Machine Learning - Conclusion
The purpose of this tutorial was to introduce you to Machine Learning, which entails training a machine to perform the same activities as a human brain, but faster and better. In games like Chess and AlphaGO, both of which are considered to be very complex, we have seen that machines can beat human champions. A number of tasks can be performed by machines that aid in human life. In several areas, machines can be trained to perform human tasks.
Supervised or unsupervised machine learning can be used. If you have fewer data sets and clearly labeled data for training, opt for supervised learning. For large data sets, unsupervised learning will usually give better performance. Go for deep learning techniques if you have a large number of data easily available. Besides Reinforcement Learning, Deep Reinforcement Learning and Neural Networks, you now know their applications and limitations.
Furthermore, when it comes to building your own machine learning models, you looked at the different development languages, IDEs, and platforms available. The next step is to learn and practice each machine learning technique. The subject is vast, so there is width, but each topic can be learned in a few hours when you take into account the depth. The topics are independent of each other. To start studying Machine Learning, you need to take one topic at a time, study it, practice it, and implement the algorithms in it using the language of your choice. By practicing one topic at a time, you will soon acquire the width that is ultimately required of a Machine Learning expert.
Frequently Asked Questions
- Machine Learning Tutorial
- Machine Learning - Introduction
- Machine Learning - What Today’s AI Can Do?
- Machine Learning - Traditional AI
- Machine Learning - What is Machine Learning?
- Machine Learning - Categories
- Machine Learning - Supervised
- Machine Learning - Scikit-learn Algorithm
- Machine Learning - Unsupervised
- Machine Learning - Artificial Neural Networks