Python Training
KEAT Center provide training on Python Training for all types students. We provide real world advance training for our students which help them to achieve their goals and ready to move ahead.
Web Development With Python and Django
Python Programming
- Python version and pip package manager
- Introduction to Google Colab, Jupyter Notebook / IDE
- Python Program and statements
- Python Arithmetic Operators
- How to define a variable name and Variable Naming convention in Python
- Operator, Operands, and Operator Precedence
- Changing and updating variable values in Python
- Assigning multiple values to multiple variables
- Data types in Python
- Number data type: int, float, complex
- Conditions and Recursion
- Iteration
- Python string
- Python Built-in data types
- None type
- Python Functions
- OOP in python
- Introduction to Exceptions
- File Handling
- Others
- Introduction to SQL in python
- Introduction to git and Github
- Pandas
- Basic Data Visualization
- Project Work (one of the following )
Django Framework
- Django Views and URL confs
- Web designing using Bootstrap
- Django Templates
- Database
- Django Models
- Generic Views
- Models and Dynamic Webpages
- Django Forms
- Django Admin Site
- User Authentication in Django
- Django Sessions
- Django Middleware And Security
- RESTful API
- Serialization
- Parsing XML and JSON with Python
- Deployment
- Final Project
- Packages
Data Science with Python Training
Course Outline: Python Programming
- Python version and pip package manager
- Introduction to Google Colab, Jupyter Notebook / IDE
- Python Program and statements
- Python Arithmetic Operators
- How to define a variable name and Variable Naming convention in Python
- Operator, Operands, and Operator Precedence
- Changing and updating variable values in Python
- Assigning multiple values to multiple variables
- Data types in Python
- Number data type: int, float, complex
- Conditions and Recursion
- Iteration
- Python string
- Python Built-in data types
- None type
- Python Functions
- OOP in python
- Introduction to Exceptions
- File Handling
- Others
- Introduction to SQL in python
- Introduction to git and Github
- Pandas
- Basic Data Visualization
- Project Work (one of the following )
Data Science Course
- Introduction
- Data Science Tool Box
- Probability and Statistics
- Numpy
- Pandas
- Scipy and Seaborn
- Plotting, Charting & Data Visualization
- Tableau Basics
- Exploratory Data Analysis (EDA) and Hypothesis Testing
- Text Mining In Python
- MACHINE LEARNING INTRODUCTION
- Supervised Learning
- Unsupervised Machine Learning
- ML Web App development Streamlit
- Projects
Python with Artificial Intelligence (AI) Training
Python Programming
- Python version and pip package manager
- Introduction to Google Colab, Jupyter Notebook / IDE
- Python Program and statements
- Python Arithmetic Operators
- How to define a variable name and Variable Naming convention in Python
- Operator, Operands, and Operator Precedence
- Changing and updating variable values in Python
- Assigning multiple values to multiple variables
- Data types in Python
- Number data type: int, float, complex
- Conditions and Recursion
- Iteration
- Python string
- Python Built-in data types
- None type
- Python Functions
- OOP in python
- Introduction to Exceptions
- File Handling
- Others
- Introduction to SQL in python
- Introduction to git and Github
- Pandas
- Basic Data Visualization
- Project Work (one of the following )
Artificial Intelligence
- Introduction and installation
- Data Preprocessing
- Linear Regression
- Logistic regression
- Support Vector Machine (SVM)
- K-nearest neighbor
- Naive Bayes classifier
- Decision trees
- Time Series Modeling
- Text Mining
- ML Web App development Streamlit
- Recommender Systems
Deep Learning in Python
- Introduction and Prerequisite
- Math for Deep Learning
- Frameworks
- Introduction to Computer Vision
- Introduction to Deep Learning
- Artificial Neural Networks
- Convolution Neural Network
- Data Preprocessing
- Image Classification and Object Recognition
- Introduction to Natural Language Processing
- Natural Language Processing Basics
- Recurrent Neural Network (RNN)
- Use case of Natural Language Processing
- Autoencoders
- Deep Reinforcement Learning
- Project Works
Machine Learning with python training
Python Programming
- Python version and pip package manager
- Introduction to Google Colab, Jupyter Notebook / IDE
- Python Program and statements
- Python Arithmetic Operators
- How to define a variable name and Variable Naming convention in Python
- Operator, Operands, and Operator Precedence
- Changing and updating variable values in Python
- Assigning multiple values to multiple variables
- Data types in Python
- Number data type: int, float, complex
- Conditions and Recursion
- Iteration
- Python string
- Python Built-in data types
- None type
- Python Functions
- OOP in python
- Introduction to Exceptions
- File Handling
- Others
- Introduction to SQL in python
- Introduction to git and Github
- Pandas
- Basic Data Visualization
- Project Work (one of the following )
Machine Learning
- Introduction and installation
- Data Preprocessing
- Linear Regression
- Logistic regression
- Support Vector Machine (SVM)
- K-nearest neighbor
- Naive Bayes classifier
- Decision trees
- Clustering
- Reinforcement Learning
- Bonus
- Project Works
Deep Learning with Python Training
Python Programming
- Environment setup
- The python programming language
- What is program?
- What is debugging?
- Variables, Statement and Statements
- Function
- Condition and Recursion
- Fruitful Functions
- Iteration
- String
- List
- Dictionary
- Tuple
- Set
- Exception Handling
- Files
- CSV
- Pandas
- Database with Python
- Basic Data Visualization
- Class and Objects
- Classes and Methods
- Callable and Non-Callable Object
- Inheritance
- GIT
- Tools
- Bonus
- Final Project
AI – Deep Learning in Python
- Prerequisites for Deep Learning
- Frameworks
- Introduction to Computer Vision
- Image Processing and Feature Detection
- Introduction to Deep Learning
- Artificial Neural Networks
- Convolution Neural Network
- Data Preprocessing
- Image Classification and Object Recognition
- Generative Adversarial Network
- Introduction to Natural Language Processing
- Natural Language Processing Basics
- Deep Learning for NLP
- Use case of Natural Language Processing
- Deep Reinforcement Learning
- GPUs and Cloud Computing
- Case Study
- Project Works
Artificial Intelligence (AI) Training in Nepal
Artificial Intelligence
- Introduction
- Supervised vs. Unsupervised Learning
- Installing Anaconda and Managing Environment
- Familiarization with Datasets
- Numpy
- Scikit Learn
- Matplotlib
- Pandas
- Data Preprocessing
- Linear Regression
- Logistic regression
- Support Vector Machine (SVM)
- K-nearest neighbor
- Naive Bayes classifier
- Decision trees
- Time Series Modeling
- Text Mining
- Recommender Systems
Deep Learning in Python
- Introduction and Prerequisite
- Math for Deep Learning
- Frameworks
- Introduction to Computer Vision
- Introduction to Deep Learning
- Artificial Neural Networks
- Convolution Neural Network
- Data Preprocessing
- Image Classification and Object Recognition
- Introduction to Natural Language Processing
- Natural Language Processing Basics
- Recurrent Neural Network (RNN)
- Use case of Natural Language Processing
- Autoencoders
- Deep Reinforcement Learning
- Project Works