Machine learning of themachineapparatusconfigurationpractice.pdf
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- 4. Lesson Plan Week 1 Introduction to Machine Learning Overview of machine learning, types of machine learning algorithms, & supervised learning. Outcome-driven Project: Students will work on a supervised learning project using scikit-learn during the live session.
- 5. Linear Regression: Simple linear regression, multiple linear regression, and model evaluation. Outcome-driven Project: Students will work on a linear regression project using scikit-learn during the live session. Classification: Logistic regression, K-Nearest Neighbors, & model evaluation. Outcome-driven Project: Students will work on a classification project using scikit-learn during the live session.
- 6. Decision Trees: Introduction to decision trees, Gini index, and Information gain. Outcome-driven Project: Students will work on a decision tree project using scikit-learn during the live session. Random Forest: Introduction to random forests, bagging, & boosting. Outcome-driven Project: Students will work on a random forest project using scikit-learn during the live session.
- 7. Unsupervised Learning: Introduction to unsupervised learning, clustering algorithms, and K-Means clustering. Outcome-driven Project: Students will work on an unsupervised learning project using scikit-learn during the live session. Dimensionality Reduction: Introduction to principal com ponent analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (t-SNE). Outcome-driven Project: Students will work on a dimensionality reduction project using scikit-learn during the live session. Week 2
- 8. Support Vector Machines (SVM) : Introduction to SVM, kernel functions, & model evaluation. Outcome-driven Project : Students will work on an SVM project using scikit-learn during the live session. Neural Networks: Introduction to neural networks, backpropagation algorithm, & activation functions. Outcome-driven Project : Students will work on a neural network project using TensorFlow during the live session.
- 9. Deep Learning: Introduction to deep learning, convolutional neural networks (CNNs), Recurrent neural networks (RNNs). Outcome-driven Project (1 hour): Students will work on a deep learning project using TensorFlow during the live session. Natural Language Processing (NLP): Introduction to NLP, text preprocessing, and bag-of-words model. Outcome-driven Project : Students will work on an NLP project using scikit-learn during the live session. Week 3
- 10. Time Series Analysis: Introduction to time series data, time series decomposition, and ARIMA model. Outcome-driven Project : Students will work on a time series analysis project using statsmodels during the live session.. Ensemble Learning: Introduction to ensemble learning, bagging, boosting, and stacking. Outcome-driven Project : Students will work on an ensemble learning project using scikit-learn during the live session. Model Deployment: Introduction to model deployment, Flask, and Heroku. Outcome-driven Project : Students will deploy one of their previous projects to Heroku during the live session. >>>
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