Machine learning techniques tutorial for bigineers

Machine learning techniques tutorial for bigineers: ML has techniques like Supervised Learning, Unsupervised Learning, Reinforcement Learning, and Representation Learning. The algorithm study and model used to perform a task without using explicit instructions. Machine learning algorithm is built as a sample data. We use ML in many applications as a vision where it is difficult to develop an algorithm for performing the task. It is closely related to statistics that focus on predictions using computers. The mining is a field of study within machine learning, and focuses on the data analysis through it. These methods are used to make system learn using methods like supervised learning and Unsupervised Learning which is classified in methods like Classification, egression, and Clustering. This selection of methods depends on the type of dataset that is available to train the model. The machine learning is a data analytics technique which teaches a computer to do the work. We use computational methods to “learn” information directly from data without relying on an equation as a model.

Machine learning uses two types of techniques:-

 Supervised learning:-that trains a model on known input and output data so that it can predict future outputs.
 Unsupervised learning:-it finds a hidden pattern in input data.
Applications of supervised learning are:-

1. Spam detection
2. Sentiment analysis.
3. Image classification
4. Predictive analysis
5. Speech/Sequence processing

Applications of Unsupervised Learning are:-

1. Automatic labeling
2. Similarity detection
3. Object segmentation.