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Index
Introduction The Basics Of Machine Learning
Introducing Machine Learning Important Machine-Learning Terminologies Machine-Learning Components Introducing Python Libraries Supervised Machine-Learning Algorithms Decision-Tree Algorithms Random-Forest Algorithms Native Bayes Theorem Unsupervised Machine-Learning Algorithms K-Means Clustering Algorithm Artificial Neural Network (ANN) Recurrent Neural Networks (RNN) Reinforcement Machine-Learning Algorithms
Machine-Learning Systems
Supervised Learning Unsupervised Learning Reinforcement Learning Expert Systems Artificial Neural Networks Analytics Techniques utilized in Machine Learning Pros and Cons of Machine Learning
Are Machine Learning And AI The Same?
Artificial intelligence. Machine learning
Using The Probability And Statistics To Assist With Machine Learning
Looking at random variables Distribution Conditional distribution Independence
Understanding Python Libraries For Machine Learning
NumPy Pandas SciPy Matplotlib Scikit-Learn Statsmodels
Classification
Installation The MNIST Measures of Performance Confusion Matrix Recall Recall Tradeoff ROC Multi-class Classification Error Analysis Multi-label Classifications Multi-output Classification
Different Models Combinations
Tree classifiers. Implementing an easy majority classifer Classifier
Conclusion
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