Machine Learning or automatic learning is a scientific field and, more precisely, a subcategory of artificial intelligence. It uses algorithms to train complex tasks using large data sets. It is machine learning with a multitude of applications.
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Machine Learning is one of the great technologies of artificial intelligence. It is a computer programming method that uses algorithms to allow computers to learn independently without net programming. To be more precise, Machine Learning is based on the exploitation of data, favoring pattern recognition.
The first Machine Learning algorithms are not recent. Some were created in the 1950s, the Perceptron being their best known.
The objective of Machine Learning is straightforward: how to “teach computers to learn” and thus act as humans do by perfecting their way of learning and their knowledge autonomously over time? Where a traditional program makes precise instructions, a Machine Learning algorithm learns from its experience and improves its performance over time. The main objective is to see computers act and react without being programmed beforehand.
The potential of Machine Learning is revealed, among other things, for Big Data, in situations where trends must be spotted from a large amount of diverse and varied data. Machine learning is preferred over traditional methods when such a large amount of data needs to be analyzed. This is thanks to its power in terms of speed and accuracy (the more data it has, the more accurate it becomes).
Machine Learning is essential for discovering patterns within the enormous databases available. It can extract data from complex information sources without human intervention.
The autonomous vehicle is an excellent example of Machine Learning. Indeed, such a vehicle has many cameras, radars and a lidar sensor. Equipment that allows:
A central computer installed in the vehicle constantly collects and analyzes this information and classifies it in the same way as the human brain’s neural networks.
As early as the 2010s, Machine Learning quickly became the most widely used form of artificial intelligence. Using statistical algorithms, it teaches a machine to recognize an image, interpret a text, trade, forecast sales or even recommend products or content that correspond perfectly to the preferences of Internet users.
Operating a Machine Learning model requires four key steps. Typically, this process is handled by a Data Scientist.
The training dataset is the first tool used by business intelligence and information technology (IT) specialists. It requires consequent exploitation of a database. This is the first phase of machine learning for AI before the model concerned goes into production.
To develop this phase, one must choose and appreciate training data. Data will essentially aim to allow the Machine Learning model to acquire notions in solving its designed problems. Labeling this data to indicate to the model the characteristics to identify is possible. Otherwise, the model must spot and extract the recurring features from itself. The data must be meticulously prepared, organized, and cleaned to avoid machine learning model training failure. Otherwise, future predictions will be directly affected.
You can use a Machine Learning framework to develop your own AI to exploit your data better. Because with the help of different frameworks, access to this technology has been more accessible over the years.
This step involves selecting an algorithm to run on the training data set. The choice of algorithm depends on the type and volume of training data and the type of problem to be solved. This algorithm must therefore be compatible with the desired result (forecast, qualification of the content of an image, text, etc.).
The chosen algorithm requires training. It is an iterative process during which the weights and the bias will be modified to optimize the result’s precision. Thus trained, the algorithm represents the Machine Learning model.
The last phase involves optimizing the model by applying it to new data. For example, a machine learning model to spot spam will be used on emails. On the other hand, a Machine Learning model of a robot vacuum cleaner will use data from real-world interactions like moving furniture or adding new objects to the room. Its performance and accuracy can thus increase over time.
According to several scientists, Machine Learning represents the only acceptable form of AI as it incorporates a central function of human intelligence: learning. But for others, it’s just a family of AI technologies to solve a finite number of problems.
Multiple and diverse Machine Learning algorithms are divided into two main categories: supervised and unsupervised, depending on whether or not it is necessary to label the data beforehand.
When it comes to supervised learning, the data used is already labeled. This is often the case with structured data from company management systems (e.g. credit repayment or not, mechanical breakdown or not). Therefore, the machine learning model knows what to look for (pattern, element…) in this data. Once the training is complete, the trained model can detect the same elements on unlabeled data.
Some examples of Machine Learning algorithms.
Linear or logistic are the least powerful but most easily interpretable algorithms. They make it possible to understand the relationships between the data.
Represented classically in the form of straight lines on a graph, the role of linear regression is to determine the value of a variable to be predicted, also called the “dependent variable”, from the value of one or more other explanatory variables, also called “independent variables”. The terms dependent/independent come from the assumption that the dependent variable depends on the independent variables, which they do not depend on (the road accident rate depends on the consumption of alcohol and not the reverse). An example of linear regression is predicting a salesperson’s annual sales based on their level of education or experience.
Logistic regression is used when the dependent variables are binary. When the dependent variables are more difficult to classify, other types of regression algorithms, such as the Support Vector Machine, are used.
A decision tree is a decision support tool. The set of choices is represented in the graphical form of a tree, hence its name. We find the different possible decisions at the ends of the branches and the tree leaves. This tool can be readable and quick to execute but also automatically calculable by supervised learning algorithms.
Clustering is a machine learning method of grouping data points. Data clustering is a data analysis method used in unsupervised learning. Clustering algorithms are thus used to divide data into subgroups called clusters. The purpose of data partitioning is to divide a set of data into different homogeneous groups so that each subset has common characteristics, according to so-called proximity criteria.
They aim to discover the patterns and relationships between data and identify “if/then” relationships, called association rules. These are rules similar to those of Data Mining.
This is the case, for example, of the Apriori algorithm, which can be used by sales teams seeking to determine which product will be chosen with another by a customer. In practice, more advanced algorithms such as collaborative filtering (User-User, Item-Item), bandit algorithms on A/B testing or matrix factorization suggest other purchases when you browse a site. E-commerce.
This is a very classic algorithm called Principal Component Analysis. Its purpose is to determine the directions where the data set has the most variances and to protect the data linearly in those directions.
Dimension reduction can also be made with reinforcement learning. Here, the algorithm learns by trying to reach its goal multiple times.
Also Read: Machine Learning And Deep Learning Increasingly Necessary For Companies
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