Python For Data Science
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21 Jul 2018
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Registration for Python For Data Science
Python is a multi-paradigm programming language: a sort of Swiss Army knife for the coding world. It supports object-oriented programming, structured programming, and functional programming patterns, among others
Python is free, open-source programming, and therefore anybody can compose a library bundle to expand its usefulness. Information science has been an early recipient of these augmentations.
Pandas is the Python Data Analysis Library, utilized for everything from bringing in information from Excel spreadsheets to preparing sets for time-arrangement examination. Pandas puts basically every regular information munging device readily available. This implies fundamental cleanup and some propelled control can be performed with Pandas' intense dataframes.
Pandas is based over NumPy, one of the soonest libraries behind Python's information science example of overcoming adversity. NumPy's capacities are uncovered in Pandas for cutting edge numeric examination.
On the off chance that you require something more particular, odds are it's out there:
SciPy is what might as well be called NumPy, offering devices and procedures for investigation of logical information.
Statsmodels centers around devices for factual investigation.
Scilkit-Learn and PyBrain are machine learning libraries that give modules to building neural systems and information preprocessing.
|Class 1||Python introduction for absolute beginners: Installation setup, installing sublime and how to use, working with string,integers and floats. What are Lists, tuples, sets. How to use key-value pairs in python using dictionary. How to use conditional and boolean expression. How to make functions. How to use loops and iterations.|
|Class 2||Intermediate Python: Installing anaconda, how to virtual environment and how to use (virtualenv) and why should we use it, how to use conda. How to slice strings and list. How to use list comprehensions and why to use it. How to do string formatting. How to read and write in file. How to use generators. How to use decorators and why should we use it. How to use try and except error handling.|
|Class 3||Introduction to numpy and scipy: Benefits of numpy and scipy. Introduction to jupyter notebook and why should use it. All numpy operation. Basic arithmetic operations,Dot product, Trigonometric operation, vectors and matrices, doing matrix operations and solving linear system using numpy.Solving linear algebra word problem using numpy.|
|Class 4||Data manipulation using pandas and Data Visualization using matplotlib, ploty, and seaborns: Introduction to pandas and why should use it, how to read different file formats using pandas, to convert pandas DataFrames into different file formats, how add columns, renames , remove columns using pandas. How to sort Dataframe. How to handle missing values and howto remove duplicate. How to use inplace parameter. How to use group by.|
|Class 5||How to make histogram, pie charts, bar charts, scatter graph, line graph and linear regression using matplotlib, seaborn and plotly and explain which one should prefered. Introduction to linear regression and root mean squared error: Introduction to linear regression, mathematical explanation of it and where it is used. How to make a linear regression model using scikit-learn and use it for forecasting the stock prices.|
|Class 6||What is root mean squared errors and why it is used and how to program it. Introduction to K nearest neighbour classification: Explanation of euclidean distance and how KNN classification works. Make KNN classifier using scikit learn and make classification on a binary classification dataset.|
|Class 7||Introduction to K means clustering: Explanation of K Means clustering and why it is used and implementation using scikit learn on some real world dataset from kaggle. Introduction to dimensionality reduction: Principal Component Analysis, its explanation and code in python.|
|Class 8||Introduction to Support Vector Machine: Explanation of Support Vector Machine and why it is used and implementation using scikit learn on some real world dataset from kaggle. Introduction to Digital Image Processing.|
|Class 9||Image Processing using openCV: Introduction to opencv and its uses. How to images, videos using cv2. Image operation using cv2. Arithmetic and logic operation using cv2. Blurring and smoothing, edge detection, corner detection, template matching and object detection using cv2.|
|Class 10||Introduction neural network: Complete explanation of non-linearity and simple feed forward neural network.Introduction to keras framework and why it is very famous. How to build a NN using keras.|
|Class 11||Use neural network on finance data and get prediction on test data using keras. Introduction to Convolutional Neural Network: Complete explanation on convolution neural network and why and where it is used.|
|Class 12||Make a CNN model to classify cats and
dogs images using Keras.
Final Project Evaluation.
Tentative: Introduction to recurrent neural network and LSTMS cells: Complete explanation of recurrent neural network and LSTMS cells and where they are used. And Used LSTMLs cells to predict movie ratings using imdb movie review using Keras.