1. Image recognition. Image recognition is a well-known and widespread example of machine learning in the real world. It can identify an object as a digital image, based on the intensity of the pixels in black and white images or colour images.9 Sept 2021
How do I choose a machine learning project?
- Data preparation. Exploratory data analysis(EDA), learning about the data you're working with.
- Train model on data( 3 steps: Choose an algorithm, overfit the model, reduce overfitting with regularization) Choosing an algorithms.
- Analysis/Evaluation.
- Serve model (deploying a model)
- Retrain model.
- Machine Learning Tools.
What are some cool AI projects?
- Voice-based Virtual Assistant for Windows.
- Facial Emotion Recognition and Detection.
- Online Assignment Plagiarism Checker.
- Personality Prediction System via CV Analysis.
- Heart Disease Prediction Project.
- Banking Bot.
What types of projects would you be interested in working on ML?
Machine learning (ML) is a type of artificial intelligence (AI) that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so. Machine learning algorithms use historical data as input to predict new output values.
- Find a problem to solve.
- Find relevant data and refine the question.
- Import the data.
- Explore and clean the data.
- Develop and refine the model.
- Communicate your results.
How do I start my own AI project?
- Focus on the right business case. Ask yourself what you want to achieve.
- Realize that AI projects are IT projects.
- Build a foundation that will last.
- Bridge the gap between the data science and business teams.
- Conclusion.
How do I start ml AI?
- Step 1: Adjust Mindset. Believe you can practice and apply machine learning.
- Step 2: Pick a Process. Use a systemic process to work through problems.
- Step 3: Pick a Tool. Select a tool for your level and map it onto your process.
- Step 4: Practice on Datasets.
- Step 5: Build a Portfolio.