Machine learning (ML) is the study of computer algorithms that improve automatically through experience and by the use of data. It is seen as a part of artificial intelligence. Machine learning algorithms build a model based on sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to do so. Machine learning algorithms are used in a wide variety of applications, such as email filtering and computer vision, where it is difficult or unfeasible to develop conventional algorithms to perform the needed tasks.
1. Finding Centroid.
2. Detection Of Malicious URl using lexical analysis.
3. Hungarian Algorithm.
4. K Means.
5. KNN
i. With Different Distance Matrix.
ii. Finding the K Nearest Neighbour.
7. Naive Bayes.
8. Recommendation System.
9. Support Vector Machine.
10. Percentage of survival on Titanic.
11. Complete ML PipeLine Project.
12. Kaggle Work.
13. Class ML notebook for concepts and techniques.
Step 1: Select and prepare a training data set.
Step 2: Choose an algorithm to run on the training data set.
Step 3: Training the algorithm to create the model.
Step 4: Using and improving the model.
Machine learning algorithms are often categorized as supervised or unsupervised.
- Supervised Machine Learning: Supervised machine learning algorithms can apply what has been learned in the past to new data using labeled examples to predict future events.
- UnSupervised Machine Learning: In contrast, unsupervised machine learning algorithms are used when the information used to train is neither classified nor labeled.
Reinforcement Machine Learning algorithms are a learning method that interacts with its environment by producing actions and discovering errors or rewards.
