This is a Course Repository Where Components for Cource "Data Science and Deep Learning using Python" will be available.
Python Basic: An introduction to the basic concepts of Python. Learn how to use Python both interactively and through a script.
• Python installation
• Python interface
• Hello world
• Comment
• Variable & type
• Variable assignment
• Variable calculation
• Operation with different types
• Type conversion
Python Lists: Learn to store, access and manipulate data in lists: the first step towards efficiently working with huge amounts of data.
• What is list
• List creation
• List with different type
• List of list
• Subsetting list
• Subset and conquer
• Subset and calculate
• Slicing and dicing
• Subsetting list of list
• List manipulation
• Replace list elements
• Append extend
• Delete list elements
• Inner workings of list
Functions and Packages: To leverage the code that brilliant Python developers have written, you'll learn about using functions, methods and packages. This will help you to reduce the amount of code you need to solve challenging problems!
• Functions
• Familiar function
• Multiple arguments
• Methods
• String methods
• List methods
• Package
• Import package
• Selective import
• Different way of importing
• Numpy: Used for nearly all data crunching in Python.
• Pandas: A library for working with tables of data.
• Matplotlib: A library for plotting data.
NumPy: NumPy is a Python package to efficiently do data science. Learn to work with the NumPy array, a faster and more powerful alternative to the list, and take your first steps in data exploration.
• Numpy
• Numpy array
• Numpy side effects
• Subsetting Numpy array
• 2D Numpy array
• Implementation of 2D Numpy array.
• Subsetting 2D Numpy array.
• Example of 2D Numpy Array.
• First Analysis.
• 2D Arithmetic.
• Numpy: Statistics
• Average Vs. Median.
• Exploring Dataset.
• Implementation.
Pandas: we will be using pandas for special cause called data framing. A Data frame is a two-dimensional data structure, i.e., data is aligned in a tabular fashion in rows and columns.
• Create a Pandas DataFrame
• Select an Index or Column From a DataFrame
• Add an Index, Row or Column to a DataFrame
• Delete Indices, Rows or Columns From a DataFrame
• Rename the Columns or Indices of a DataFrame
• Format the Data in Your DataFrame
• Write a DataFrame to a File
Matplotlib: Matplotlib will be using for the basics Python data visualization: the anatomy of a plot, pyplot and pylab, and much more.
• matplotlib, plotting
• sub-plot, axes, figure.
• plot creation.
• plotting routine.
• plot customization.
• saving, showing, clearing plot.
Topic:
• Regression
• R-Square Theory
• k-nearest neighbor
• support vector
• SVM
• Clustering
• Recurrent Neural Network (RNN)
• Convolutional Neural Network
• TF-Learn
Starts From:
• Regression
• Regressions Features and Labels
• Regression Training and Testing
• Regression Forecasting and Predicting
• Pickling and Scaling
• Regression How it works
• How to program the best fit slop
• How to program the best fit line
• R-Squared Theory
• Programming R-Squared
• Testing Assumptions
• Classification w/k nearest neighbors
• K nearest neighbors applications
• Euclidean Distance
• Creating k nearest neighbors algorithm
• Writing k nearest neighbors in code
• Applying k nearest neighbors algorithm
• Final thoughts on k nearest neighbors
• Support Vector Machine intro and applications
• Understanding vectors
• Support vectors assertion
• Support Vectors Machine fundamentals
• Support Vectors Machine optimization
• Creating an SVM From scratch
• SVM Training
• SVM Optimization
• Completing SVM from scratch
• Kernel Introduction
• Soft Margin SVM
• Soft Margin SVM and Kernel with CVXOPT
• SVM Parameter
• Clustering introduction
• Handling Non-Numeric Data
• K-Means with titanic dataset
• Custom K-means
• Deep Learning with neural network and TensorFlow introduction
• Tensorflow Basic
• Neural Network model
• Running Network
• Processing own data
• Preprocessing Cont’d
• Training Testing our own data
• Recurrent Neural Network (RNN)
• Recurrent Neural Network (RNN) Example
• RNN Example in TensorFlow
• Convolutional Neural Network Basic
• Convolutional Neural network with tensorflow
• TF-Learn