WEEK 1 - End-to-end machine learning project on scikit-learn
WEEK 2 - End-to-end machine learning project on scikit-learn (continued)
WEEK 3 - Regression on scikit-learn - Linear regression Gradient-descent- Batch (MBGD) and Stochastic (SGD).
WEEK 4 - Polynomial regression, Regularized models
WEEK 5 - Logistic regression
WEEK 6 - Classification on scikit-learn - Binary classifier
WEEK 7 - Classification on scikit-learn - Multiclass classifier
WEEK 8 - Support Vector Machines using scikit-learn
WEEK 9 - Decision Trees using scikit-learn
WEEK 10 - Ensemble Learning and Random Forests using scikit-learn
WEEK 11 - Clustering using scikit-learn
WEEK 12 - Neural networks models in scikit-learn
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Understand the life cycle of a machine learning project - typical steps involved and tools that can be used in each step.
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Using machine learning algorithms to solve practical problems using libraries like scikit-learn and tensorflow.
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Fine tuning the algorithms through regularization, feature selection, and better models.
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Develop an understanding of evaluation of machine learning algorithms and decide the next steps based on the analysis.