Over the past decade, machine learning has brought us self-driving cars, voice recognition, effective web search engines, etc. It is so common today that you may use it dozens of times a day naturally without notice. In this post, we'll share everything about machine learning code. Let's get started.
What is Machine Learning
Machine learning (ML) is part of artificial intelligence (AI) and belongs to computational science that researches how to let computers learn and act. It specializes in analyzing and interpreting the patterns and structures of data to achieve the purpose of learning, reasoning, and decision-making.
Simply put, you can input a large amount of data to computer algorithms, then let machines analyze these data and give suggestions and decisions. The algorithm will consolidate information and improve future decisions if it recognizes any corrections.
Key elements of Machine Learning
Each machine-learning algorithm consists of three main parts.
Representation: To represent data
Examples:
- Decision Trees
- Sets of Rules, Instances
- Graphical Models
- Neural Networks
- Support Vector Machines
- Model Ensembles, etc.
Evaluation: A method to evaluate based on assumptions
Examples:
- Accuracy
- Prediction and recall
- Squared error
- Likelihood
- Posterior probability
- Cost
- Margin
- Entropy k-L divergence
Optimization: Adjust parameters to improve accuracy
- Combinatorial optimization
- Convex optimization
- Constrained optimization
What are the steps of machine learning?
There are five basic steps to perform machine learning tasks:
1. Data collection
Whether it comes from excel, access, text files, etc., collecting past data forms the basis for future learning. The more types, densities, and quantities of relevant data, the better the learning prospects of machines.
2. Prepare data
Any analysis process will depend on the quality of the data used. You have to take time to determine data quality and then take measures to solve problems such as missing data and outlier processing.
3. Training model
This step involves selecting the appropriate algorithm and data representation in a model. The cleaned data is divided into two parts - training data and testing data. The training data is used to develop and train the model.
4. Evaluate the model
You can use the training data to test the accuracy of your trained model. It determines the accuracy of algorithm selection. It is a better approach to check the accuracy and performance of the model by using data that has yet to be used during model construction.
5. Improve performance
It involves selecting completely different models or introducing more variables to improve efficiency. Adjusting parameters may affect the efficiency of the model.
Types of Machine Learning
Supervised Learning
Supervised learning gives labeled data sets. Computers can use specific patterns to recognize new samples of each tag type. The two main types of supervised learning are classification and regression.
Supervised learning data has clear labels or characteristics, which can clearly tell you which types or values there are.
Unsupervised Learning
In unsupervised learning, the data is unlabelled. Since most real-world data is not labeled, these algorithms may be more practical. Unsupervised learning is divided into clustering and dimension reduction.
In short, unsupervised learning has a pile of data. But it's up to you to find everything yourself, regarding their different types and values.
Reinforcement Learning
In reinforcement learning, the machine learns how to get high scores in the environment and show excellent results. But behind these results are its hard work, continuous trial and error, and accumulative learning experience.
Summary
With the upsurge of artificial intelligence, people have gradually become interested in machine learning. To master machine learning, working with data is a must. You should check your CPU to ensure the code is runnable on your machine.
Welcome to contact us if you are keen on more coding-related information. Start your ML journey now!
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