TRAININGS - Joworkspaces

Getting Started with Python
Installation
Basic Python syntax
Type Variables and Operators
Variables
MAGIC FUNCTIONS
Operators
Strings
Python strings
Slicing for substrings
Python string methods
String functions
Tuple
Tuple Functions
Operations of tuples
Lists
Creating a list
List Operations
list functions
list methods
list comprehensions
Dictionary
Operations on the dictionary
Dictionary Functions
Dictionary Methods
Python dictionary with for loop
Control Statements and Loops
The if and if..else statement
The if..elif..else statement
Loops
Functions
Pass by reference versus pass by value
Scope of variables
Modules and Packages
The import statement
Locating Python modules
Compiled Python files
The Python package
File Handling and Exceptions
Reading text from a file
Writing text to a file
Pickiling
Unpickling
Exceptions
Data visualization using Pyplot
Line Chart
Pie Chart
Bar Chart
Collections
Class and Objects
Object-oriented programming overview
Instance variables
The __init__ method
Class variables
Class Inheritance
Overriding methods
Operator Overloading
The class method
The static method
The private variable
Preparing the Data
Reading and writing CSV/TSV files with Python
Reading and writing JSON files with Python
Reading and writing Excel files with Python
Retrieving HTML pages with pandas
Imputing missing observations
Normalizing and standardizing the features
Exploring the Data
Exploring correlations between features
Producing histograms
Sampling the data
Splitting the dataset into training, cross-validation, and testing
Classification Techniques
Testing and comparing the models
Classifying with Naïve Bayes
Using logistic regression as a universal classifier
Utilizing Support Vector Machines as a classification engine
Classifying calls with decision trees
Predicting subscribers with random tree forests
Clustering Techniques
Assessing the performance of a clustering method
Clustering data with k-means algorithm
Discovering clusters with mean shift clustering model
Reducing Dimensions
Reducing the dimensions using the kernel version of PCA
Extracting the useful dimensions using Linear Discriminant Analysis
Regression Methods
Employing the kNN model in a regression problem
Time Series Techniques
Handling date objects in Python
Understanding time series data
Smoothing and transforming the observations
Filtering the time series data
Removing trend and seasonality
Forecasting the future with ARMA and ARIMA models
Graphs
Introduction
Handling graph objects in Python with NetworkX
Identifying people whose credit card details were stolen
Identifying those responsible for stealing the credit cards