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Introduction to machine learning
- 1. Welcome
- 4. Agenda ❏ Machine Learning ❏ Machine Learning Application ❏ Type of Machine Learning ❏ Machine Learning Algorithms ❏ Machine Learning Workflow ❏ User of Machine Learning
- 6. Artificial Intelligence ❏ Enable the machine to think ❏ Is the brain of a computer
- 7. Machine Learning ❏ Gives computers the ability to learn without being explicitly programmed ❏ Statistical tools to explore and analyze data
- 8. ML Application ❏ Spam filtering ❏ Recommendation Engine ❏ Search Engine ❏ Fraud Detection ❏ Sentiment Analysis ❏ News Classification
- 9. Type of Machine Learning ❏ Supervised learning ❏ Unsupervised learning ❏ Semi-supervised Learning ❏ Reinforcement learning
- 10. Supervised Learning ❏ Learn from labeled training data ❏ Input and desired output data are provided
- 11. Supervised Learning ❏ Classification ❏ Regression
- 12. Classification A classification problem is when the output variable is a category, such as: » “red” or “blue” or “green” » “man” and “women”. » “0” or “1”
- 13. Regression A regression problem is when the output variable is a real value, such as: » “dollars” » “Weight” » “Temperature”
- 14. Supervised Learning Algorithms ❏ Naive Bayes ❏ Linear regression. ❏ Logistic regression. ❏ Artificial neural networks (ANN) ❏ Decision trees. ❏ Support vector machines (SVM). ❏ Random forests.
- 15. Unsupervised Learning ❏ Find hidden structure in unlabeled data ❏ Input and desired output data are not provided
- 16. Unsupervised Learning ❏ Clustering ❏ Association
- 17. Clustering A clustering problem is where you want to discover the inherent groupings in the data, such as grouping customers by purchasing behavior.
- 18. Clustering
- 19. Association An association rule learning problem is where you want to discover rules that describe large portions of your data, such as people that buy X also tend to buy Y.
- 20. Unsupervised Learning Algorithms ❏ Hierarchical clustering ❏ K-means clustering ❏ K-NN (k nearest neighbors) ❏ Principal Component Analysis ❏ Singular Value Decomposition ❏ Independent Component Analysis ❏ Association Rules
- 21. Semi-Supervised Learning ❏ A mix of supervised and unsupervised learning ❏ Mix of labeled and unlabeled data
- 22. Reinforcement learning ❏ Allows the machine or software agent to learn its behavior based on feedback from the environment ❏ Learn by doing ❏ Learn from mistake
- 23. Deep Learning
- 24. Deep Learning ❏ Deep learning is a machine learning technique that teaches computers to do what comes naturally to humans: learn by example. ❏ Artificial Neural Networks
- 25. DL Application ❏ Computer Vision ❏ Face Recognition ❏ Speech Recognition ❏ Natural Language Processing ❏ Audio Recognition ❏ Image Processing
- 26. ML Workflow ❏ Find Out The Problem ❏ Creating Dataset ❏ Dataset Cleaning and Visualization ❏ Algorithm Selection ❏ Model Training ❏ Model Testing ❏ Deployment
- 27. USE OF MACHINE LEARNING ❏ FACEBOOK ❏ GOOGLE, YAHOO, MSN ❏ GMAIL, YAHOO, HOTMAIL ❏ AMAZON, EBAY, ALIBABA ❏ ROBOTICS
- 28. Python for Machine Learning ❏ Numpy ❏ Pandas ❏ Matplotlib, Seaborn ❏ Scikit-learn ❏ Tensorflow, PyTorch ❏ OpenCV ❏ And Many more ……….
- 30. Coding Example
- 31. Question ?
- 32. Thank YOU