Roadmap for Artificial Intelligence (AI)
What does an AI Engineer do daily?
- Understanding Problems: An AI engineer starts their day by trying to understand what sorts of problems AI can help solve.
- Data Collection and Cleanup: They gather data from various sources and ensure it’s clean and ready to use in their AI models.
- Creating AI Models: AI engineers build AI models, which are like special tools that can help with these problems.
- Testing Models: They check how well these AI tools work and make improvements if needed.
- Writing Instructions: They write instructions in the form of computer code to make sure their AI models understand what to do.
- Staying Updated: They continue learning about the latest AI technology and techniques to stay on top of their game.
- Teamwork: AI engineers work with others on AI projects and share their progress and ideas.
- Fixing Issues: If anything goes wrong during their work, like computer problems, they figure out how to fix them.
- Keeping Records: They write down everything they do and how it all works for future reference.
- Meetings and Communication: They participate in meetings to discuss how their projects are going and plans for the future.
- Project Management: They ensure their AI projects stay on track, meeting deadlines and goals.
- Thinking Ethically: AI engineers consider what’s right and wrong in using AI and ensure their work follows the rules and is ethically sound.
- Continuous Learning: They keep learning new skills and stay updated with the latest advancements in AI.
- Putting AI to Use: They make sure the AI models they’ve created are working correctly in real-life situations.
- Quality Checks: They ensure the results produced by AI are accurate and trustworthy through careful testing.
Being an AI engineer requires flexibility, good problem-solving skills, and proficiency in computer programming to succeed in the exciting field of AI.
Now, let’s understand the biggest confusion many AI Enthusiasts have which is How AI, ML, Deep Learning, Generative AI, LLMs, and RAG are connected. So, let’s understand the relationship between AI, ML, Deep Learning, Generative AI, LLMs, and RAG.
Step-by-Step AI Project
Project Example: Sentiment Analysis of Movie Reviews
Step 1: Define the Problem
Example: Build a model that classifies movie reviews as positive or negative.
Step 2: Gather and Prepare Data
- Collect Data:
- Use a dataset like the IMDb Movie Reviews dataset.
- Prepare Data:
- Clean the data by removing special characters and converting text to lowercase.
- Convert the text into a numerical format using methods like Bag of Words or TF-IDF.
Step 3: Choose a Model
- Start with simple models like Logistic Regression or Naive Bayes.
- As you learn more, try advanced models like LSTM or Transformers.
Step 4: Train the Model
- Split Data:
- Divide the dataset into training and testing sets (e.g., 80% for training, 20% for testing).
- Train the Model:
- Use the training data to teach your model.
Step 5: Evaluate the Model
- Test the model with the testing set.
- Measure accuracy, precision, recall, and F1 score to see how well the model performs.
Step 6: Improve the Model
- Try different data cleaning techniques.
- Test different models and adjust settings (hyperparameters).
- Use cross-validation to ensure the model performs well on new data.
Step 7: Deploy the Model
- Make the model available as a web service or integrate it into an application.
- Use tools like Flask or FastAPI for deployment.
Step 8: Monitor and Maintain the Model
- Regularly check how well the model performs.
- Update the model with new data as needed.
Real-World Example: Sentiment Analysis of Movie Reviews
Step-by-Step Implementation:
- Problem Definition:
- Build a sentiment analysis model to classify IMDb movie reviews as positive or negative.
- Data Collection:
import pandas as pd
from sklearn.datasets import load_files
data = load_files('aclImdb/train')
reviews, labels = data.data, data.target
3. Data Preparation:
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.model_selection import train_test_split
from sklearn.naive_bayes import MultinomialNB
from sklearn.pipeline import make_pipeline
from sklearn.metrics import accuracy_score
# Clean reviews
reviews = [review.lower().replace(b'<br />', b' ') for review in reviews]
# Split data
X_train, X_test, y_train, y_test = train_test_split(reviews, labels, test_size=0.2, random_state=42)
# Create a model pipeline
model = make_pipeline(CountVectorizer(), MultinomialNB())
4. Train the Model:
model.fit(X_train, y_train)5. Evaluate the Model:
y_pred = model.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print(f'Accuracy: {accuracy}')6. Improve the Model:
- Try using TF-IDF instead of CountVectorizer.
- Experiment with other models like Logistic Regression.
7. Deploy the Model:
- Use Flask to create a web application.
from flask import Flask, request, jsonify
app = Flask(__name__)
@app.route('/predict', methods=['POST'])
def predict():
review = request.json['review']
prediction = model.predict([review])
return jsonify({'sentiment': 'positive' if prediction[0] == 1 else 'negative'})
if __name__ == '__main__':
app.run(debug=True)
8. Monitor and Maintain the Model:
- Regularly check the model’s performance.
- Retrain the model with new data to keep it accurate.
This project helps you understand how to start and complete a basic AI project. As you gain experience, you can explore more advanced techniques and projects. Congratulations, it’s your first step toward AI.
Detailed Roadmap :
https://www.mltut.com/artificial-intelligence-learning-roadmap/
