Machine learning is one of the emerging technologies of recent times. Recruiters are looking to hire individuals who have the skills and are aware of machine learning basics for beginners. In this machine learning tutorial, we will learn more about what machine learning means, what are its advantages and disadvantages, python for machine learning, and more. This ML tutorial is designed for beginners as well as working professionals who wish to learn the basic concepts of ML. Before we move ahead, let us understand what machine learning is.
What is Machine Learning?
The term machine learning was coined in the year 1959 by Arthur Samuel. A pioneer in AI and computer gaming, he defined machine learning as the “field of study that gives computers the ability to learn without being explicitly programmed”.
But what does this mean? In a much simpler way, it is an application of artificial intelligence that allows programs or softwares to learn through past experiences and to improve its working without being programmed to do so. Through machine learning, machines are made more human-like in their behaviour and ability to learn by developing their own programs. They work based on a set of data that is fed to the machines. Machine learning models and algorithms are built on the basis of this data. Data is the fuel for machine learning.
If you are wondering how machine learning is different from traditional programming? In traditional programming, we are providing the machine with the data, and a well-defined program as well. The machine generates the output based on this data and program. However, in case of machine learning, in the learning phase, the input data and the output is fed to the machine, and it would work out a program by itself.
Although Machine Learning has been around for a long time now, the reason for it’s increased popularity is due to the advancements in computational power. ML requires a high computational power and we have now reached a stage where this is achievable. The amount of data and storage available has also increased.
Why do we need Machine Learning?
Before we move ahead in the machine learning tutorial, we must understand why ML is required in the first place. Firstly, it can help us automate tasks. There are several tasks which only humans can perform, but through machine learning, we can automate these tasks. Passing on our intelligence to the machines will help us in automation. Routine tasks can be automated as well.
A number of industries today are highly dependent on data and insights to help them in taking important business decisions, optimise operations, and more. Data analysis can be performed with the help of ML algorithms. ML models can be created that will help us in processing and analysing the large amount of data that is available to us, and also to help in delivering accurate decisions and results.
Machine learning models are highly precise as there is no room for human error to take place. They are scalable and account for a shorter turnaround time. Businesses today are able to leverage these technologies and profit from opportunities. A few of the real-world applications of machine learning are image recognition and text generation. The next step in our machine learning tutorial is to understand the features of ML.
Features of Machine Learning
- Improved customer experience
- Business intelligence
- Automated data visualization
Types of Machine Learning
Machine Learning is typically divided into three main categories. You won’t be learning more about these types in this tutorial as it is a ML tutorial for beginners. You can learn more about these concepts by taking up a machine learning course.
- Supervised learning
- Unsupervised learning
- Reinforcement learning
Machine Learning Algorithms
When you embark on your journey to learn machine learning, you will have to learn most of these algorithms to build a successful career in the domain. In this machine learning tutorial, we will only name the algorithms and not learn any in-depth knowledge. Listed below are the most popular algorithms in the domain.
- Linear regression
- Logistic regression
- Decision Trees
- K-means clustering
- K-nearest neighbor
- Random Forest
- Naive Bayes
- Gradient Boosting algorithms
Learning more about these algorithms with the help of a machine learning course will help you gain the necessary knowledge and skills.
Steps in Machine Learning
Machine learning is not only about applying algorithms to the data, there is a lot more involved in the entire process. It is not as simple as it seems and there are a few key steps to be kept in mind while working on a machine learning project. In this machine learning tutorial for beginners, let us understand the different steps involved in the process.
- Gathering data
- Preparing this data
- Choosing a machine learning model
- Hyperparameter Tuning
- Evaluating the models
Python for Machine Learning
Python is the mostly widely used programming language when it comes to machine learning. It is the language that most programmers are comfortable as it offers a set of pre-built libraries. There are several different packages for different types of machine learning applications, some of these are mentioned below.
- NLTK, Numpy, and Scikit while working with text
- Numpy, Scikit, and OpenCV for images
- Matplotlib, Scikit, and Seaborn while working with data representation
- Librosa when it comes to audio applications
- Scipy is used for scientific computing
- Pytorch and TensorFlow are used for deep learning applications
- Pandas is used for high-level data analysis and structures
- Django is used for integrating web applications
We are almost coming to the end of the machine learning tutorial. We hope that you have gained some knowledge so far and have understood the basics and foundations of machine learning. Before we end the machine learning tutorial, let’s understand the advantages and disadvantages.
Advantages of ML
- Continuous learning and improvement
- Easy to identify a trend or a pattern
- Can handle multidimensional and a wide range of data
- It has a wide range of applications
Disadvantages of ML
- Requires a lot of time and resources to work
- Data Acquisition can be difficult
- Susceptible to errors
- The interpretation of results may be difficult
This brings us to the end of the machine learning tutorial. We hope that you are now better-equipped regarding the domain. The future of machine learning is bright and this is the right time to upskill in the domain.