • Linear Regression: It’s one of the first ML models that people learn because the algorithm is easy to understand using one x-variable to make a best-fit line when making new data points predictions.
  • Other Supervised ML Models: Other complex ML models include K Nearest Neighbors (KNN) used as either regression or classification; Support Vector Machines that work by creating a boundary between data points; and Decision Trees & Random Forests.
• Unsupervised learning (UL) is a new ML approach that’s not labeled; the opposite of supervised ML. With unsupervised machine learning, you do not know whether your customers like your new product or not, and it’s up to the machine to find data patterns using a model to guess what will happen. Check the following unsupervised learning models:
  • K Means Clustering
  • DBSCAN Clustering
  • Neural Networks

Takeaways

ML programs utilize features and patterns to make smart predictions and decisions. Building ML pipelines involve highly technical steps, which boils down to creating and testing ML models and algorithms to make machines or computers more intuitive for massive, self-learning data. Indeed, ML works as a digital robot with higher intelligence influenced by AI and data science, and is greatly beneficial in various applications and industries.

Source

The post 5 Things To Know About Machine Learning – UKTN appeared first on abangtech.

source https://abangtech.com/5-things-to-know-about-machine-learning-uktn/