Getting Started with Python
Introduction about the Python
Different Version of Python
Installing Python
Check the Python Installation
Python Command Line
Advantage of Using Python Command Line
Course Syllabus
Setting up the Development Environment
What is Integrated Development Environment?
Installing the IDE
Installing the Extension for the Python Programming
Exploring the IDE
Python Programming –Basic
Python Syntax and Code Block
Understanding Python Indentation
Commenting the Code in Python
Variables in Python
Defining the Variables
Data types in Python
String Data types
Numeric Data Types
Boolean Data Types
Operators in Python
Arithmetic Operators
Comparison Operators
Logical Operators
Function Introduction
Defining or creating the function
Code Reusability using function
Function Definition
Function Accessing
Passing Parameters to the Functions
Returning from the function
Function with default Parameters
Keyword Arguments and Positional Arguments
Control Statements introduction
Condition Introduction
If Statement in Python
If else statement in Python
If elif statement in Python
Nested statement in Python
Loops Introduction
For Loop
Break statement for Execution Control
Continue statement for Execution Control
Pass statement for Execution Control
While statement
Nested Loop Statements
Conditions inside the loops
Exception handling introduction
When Error Occurs
Error Types
Catching Exceptions
Basic exception handling
Nested exception handling
Custom Exception Creations
Finally Clause
Python Programming -Intermediate
Package Introduction
Package types
Package cache
Modules
Importing the package
Import statement in python
From import statement
Finding Package on Disk
Downloading third-party Package
Opening anaconda prompt
Working with anaconda prompt
Installing package using pip
Viewing the Package Content
Package Documentation
List Introduction
Creating List
Accessing List
Looping through Lists
Modifying Lists
Searching Lists
Sorting Lists
Printing Lists
List Methods
Understanding Collections
Working with Tuples
Working with Dictionaries
Creating and Using Dictionary
Creating Stacks using Lists
Working with queues
Working with queues
Python Programming –Advance
Class Introduction
Class Packaging method
Class Definition
Built-in Class Definition
Object Instance
Inheritance
Multiple Inheritance
Private Variables
Working with methods
Working with constructors
Working with properties
Python Libraries
Python Standard Library
Math Module Function
Data Science Library
Import : A Deeper Look
Using IPython
Using Jupyter
Introduction to Data Science
Basic Descriptive Statistics
Measures of Central Tendency
Mean, Median and Mode
Measure of Dispersion
Simulation and Static Visualization
Sample Graph
Visualizing the Frequencies and Percentage
Dynamic Visualization
How Dynamic Visualization works
Implementing a Dynamic Visualization
Pandas, Regular Expressions and Data Munging
NumPy –The Python Package
NumPy Introduction
Creating Array
Array Attributes
Filling Arrays with Specific Values
Creating Arrays from Ranges
List and Array Performance
Array operators
NumPy Calculation Methods
Universal Functions
Indexing and Slicing
Shallow Copies
Deep Copies
Reshaping and Transposing
Pandas Series and Data Frames Introduction
String Operations
String
Formatting String
Presentation Types
Field widths and Alignment
Numeric Formatting
String’s Format Method
Concatenating and Repeating Strings
Changing Character Case
Comparison Operators for Strings
Searching for Substrings
Replacing Substrings Splitting and Joining Strings
Character and Character Testing Methods
Raw String
Regular Expressions Introduction
Re Module and Function full match
Replacing Substrings and Splitting Strings
Other Search functions, Accessing Matches
Working with CSV files
Python Standard Library Module CSV
Reading CSV files into Pandas Data Frames
Simple Data Analysis with Titanic Disaster Dataset
Passenger Age Histogram
Data Structure and Algorithms
Introduction Factorials
Recursive
Factorial Recursive
Fibonacci Series
Recursion vs Iteration
Searching and Sorting
Linear Search
Efficiency of Algorithms Binary Search
Binary Search Implementation
Big O of the Binary Search
Sorting Algorithm
Selection Sort
Selection Sort Implementation
Insertion Sort
Merge Sort
Searching and Sorting Algorithms
Visualizing Algorithms
Generator Functions
Implement the Selection Sort Animation
Natural Language Processing(NLP)
Introduction
TextBlob
Creating a TextBlob
Tokenizing Text into Sentences and Words
Parts of Speech Tagging
Extracting Noun Phrases
Sentiment Analysis
Language Detection and Translation
Pluralization and Singularization
Spell Checking and Correction
Normalization Word Frequencies
n-grams
Bar Chart and Word Clouds
Natural Language Datasets
Machine Learning Classification, Regression and Clustering
Introduction to Machine Learning
Scikit Learn
Types of Machine Learning
Datasets Bundled with Scikit Learn
Classification with K-Nearest Neighbors and Digits Dataset
k-nearest Neighbours Algorithm
Loading the Dataset
Visualizing the Data
Splitting the Data for Training and Testing
Creating the Model
Training the Model
Predicting Digit Classes
Classification with k-Nearest neighbors and Digits Dataset
Metrics for Model Accuracy
K-Fold Cross-Validation
Running Multiple Models to Find the Best
Hyperparameter Tuning
Time Series and Simple Linear Regression
Multiple Linear Regression
Loading the Dataset
Exploring the Data with Pandas
Visualizing the Features
Splitting the Data for Training and Testing
Training the Models
Testing the Model
Regression Model Metrics
Choosing the Best Models
Unsupervised Machine Learning
Dimensionality Reduction
k-Means Clustering
Loading the Iris Dataset
Descriptive Statistics with Pandas
Visualizing the Dataset
K Means Estimator
Reduction with Principal Component Analysis
Choosing the Best Clustering Estimator
Deep Learning
Deep Learning Introduction
Deep Learning Application
Deep Learning Demo
Keras Resources
Keras Built-in Datasets
Custom Anaconda Environments
Neural Networks
Tensors
Tuning Deep Learning Models
Reinforcement Learning
Deep Q-Learning
Parthipan
Data Science Trainer
Data Science Training Expert worked as Data Scientist for five years, he is an expert in Python, R, Machine Learning, Data Visulaisation, Deep Learning, NLP. He is good at mentoring for career guidance
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