Day 1 & Day 2:

Python language fundamentals, basic syntax, data types, lists, dictionaries, sets, tuples

Day 3:

Dataframe library, Pandas (data manipulation)

Day 4:

matplotlib (2D plotting & charting)

Day 5:

Pandas advanced groupby, case study on sample datasets, Basic Data Exploration, Basic Statistics

Day 6:

(Normalization) Variable scaling using min-max, standardization, L1 and L2 vector normalization and robust interquartile Least-Squares Linear Regression, Basic cluster analysis, Osmnx overview, Osmnx network analysis

Day 7:

Machine learning: Linear regression, Logistic regression, Naive bayes, Support Vector machine, Decision tree, Overfitting and underfitting concept, Unsupervised clustering techniques, Feed-forward neural network

Day 8:

Feed-forward neural network for classification problem, Dropout for reducing overfit neural network, Regularization for reducing overfit neural network, Batch normalization neural network, Recurrent neural network, Convolutional neural network, Bayesian optimization for choosing neural network hyperparameters

Day 9 & 10:

Full Use Cases with Python