Here is how to develop a machine learning algorithm. Take the following steps:
1. Review different machine learning algorithms and choose the algorithm to build
You need to first understand your own project requirements. Project teams use different machine learning methods for different purposes.
Data scientists might use predictive analytics for data science-specific use cases, whereas, another Artificial Intelligence (AI) team might build machine learning systems for other reasons. E.g., a project team might use machine learning with AI capabilities like natural language processing (NLP), computer vision, etc.
Review the prominent machine learning algorithms before choosing the right algorithm to build. The following examples of important machine learning algorithms:
A. Naïve Bayes Classifier Algorithm
ML (machine learning) project teams use this popular algorithm to solve classification problems. It uses the supervised learning approach, i.e., it works with “labeled” input data.
B. K Means Clustering Algorithm
It’s one of the unsupervised learning algorithms. ML project teams utilize this for clustering of the input data set.
C. Support Vector Machine Algorithm
While most project teams use the “Support Vector Machine” (SVM) algorithm for classification problems, some of them use it to solve regression problems. It’s one of the well-known supervised learning algorithms.
D. Linear Regression
Data scientists and ML project teams make great use of this supervised learning algorithm to solve linear regression problems.
E. Logistic Regression
This supervised learning algorithm helps to address machine learning problems where you need to find discrete values of dependent variables from independent variables.
F. Artificial Neural Networks (ANNs)
Artificial Neural Networks have significant utility in deep learning. You design and create Artificial Neural Networks by taking inspiration from the way the human brain operates. These algorithms use the reinforcement learning approach.
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G. Decision Trees
This supervised learning algorithm helps to create flow charts that look like trees. ML projects use it for solving many real-world problems like binary classification problems.
2. Hire developers to develop a machine learning algorithm
You need the right developers to develop effective algorithms and machine learning models. We recommend you hire a Python developer to develop a machine learning algorithm. Python has a great reputation among artificial intelligence/machine learning developers and data scientists.
Look for programming skills when hiring developers, however, a deeper understanding of machine learning is even more important. The programmer you hire should know what it takes to create good models and algorithms.
The developer needs a thorough understanding of different algorithms. Programmers should know how to improve a machine learning model’s performance.
Developers should know of different types of mathematical problems like ordinary least squares and binary classification problems. Depending on the project, programmers might need to know about loss functions like the “Mean Squared Error” (MSE).
3. Learn about the algorithm before diving deep into how to develop a machine learning algorithm
You need to learn sufficiently about the algorithm that you have decided to build. Understand the functionality of the algorithm, and understand where it’s used. Learn when you shouldn’t use this algorithm.
Explore relevant sources for learning. E.g., you can look at an authoritative book. A good example is “Machine Learning For Absolute Beginners” by Oliver Theobald.
You can also look at informative blog posts, e.g.:
- A guide to writing linear and logistic regression algorithms;
- A guide to writing a single-layer perceptron algorithm.
4. Data collection and data preparation
You might collect data for your machine learning model and algorithm from different data sources. You can’t use that data straight away after you collect data though.
An ML project team needs to prepare data sets first. This enables them to have clean, consistent, and accurate data sets.
You need to take help from business stakeholders and data scientists for this. They need the same unlimited access to the data that your ML developers have.
Implement a set of repeatable steps so that you can execute them for new data sets. Invest in technology solutions so that you can prepare more data when you need it with the same scale and speed.
The data preparation steps are as follows:
A. Data collection
You need to first collect data from the relevant data sources. Your ML project team should work on the following challenges at this stage:
- Scanning external data sources and identifying relevant data;
- Determining the relevant attributes in data sets;
- Parsing data from files like XML and JSON into tabular formats;
- Combining data into the appropriate number of data sets;
- Preparing plans to remove biases from the input data sets.
B. Explore data and create data profiles
You now need to assess the condition of the input data that you have collected. Do the following at this stage:
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- Identify trends in the input data sets.
- Examine the data sets for outliers.
- Find out the various exceptions in the data sets.
- Make a list of incorrect or missing data points.
- Identify the inconsistencies in the data sets.
- Look for issues that could introduce biases in your expected outputs.
C. Organize the data sets in the appropriate format for consistency
You might have gathered data for your training and test sets from different data sources. They might have different formats.
Furthermore, you might not be the only one to manually update the data sets. Other users might have unlimited access to the data sets, and they might update them. All of the above examples might result in different formats in different data sets.
However, your machine learning model might need the data in a certain format. Your team needs to organize your input data sets in that format. This task might require standardizing certain values in several columns.
D. Improve the quality of the data sets
Improve the quality of your input data sets. You might need to do the following:
- Build a strategy to correct data errors.
- Manage the missing values.
- Manage the extreme values in the data sets.
- Find a solution to outliers in the input data sets.
- Review the distribution of your data and identify discrepancies.
- Analyze the “outliers” in your data sets.
- Use appropriate data preparation tools.
- Ensure that your modified data sets are similar to the real data sets.
E. Feature engineering after analyzing the input variables
The term “feature engineering” refers to the act of modifying raw data into features for the understanding of machine learning algorithms. This step helps ML algorithms to understand the data better since they can see patterns in the data.
Feature engineering might involve decomposing the input data sets into multiple parts. An ML project team might do this to categorize data by different values.
Each part of the data set will help the ML algorithm to understand specific relationships in the data sets. The ML algorithms can also find patterns in the data.
F. Split data sets into training data and test data sets
You can now divide your input data sets into two sets. One of these two sets is to train the ML algorithm that you are building. You should use the other data set for testing your algorithm.
What if you have heavily skewed training examples in your input data? This can result in biases. This can adversely impact the performance of your machine learning model, and this is especially true with respect to complex problems. You need to choose the “random state” effectively. This argument helps you to eliminate biases in your input data sets.
5. Design and implement a robust information security solution
You use AI and ML to build autonomous systems. Such systems differ fundamentally from explicitly-programmed systems.
AI and ML systems learn from input data sets and improve their performance over time. The quality of learning influences their performance, therefore, you need to feed them with high-quality training data.
Depending on the sensitivity of your ML project, protecting the sanctity of the training and test data sets can be hard. Malicious players might try to tamper with the training data, which is called “data poisoning”. ML models can make wrong inferences based on manipulated training data.
Analyze the information security risks faced by your organization. Strategize and design an information security solution to prevent “data poisoning” and other attacks. Implement the information security solution.
6. Create the pseudocode for the machine learning algorithm
Before you start coding, you need to create the pseudocode for the ML algorithm that you plan to build. Write the pseudocode in as much detail as you can. That will help you to understand the algorithm in more detail than what you learned so far.
Take the simple example of a linear regression algorithm. Under which conditions will you get the “best-fit” straight line in the output? By creating the pseudocode, you get this understanding even before the programming phase.
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The exact work in this phase will depend upon the algorithm you are developing. You can refer to authoritative books and blog posts for more information before you create the pseudocode. The following are a few examples of authoritative resources:
- A guide to the Naïve Bayes Classifier algorithm;
- An explanation of the K-Means Clustering algorithm with examples;
- A descriptive guide to the Support Vector Machine algorithm;
- An explanation of the Linear Regression algorithm;
- A guide to the Logistic Regression algorithm;
- An explanation of Artificial Neural Networks (ANNs);
- A guide to the Decision Tree algorithm.
You need to implement a review of the pseudocode created. Your ML project team should incorporate the relevant findings from the review.
7. Code the machine learning algorithm
Having created the pseudocode, you now need to develop the ML algorithm. Your project plan should include a structured code review process. This helps you to detect defects even before you start testing.
8. Train the machine learning algorithm you have created
You had earlier created separate input data sets for training and testing. Now, you need to utilize the training data set to train the new algorithm you have created.
Review the machine learning model created during this training, and analyze the outliers. You might find problems with the input data that earlier escaped your attention.
Analyze data errors if you find them. Run the previously-created data preparation process to create better training data. Reiterate the training and review processes.
9. Test the machine learning algorithm
You now need to validate the ML algorithm with the help of your test data set. Execute the algorithm and create an ML model. Review the output in detail. Pay special attention to outliers and exceptions, and examine the reasons.
Check whether the outliers and exceptions originated due to errors in the input data sets. In that case, make the necessary corrections in the input data sets. Rerun the tests. Reiterate the review process.
You would want to compare the output of your ML algorithm against a standard implementation of that algorithm and the same input data set. Scikit-learn, a popular Python library already includes standard implementations of many popular ML algorithms. The following are a few examples:
- Scikit-learn Naïve Bayes Classifier;
- Scikit-learn K-Means Clustering;
- Scikit-learn Support Vector Machine;
- Scikit-learn Linear Regression;
- Scikit-learn Logistic Regression;
- Scikit-learn Decision Tree.
Review the comparison results and analyze the differences. Take corrective actions if applicable.
If you need help developing your machine learning algorithm then why not take a moment to contact DevTeam.Space via this project specification form.
This depends on the training data. If the given training data set has questions and answers, then it’s a “labeled” data set. You can use a supervised learning algorithm in that case. However, most of the real-world data sets are “unlabeled”. Such training sets require unsupervised learning.
Many data mining techniques are widely utilized in machine learning. A few examples are association rule learning, classification, clustering analysis, correlation analysis, decision-tree induction, and regression analysis. Data mining knowledge is important in machine learning.
The “random state” is an argument in machine learning algorithms. You need to eliminate biases in your available data sets. Therefore, you need to split data sets into test data sets and training data sets. Choosing the right random state argument helps you to split data sets effectively.