578_Machine _learning @_presentationnn_12
- 4. Natural Language Processing Applications
- 5. Machine Learning Machine learning is a branch of artificial intelligence and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy.
- 6. Types of Machine Learning Supervised ML Unsupervised ML Semi- supervised ML Reinforcement Learning
- 7. Supervised ML Supervised machine learning also known as supervised learning, is defined by its use of labeled datasets to train algorithms to classify data or predict outcomes accurately.
- 8. Unsupervised ML Unsupervised machine learning uses machine learning algorithms to analyze and cluster unlabeled datasets. These algorithms discover hidden patterns or data groupings without the need for human intervention
- 9. Semi-supervised learning: Semi-supervised learning (SSL) is a machine learning technique that uses a small portion of labeled data and lots of unlabeled data to train a predictive model.
- 10. Reinforcement Learning Reinforcement machine learning is a machine learning model that is similar to supervised learning, but the algorithm isn’t trained using sample data. This model learns as it goes by using trial and error. A sequence of successful outcomes will be reinforced to develop the best recommendation or policy for a given problem.
- 11. Types of Supervised Learning Classification • The Classification algorithm is a Supervised Learning technique that is used to identify the category of new observations on the basis of training data. • In Classification, a program learns from the given dataset or observations and then classifies new observation into a number of classes or groups. • Such as, Yes or No, 0 or 1, Spam or Not Spam, cat or dog, etc. Classes can be called as targets/labels or categories. Regression • Regression analysis is a statistical method to model the relationship between a dependent (target) and independent (predictor) variables with one or more independent variables. • More specifically, Regression analysis helps us to understand how the value of the dependent variable is changing corresponding to an independent variable when other independent variables are held fixed. • It predicts continuous/real values such as temperature, age, salary, price, etc
- 12. Classification Algorithms: • Logistic Regression • Naive Bayes • K-Nearest Neighbors • Decision Tree • Support Vector Machines Regression Algorithms: • Linear Regression • Polynomial Regression • Ridge Regression • Lasso Regression • SV Regression • Decision tree for regression • Random forest regression • KNN regression
- 13. Unsupervised Learning Algorithms • Clustering: Clustering is a method of grouping the objects into clusters such that objects with most similarities remains into a group and has less or no similarities with the objects of another group. Cluster analysis finds the commonalities between the data objects and categorizes them as per the presence and absence of those commonalities. • Association: An association rule is an unsupervised learning method which is used for finding the relationships between variables in the large database. It determines the set of items that occurs together in the dataset. Association rule makes marketing strategy more effective. Such as people who buy X item (suppose a bread) are also tend to purchase Y (Butter/Jam) item. A typical example of Association rule is Market Basket Analysis.
- 14. Traditional programming vs ML
- 15. ML Project Workflow • Data collection • Data Preprocessing • Split data into test and train • Model selection • Training • Testing • Deployment
- 16. Thank You