We’ll know data science and the road map of these courses.
We’ll know more about useful commands in Linux.
Linux Bash Shell Cheat Sheets
We need to learn a programming language. Python is our recommendation.
We’ll begin to work with python.
Any python tutorial is fine!
Python interactive development environments.
We’ll know about Anaconda and pandas.
We’ll learn about simple statistical analysis on some real data.
Statistical inference, significant difference, CL estimation, developing and evaluating hypotheses, probability, best fitting, correlations
preliminary statistical analysis to mine a data set, How programming languages are able to deal with analytical mathematics
An Introduction to Statistical Learning
Bayesian inference (from session 5), how we are able to analyze sound/image/video
Introduction to Time Series Forecasting
OOP, ML concepts, features, classification, regression, overfitting problem, regulators
Introduction to Machine Learning with Python
Cross validation, hyper parameter optimization, how we are able to how to manage and track progress of a project. What will happen after project delivery?
You’ll learn about data visualization and presentation importance and related techniques.
Search about matplotlib, seaborn, bokeh, plotly and dash.
Perceptron, CNN, GD, SGD
Basics; train, save, restore, tune up a model
RNNs, autoencoders, variational autoencoders, GANs
Azadi St., intersection of Dr. Gharib, No. 134