Machine Learning Tutorial: 20 Tips
Machine Learning Tutorial: 20 Tips
Machine learning is a growing technology that enables computers to learn automatically from past data as discussed over at runrex.com. If you have heard about and are wondering what it is all about, then this article, through the following 20 tips, should help you grasp basic and intermediate concepts of machine learning.
What is machine learning?
As the gurus over at guttulus.com outline, machine learning is an application of Artificial Intelligence (AI) which enables a program (software) to learn from the experiences and improve itself at a task without being explicitly programmed. It can be said to be the study of making machines more human-like in their behavior and decision-making by giving them the ability to learn with minimum human intervention, i.e., no explicit programming. If you are wondering how a program can attain any experience and from where does it learn, then the answer is with data. Data is also called the fuel for Machine Learning and it is safe to say that there is no machine learning without data.
How is machine learning different from traditional programming?
As discussed over at runrex.com, in traditional programming, we would feed the input data and a well-written and tested program into a machine to generate output. However, when it comes to machine learning, input data along with the output associated with the data is fed into the machine during the learning phase, and it works out a program for itself.
Why do we need machine learning?
As per the experts over at guttulus.com, machine learning can automate many tasks, especially the ones that only humans can perform with their innate intelligence. Replicating this intelligence to machines can be achieved only with the help of machine learning. With the help of machine learning, businesses can automate routine tasks. It also helps in automation and quickly creates models for data analysis. Image recognition, text generation, and many other use-cases of machine learning are finding applications in the real world.
How does machine learning work?
A machine learning model learns from the historical data fed to it and then builds prediction algorithms to predict the output for the new set of data that comes in as input to the system. The accuracy of these models would depend on the quality and amount of input data. A large amount of data will help build a better model which predicts the output more accurately.
History of machine learning
Machine learning has actually been around for over 70 years now. It all started in 1943, when neurophysiologist Warren McCulloch and mathematician Walter Pitts wrote a paper about neurons, and how they work. They decided to create a model of this using an electrical circuit and, therefore, the neural network was born. In 1950, Alan Turing created the “Turing Test” to determine if a computer has real intelligence. In 1957, Arthur Samuel wrote the first computer learning program, and later in 1967, the “nearest neighbor” algorithm was written, allowing computers to begin using very basic pattern recognition. Then a big leap occurred in 1997 when IBM’s Deep Blue became the first computer chess-playing system to beat a reigning world chess champion. In 2006, Geoffrey Hinton created the term “deep learning” to explain new algorithms that help computers distinguish objects and text in images and videos.
Machine learning at present
2012 saw the publication of an influential research paper by Alex Krizhevsky, Geoffrey Hinton, and Ilya Sutskever, describing a model that can dramatically reduce the error rate in image recognition systems. Google’s X Lab then developed a machine learning algorithm capable of autonomously browsing YouTube videos to identify the videos that contain cats as covered by runrex.com. In 2016, AlphaGo won for out of five matches against Lee Sedol, who has been the world’s top Go player for over a decade. In 2020, OpenAI released GPT-3, which is the most powerful language model ever with the ability to write creative fiction, generate functioning code, compose thoughtful business memos, and so much more.
Features of machine learning
The following are the key features of machine learning according to guttulus.com:
Automation – The ability to automate repetitive tasks is one of the biggest characteristics of machine learning.
Improved customer experience – For any business, one of the most crucial ways to drive engagement, promote brand loyalty, and establish long-lasting customer relationships is by providing a customized experience and better services. Machine learning helps us to achieve both of them.
Automated data visualization
Business intelligence
Types of machine learning
As is covered over at runrex.com, machine learning has been broadly categorized into 3 categories:
Supervised Learning
Unsupervised Learning
Reinforcement Learning
What is Supervised Learning?
This is the type of machine learning method in which we provide sample labeled data to the machine learning system to train it, and on that basis, it predicts the output. The system creates a model using labeled data to understand the datasets and learn about each data, once the training and processing are done, then we test the model by providing sample data to check whether it is predicting the exact output or not. The goal of supervised learning is to map input data with the output data. Supervised learning is based on supervision, and it is the same as when a student learns things under the supervision of the teacher. Supervised learning can be grouped further in two categories of algorithms:
Classification
Regression
What is unsupervised learning?
Unsupervised learning is a learning method in which a machine leans without any supervision. The training is provided to the machine with the set of data that has not been labeled, classified, or categorized, and the algorithm needs to act on that data without any supervision. The goal of unsupervised learning is to restructure the input data into new features or a group of objects with similar patterns. In unsupervised learning, we don’t have a predetermined result and the machine, therefore, tries to find useful insights from the huge amounts of data. Unsupervised learning can be further classified into two categories of algorithms:
Clustering
Association
Reinforcement learning
As per the gurus over at guttulus.com, reinforcement learning is a feedback-based learning method in which a learning agent gets a reward for each right action and gets a penalty for each wrong action. The agent learns automatically with this feedback and improves its performance. In reinforcement learning, the agent interacts with the environment and explores it. The goal of an agent is to get the most reward points and, therefore, it improves its performance.
Machine learning algorithms
As is covered in more detail over at runrex.com, there are many algorithms in Machine Learning. The most popular ones are:
Linear Regression
Logistic Regression
Decision Tree
SVM
Naïve Bayes
K-nearest Neighbor
K-Means
Random Forest
Gradient Boosting algorithms like GBM, XGBoost, CatBoost, and LightGBM
How to choose a machine learning algorithm
Given that there are plenty of machine learning algorithms, it could be a tough task to decide which algorithm to choose for a specific application. The choice of the algorithm will depend on the objective of the problem you are trying to solve according to guttulus.com.
Steps in machine learning
There are several steps in machine learning which are a must for each project:
Gathering data – This involves collecting data that can help you solve your problem
Preparing the data – Once you have your data, you need to bring it into a proper format and process it, which is what this step is about, and it includes data cleaning.
Choosing a model
Hyperparameter tuning – Hyperparameters are crucial as they control the model behavior of a machine learning model. The ultimate goal is to find am an optimal combination of hyperparameters that gives you the best results.
Evaluation – This step will allow you to know if the model is performing well or not and involves testing the model with some data (testing data) which must be a subset of the data (training data) on which we trained the algorithm.
Prediction
Evaluation of a machine learning model
For evaluating the model, we hold out a portion of data called test data and do not use this data to train the model. Later on, we can now use the test data to evaluate various metrics. The result of predictive models can be viewed in various forms such as by using confusion matrix, root-mean-squared error (RMSE), AUC-ROC, etc.
Python for machine learning
While many languages can be used for machine learning, the gurus over at runrex.com point out that Python is arguably the best programming language for machine learning applications. This is mainly because of Python’s readability and relatively lower complexity as compared to other programming languages, which is important as machine learning applications involve complex concepts like calculus and linear algebra which take a lot of effort and time to implement.
What other languages can be used for machine learning?
While Python is usually preferred, other programming languages that could be used for machine learning applications are R, C++, Java, JavaScript, C#, Julia, Shell, TypeScript, and Scala. According to guttulus.com, R is also a really good language to get started with machine learning.
Advantages of machine learning
The following are some of the advantages of machine learning, with a detailed write-up on the same to be found over at runrex.com.
Easily identifies trends and patterns
Continuous improvement that helps models upgrade with time and increase their performance
Handling multidimensional and multi-varied data
Wide applications from commerce to healthcare industries, and many others
Disadvantages of machine learning
Some of the drawbacks of machine learning are:
Data acquisition – Machine learning requires a massive amount of data sets to train on. There are also times where we must wait for new data to be generated.
Time and resources – Machine learning needs enough time to let the algorithms learn and develop enough to fulfill their purpose with a reasonable amount of accuracy and relevancy. It also needs massive resources to function which can mean additional requirements of computer power for you.
Interpretation of results – The ability to accurately interpret results generated by the algorithms is another major challenge.
High error-susceptibility – While machine learning is autonomous, it is also highly susceptible to errors.
Future of machine learning
Finally, because of machine learning, things that are currently being done manually will be done tomorrow by machines. With the introduction of projects such as self-driving cars, we have already started to catch a glimpse of what the future may hold.
Hopefully, this article will help you grasp some of the main concepts as far as machine learning is concerned, with more on this topic and more tips to be found over at the highly-rated runrex.com and guttulus.com.
