Machine learning is one of the hot trends in the software market today. The market of ML has had an amazing growth rate of 38.76% for this decade.
But why so? What is machine learning, actually? Or how it is changing our lives?
If these questions are bugging you, you've come to the right place. Here, I will tell you everything that you should know about machine learning.
Understanding The Concept: Machine Learning
Machine learning is a subset or a part of artificial intelligence (AI) that is designed to focus on the development of machine algorithms and models. It allows many computers to learn and improve their performance on a task through experience and data without being explicitly programmed.
The most common example of machine learning is AI face recognition and automatic tagging on Facebook. Whenever you are about to upload any image or a picture, it will automatically match the image through its database.
After that, it will respond as per your post. If your image has a picture of your friend, Facebook will suggest you tag that person. If your image contains any adult content, it will be deleted automatically. There's no doubt that Facebook's AI face recognition is 97.35% accurate.
How Does Machine Learning Work?
For the last several years, Intetics has been working with the development of AI or ML-based software. So, here's the step-by-step process of how machine learning actually works.
Step 1: Data Collection and Sorting
The first step of Machine learning is to gather data from multiple sources. The data could be Text, images, patterns, or more. This includes input data and the corresponding output or target values. For example, if you're making a spam email classification software, the dataset would include emails as input data and labels indicating whether each email is spam or not as output.
Raw data is often messy and may require cleaning and preprocessing. So, the next task is to remove duplicates, handle missing values, and convert data into a suitable format for analysis.
Step 2: Feature Engineering
In Feature engineering, we select the raw data and transform them into "Features" after extraction. The term feature refers to the data that the machine learning model will use to make predictions.
The input data is usually organized into a tabular form having rows and then proceeded ahead.
Step 3: Splitting the Dataset
Now, the completely filtered dataset is typically divided into two or more subsets: a training set and a testing set. The training set (70-80% of net data) is used to give training to the machine learning algorithms and model, while the testing set (20-30% of net data) is used to evaluate its performance.
In some cases, a validation set is also used for hyperparameter tuning.
Step 4: Choosing an Algorithm
Once you are sure that the data is completely accurate, we can start selecting the appropriate machine-learning algorithm. The choice of algorithm depends on the nature of the data (e.g., structured or unstructured), the type of problem (e.g., regression, classification, clustering), and other factors.
|
Data Type |
Task |
Machine Learning Algorithms |
|
Continuous Numerical Data |
Regression |
Linear Regression |
|
Support Vector Regression (SVR) |
||
|
Binary Classification (Two Classes) |
Classification |
Logistic Regression |
|
Structured Data (Tabular) |
Classification or Regression |
Random Forest |
|
Decision Trees |
||
|
Unlabeled Data |
Clustering |
K-Means Clustering |
|
High-Dimensional Data |
Dimensionality Reduction |
Principal Component Analysis (PCA) |
|
Text Data |
Text Classification |
Naive Bayes (Multinomial or Gaussian) |
|
Sequential Data (e.g., Text) |
Sequence Modeling |
Recurrent Neural Networks (RNNs) |
|
Images or Image-Like Data |
Image Processing |
Convolutional Neural Networks (CNNs) |
|
Real-Time Object Detection |
Object Detection |
YOLO (You Only Look Once) Algorithm |
|
Interaction Data with Environment |
Sequential Decision Making |
Q-Learning |
|
Data with Anomalies/Outliers |
Anomaly Detection |
Isolation Forest |
|
User-Item Interaction Data |
Recommendation Systems |
Collaborative Filtering (Matrix Factorization) |
Step 5: Training the Model
During the training, the data is treated with a suitable algorithm to train the model. The algorithm learns from the given data by adjusting its internal parameters.
Once the model is trained, it is considered using the testing dataset, which it has never seen before (For the developed software). After the tests, the model's predictions are compared to the actual target values and various performance metrics (e.g., accuracy, precision, recall, F1 score).
If the model meets the desired performance criteria, it will be deployed as the beta version. It will be integrated into a software system, a website, or any other platform where it can make predictions, decisions, and output based on new, unseen data.
And that's how the whole custom software development works with Machine Learning.
Bottom Line
98.8% of companies are working on big data, machine learning, and AI development. As the leading custom software development firm, Intetics develops the best machine learning practices for the new world.
Our dedicated developers work with powerful strategies and expert methodologies to create each product with unmatched perfection. To learn more about machine learning and our services, visit Intetics.com.
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