AN OVERVIEW OF MACHINE LEARNING
Machine learning is an application of AI (artificial intelligence) that gives systems the ability to learn and improve automatically with experience without being explicitly programmed. ML focuses on the development of computer programs that can access data and use it to learn on their own.
The learning process begins with observations or data, such as examples, direct experience, or instructions, to look for patterns in the data and make better decisions in the future based on the examples provided. The main objective is to allow computers to learn automatically without human intervention or assistance and to adapt actions accordingly.
However, using classic machine learning algorithms, text is viewed as a sequence of keywords; on the contrary, an approach based on semantic analysis mimics the human capacity to understand the meaning of a text.
The evolution of machine learning
Thanks to new information technologies, machine learning today is not like machine learning in the past. It emerged from pattern recognition and the theory that computers can learn without being programmed to perform specific tasks; researchers interested in artificial intelligence wanted to see if computers could learn from data. The iterative aspect of machine learning is important because, as models are exposed to new data, they can be adapted independently. They learn from previous calculations to produce reliable and repeatable decisions and results. It is a science that is not new but which has taken on new impetus.
While many machine learning algorithms have been around for a long time, the ability to automatically apply complex mathematical calculations to big data over and over again at an ever-faster pace is a recent development. Here are some examples of large-scale advertising machine learning applications you may be familiar with:
1. The essence of machine learning: Google's self-driving cars.
2. Machine learning application for everyday life: online recommendations offers such as Amazon and Netflix
3. Machine learning combined with the creation of linguistic rules: Knowing what customers are saying about you on social media (i.e., Twitter)
4. An important use of Machine Learning: interception of fraud
TYPES OF MACHINE LEARNING ALGORITHMS
Supervised Machine Learning Algorithms
This type of machine learning algorithm can apply what it has learned in the past to new data using labeled examples to predict future events. The training algorithm produces an inferred function for making predictions about the output values by analyzing a known training data set. The system can provide goals for any new entry after sufficient preparation. The training algorithm can also compare the result with the expected correct result and can find errors to modify the model accordingly.
Unsupervised Machine Learning Algorithms
These are used when the information used for training is not classified or labeled. Unsupervised learning studies how methods can infer a function to describe a hidden structure from unlabeled data. The system does not find the correct result but explores the data and can make inferences from the datasets to describe the hidden structures of the unlabeled data.
Semi-supervised Machine Learning Algorithms
These fall somewhere between supervised and unsupervised learning. They use labeled and unlabelled data for training, typically a small amount of labeled data and many unlabelled data. Systems that use this method can greatly improve learning accuracy. Semi-supervised learning is usually chosen when the labeled data acquired requires qualified and relevant resources for training/learning. Otherwise, acquiring unlabelled data does not require additional resources.
Reinforcement Machine Learning Algorithms
This type of machine learning algorithm interacts with your environment by producing actions and discovering errors or rewards. Trial and error search and delayed rewards are the most relevant features of reinforcement learning. This method allows machines and software agents to automatically determine the optimal behavior in a specific context to maximize their performance.
Simple feedback for the reward is needed to inform the agent of the best action; this is called a reinforcement signal.
Machine learning enables the analysis of large amounts of data. While it typically provides faster and more accurate results in identifying profitable opportunities or hazardous risks, it may also require supplementary time and resources to train properly. Combining machine learning with artificial intelligence and cognitive technologies can make you even more efficient in processing large volumes of information.
Why is machine learning important?
The renewed interest in machine learning is driven by the same factors that have made data analysis more popular than ever. Things like increasing the volume and variety of data available, cheaper and more powerful computing, and accessible data storage.
All of this means you can quickly and automatically produce models that can analyze larger, more complex data and deliver faster, more accurate results, even at scale. And by creating accurate models, an organization is more likely to identify profitable opportunities or avoid unknown risks.
What do you need to create good machine learning system?
Algorithms: basic and advanced.
Automation and iterative processes.
Data preparation skills.
Ensemble Modeling.
Scalability.
SOME MACHINE LEARNING ALGORITHM
Naive Bayes classification
Supports vector machines (SVM)
Decision trees
Regression
Some Commonly Used Machine Learning Terminologies
Outliers
These are data points that differ significantly from other observations and can be harmful to the model as they can cause less accurate models.
Clustering
Clustering is an unsupervised machine learning task which involves dividing data points into groups such that similar data points are in the same group.
Feature Selection
This is the process of reducing the number of variables by identifying the most important characteristics of your data using human intuition and algorithms.
Feature Scaling
This is a preprocessing method used to normalize the features of the data.
Overfitting
When a large amount of data drives a machine learning model, it tends to learn from noise and inaccurate data entry. In this case, the model fails to characterize the data correctly.
Prediction
The output the machine learning model gives after being fed with data and trained.
Feature
A feature is a property or a measurable parameter of the dataset.
Underfitting.
This is the scenario where the model fails to decipher the underlying trend in the input data. It destroys the precision of the machine learning model. Simply put, the model or algorithm does not match the data well.
Target (label)
The value that the machine learning model should predict is called a target or label.
Training
An algorithm takes as input a set of data called "training data." The training algorithm finds patterns in the input data and trains the pattern for the expected (target) results. The result of the training process is the machine learning model.
Feature vector
This is a set of several digital resources. We use it as the input to the machine learning model for training and forecasting purposes
Model
Also known as "hypothesis," a machine learning model is the mathematical representation of a real process. A machine learning algorithm associated with the training data creates a machine learning model.
