We live in a world where the growth of technology has increased to such an extent that many people cannot keep up with it and are left behind. Artificial Intelligence is a relatively new trend in science that wants to create fundamental changes in people’s lives. Artificial intelligence is a bit difficult to define, but we can say that artificial intelligence is a combination of different sciences to make machines intelligent. One of the famous subfields of artificial intelligence is machine learning, which is heavily discussed these days. You feel the impact of machine learning every day in your daily life and this science is present in our daily life to some extent. If you decide to learn machine learning and don’t know where to start, this article can be useful for you.
What is machine learning?
Machine learning is a method of artificial intelligence that allows computers to learn from data and make predictions and decisions using trained algorithms and models. This process automatically discovers patterns and connections that are difficult for humans to detect by analyzing data.
If we want to have a simple definition of Machine Learning, we should say that machine learning is a science that teaches machines how to learn new things from themselves. After hearing this sentence, you are probably asking yourself, why should machines learn from themselves? How does this work for us? We will examine this sentence with an example.
Suppose we want to clean the floor of a field. When a human does this work, the quality of the work can be very variable because it depends on many factors. The possibility that a person will get sick or get tired after a few hours of work or even want to quit is very high.
But if we teach the machine to recognize the dirt on the ground and start cleaning its surface based on the amount of dirt and the condition of the ground. If we define this task for a machine, it can do it much better than a human. Without being tired or likely to get sick. The machine in question should be able to answer the following questions:
- When does the floor need to be cleaned?
- How long should the floor cleaning last?
- Etc
This is what machine learning does. That is, it allows the machine to learn from itself and constantly improve its behavior.
Machine learning is useful for many things, including:
1. Forecast
Machine learning can make predictions about the occurrence of events based on past data; Such as demand forecasting, price forecasting, etc.
2. Pattern recognition
This technique is useful for identifying different patterns in the data. For example, in medicine, medical images are used to diagnose diseases.
Pattern recognition in machine learning is done by learning from data. The main steps that the machine learning model performs for pattern recognition are:
- 1. **Choosing the right model:** First you need to choose the right model to recognize the pattern. This model can be different models including neural networks, decision trees, support vector machines, etc.
- 2. **Training the model:** The selected model is trained using the training data. This training data contains examples of patterns and features that the model needs to learn.
- 3. **Extraction of features:** In this step, the model extracts various features from the data. These features may be numerical representations of the data from which the model can infer patterns and relationships.
- 4. **Pattern recognition:** The model recognizes the patterns and rules that exist in the data by carefully analyzing the features and input data. This allows the model to automatically extract patterns from the data.
- 5. **Prediction and Evaluation:** After training, the model can make predictions on new data. Then, by comparing the model predictions with the actual results, the performance of the model is evaluated. If the model correctly detects patterns, its predictions will be more accurate.
Pattern recognition in machine learning generally means learning patterns, rules and hidden features in data. This allows the model to be used for a variety of pattern recognition problems, such as recognizing faces in images, detecting states associated with diseases in medical data, and many others.
How many categories are machine learning divided into?
In general, machine learning is divided into 3 categori:
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
In the continuation of the article, what is machine learning, we will explain these three categories.
What is supervised learning?
As you can guess from its name, in this case the car needs a supervisor or guide. Just like a person sitting behind the wheel and learning to drive. Someone sits next to this person as a guide and gives him the necessary advice. In supervised learning, a series of pre-prepared data is given to the machine as a guide, and the machine makes the necessary decisions according to the relevant model.
What is unsupervised learning?
In this case, the machine does not need a guide and can discover relationships between data with the help of observations. In this case, after the computer receives different data, it can discover the relationships between them. An example in unsupervised learning is a machine that can recognize the difference between Samand and Dana cars based on the patterns it has understood with the help of clustering. That is, if 100 cars are introduced to the car, it will be placed in one of these 2 categories.
What is reinforcement learning?
Again, you can understand its function by carefully looking at the name of this model. In this case, the machine constantly improves itself and tries to learn new things in relation to an agent or environment. This method tries to solve the problem with the help of trial and error. In this case, the machine tries to be more successful in its future decisions.
What is the difference between deep learning and machine learning?
The 3 concepts of artificial intelligence (AI), machine learning (ML) and deep learning (DL) are usually placed together and sometimes confused. Deep Learning includes concepts and algorithms inspired by artificial neural networks that make up the structure of the human brain.
In other words, deep learning is a subset of machine learning, and machine learning itself is considered a subset of artificial intelligence. The image below shows the relationship between these 3 concepts.
Machine learning is a knowledge that helps computers do new things without specific programming and by modeling their own behavior. We said that machine learning as a subset of artificial intelligence is divided into 3 general categories with supervision, unsupervised and reinforcement. Machine learning is present in different parts of people’s lives and different services are created with the help of this knowledge.
What are the skills needed to become a machine learning expert?
To become a machine learning expert you need to know many skills. From computer science to mathematics, statistics and probability. This field has its own challenges and if you enter it without interest, you will probably get bored in the middle of the way. Although it is impossible to fit everything you need to become a machine learning expert in one article, but showing the path will help the true enthusiasts to find new and relevant things themselves. Of course, when you start studying and practicing in the field of machine learning, you will realize that there are many things to learn.
1- Learn the theory of machine learning and its algorithms well
It is very important for a machine learning expert to know the basic principles and basic concepts of machine learning. As a machine learning expert, you must know the famous algorithms of this field and the goals they follow. The next step is to understand how these algorithms work with data. As we said in the previous section, machine learning algorithms are in three general areas of supervision, unsupervised and reinforcement. Below we list some of the most famous algorithms for you to read about:
- Linear regression
- logistic regression
- Clustering
- decision tree
- Random forest
- CART
- Apriori
- PCA
- K-means
- KNN
- Etc
Also, reading the article on 10 of the best data mining algorithms can prepare your mind to enter the topics of machine learning.
At this stage, before anything else, try to increase your general knowledge about machine learning. In the following, I will raise some questions and ask you to find their answers. Think about these concepts until you are sure that they have become the queen of your mind.
- What is data science?
- What does Big Data mean?
- What is artificial intelligence?
- What is machine learning?
- What is deep learning?
- What do the above 3 concepts have in common or what do they differ from each other?
- What are the uses of all these heavy and complicated terms outside the world of scientists and in the real world?
2- Have a good understanding of computer science
A machine learning expert should have a lot of knowledge in computer science. If you studied computer engineering or similar at university, you are several steps ahead of other people who want to learn machine learning. Of course, anyone who is interested in this field can quickly get acquainted with these concepts. In general, I mean the following computer science concepts in machine learning:
- You should have a good understanding of data structure: for example stack, queue, arrays, tree, forest, etc
- You should have a good understanding of algorithm design: for example searching, sorting, optimization, computation and complexity, etc
- You should have a good understanding of computer architecture: for example memory, the concept of deadlocks, asymmetric processing, etc
Learning the above concepts alone is not enough. As a machine learning expert, you should be able to implement these concepts and use them correctly when programming.
3- Have a sufficient understanding of statistics and probability
You don’t need to be a statistics and probability genius to become an expert in machine learning. It is enough to learn the basic and necessary concepts. This is good news for those who do not like mathematics and statistics, but want to become machine learning experts. In the following, I will introduce some important and basic concepts of statistics:
- Sampling
- Probability and probability distributions
- Distribution of random variables
- Linear, multiple and logistic regression
- Etc
As you know, to master the mathematical concepts, you have to struggle with the problems and reach the solution yourself. Otherwise, you cannot be sure that you have understood the topic well. There are many resources available for studying general statistics such as books, online courses, etc.
4- Learn Python or R programming language (or both) for data analysis
What is the appropriate programming language for machine learning?
Python programming language is very powerful and flexible while being simple. Python is used in various fields such as web design, application development, game development, etc. When it comes to artificial intelligence and machine learning, Python gets more attention than any other language.
As a machine learning specialist, you must learn a programming language that, due to its simplicity and powerful libraries, Python is a suitable option for people active in the field of machine learning. However, the R language has many features and can help you solve machine learning problems.
5 – Get to know Big Data
However, working with big data is considered a separate specialty and requires specialized personnel. But as a machine learning engineer, you should be familiar with the principles of Big Data because you may deal with a large amount of data during your work.
If you understand how big data is stored, how it is called or how it is processed, you can provide very suitable solutions for a variety of machine learning problems.
I recommend that you definitely use a Linux distribution to practice and learn Big Data because Linux has a good middle ground with Apache Hadoop. Hadoop is a set of scripting tools that solve big data problems using multiple supercomputers. In the following, I will introduce some concepts that will help you in learning machine learning:
- HDFS
- Mapreduce
- Hive
- Pig
- Spark
- Scala
- Etc
6- Start reading about deep learning models
Machine learning models are among the advanced topics in this field. These models helped Apple and Microsoft build Siri and Cortana voice assistants or help car companies work on driverless cars. After learning the previous topics, you should enter a serious phase, which is working with machine learning models.
For starters, you can design a model that can distinguish a flower image from a fruit image. Although this model can’t get you close to building a driverless car right away, it’s a good start and gives you a good idea of where you need to go. Some of the topics that you should learn in this course are:
- Artificial Neural Networks
- Natural language processing
- Convolutional neural networks
- TensorFlow or TensorFlow
- OpenCV or Open Computer Vision Library
Conclusion:
Machine learning is a well-known subfield in artificial intelligence that helps machines or computers to make decisions and act without specific programming and by modeling their own behavior. Machine learning is present in different parts of people’s lives and different services are created with the help of this knowledge. Finding expertise in the field of machine learning has many fans in the world of computer science, because many job fields in the world require data science specialists. To become a machine learning expert, you must know the concepts and theories of Machine Learning, know the famous ML algorithms, have a good understanding of statistics, probability and mathematics, and more importantly, an acceptable ability in the field of information processing and working with Have data through programming. In the article, what is machine learning, we explained the applications of machine learning, how to become a machine learning expert, and the future of machine learning. If you have any questions or comments in this regard, we will be happy to share them with us and Son Learn users.
