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Building AI models begins with clear goals, clean data, and selecting appropriate algorithms.
Beginners can use tools like Python, scikit-learn, and TensorFlow to start building AI models from scratch
Continuous evaluation and tuning are key to improving AI model performance over time.
Artificial Intelligence (AI) has been gaining traction from organizations across industries to automate workflows and reduce the probability of errors caused by human negligence. Utilizing this technology can save plenty of resources and manpower.
The most common examples of AI’s role across different sectors include content recommendation systems for streaming platforms and disease detection models for early diagnosis.
Developing AI models falls under the machine learning domain. This guide is for people curious about the subject and process of building AI models. It caters to both individuals who have started learning and professionals looking to level up by providing an in-depth explanation.
Here is a step-by-step overview of the AI model development process:
Before creating an AI model, it is important to specify the purpose of the project. Whether it is predicting sales, classifying images, or recommending products, having clear goals can help identify necessary datasets and the type of model required for AI model training. This will further help streamline the code-writing process.
Example: "Predict whether a loan applicant is likely to default based on their credit history."
Machine learning systems are trained on a large volume of data, and the quality of this data directly affects the results produced by the model. An inefficient dataset can render the best algorithms ineffective. Learners can consider the following pointers while preparing datasets:
Use trusted sources for gathering data. E.g., Kaggle, Hugging Face, and the Open Images Dataset by Google.
Clean the data by removing duplicates and handling missing values.
If necessary, normalise or scale features.
Selecting the algorithm depends on the task at hand. Here is a rundown on how to choose an appropriate algorithm:
Classification: Decision Trees, Random Forest, SVM, or Neural Networks
Regression: Linear Regression, XGBoost
Clustering: K-means
Natural Language Processing (NLP): Transformers or LSTM
Also Read: Building a Career in AI Without Coding: A Beginner’s Guide
Training the AI models on the entire dataset might be counterproductive. It can overwhelm the system and lead to overfitting and inefficient utilization of computing resources. Splitting the datasets into ‘training’ and ‘testing’ sets can help improve the AI model’s performance:
Training Set (usually 70-80%) – Used to teach the model
Test Set (20-30%) – Used to assess the model's performance on new data.
The AI model learns from the training data through the selected algorithms and adjusts itself to minimise errors.
Here’s a sample code for AI model training on a logistic regression algorithm using the scikit-learn library:
From sklearn.linear_model import LogisticRegression
model = LogisticRegression()
model.fit(X_train, y_train)
While the code may look simple, it's the quality of the data and the choice of algorithm that truly determine the results.
Once the model is trained, learners need to use the testing dataset and check several parameters to evaluate the model’s performance. Some metrics to observe include:
Accuracy
Precision & Recall
Confusion Matrix
Mean Squared Error (MSE) for regression tasks
Also Read: Building AI Models from Scratch: A Guide
Model performance is not often achieved perfectly on the first attempt. This is when tuning, also known as hyperparameter optimization, is required. Tools like GridSearchCV enable the testing of a large number of configurations to determine which one works best.
After using GridSearchCV, developers need to continue testing on real-world data to achieve the best results over time.
Once the model is performing decently, deploy the model and see how it interacts with live user inputs. You can also share the source code on developers' sites like GitHub to receive feedback from other experts. Some of the platforms available for deployment include:
Flask or FastAPI through web apps
AWS, Azure, Google Cloud, or similar cloud platforms
Integrate into live software or mobile apps
Acquiring skills on how to build AI models from scratch can open doors for aspiring data scientists and working professionals. While the process doesn't involve hard-coding, it is more about critical thinking, working with data, and iteration.
Building intelligent systems requires patience and practice, which are essential for tackling real-world problems. TensorFlow or scikit-learn are some of the best tools for machine learning and artificial intelligence models.
