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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

• Understand the life cycle of a machine learning project - typical steps involved and tools that can be used in each step.

• Using machine learning algorithms to solve practical problems using libraries like scikit-learn and tensorflow.

• Fine tuning the algorithms through regularization, feature selection, and better models.

• Develop an understanding of evaluation of machine learning algorithms and decide the next steps based on the analysis.