PYTHON FULL STACK COURSE TRAINING IN HYDERABAD

About LearnAi.co.in

LearnAI.co.in is the premier training institute for Python full stack courses, catering to individuals at all skill levels. Our comprehensive curriculum is tailored to meet the unique needs of beginners, intermediates, and experts alike. Beginners will benefit from our foundational Python training, while intermediates can build on their skills with advanced topics. For experts, we offer specialized, cutting-edge content to keep them at the forefront of the field. Join LearnAI.co.in today to unlock your full stack potential and excel in the world of Python development.

Upcoming Batches

Course	Branch	Training Type	Week	Day
Python Full Stack	Dilsukhnagar	Hybrid	1st	Wednesday
Python Full Stack	Dilsukhnagar	Hybrid	2nd	Monday
Python Full Stack	Dilsukhnagar	Hybrid	3rd	Wednesday
Python Full Stack	Dilsukhnagar	Hybrid	4th	Monday

What is Python Full Stack?

Python full stack is a versatile and powerful way of programming that covers both the front-end and back-end of a website. It enables developers to create entire web applications, handling everything from the user interface to the server logic, making it an essential skill for modern web development.

Why to learn Python Full Stack?

Learning Python full stack is a strategic career move due to the immense demand for full stack developers in the job market. With proficiency in both front-end and back-end development, you can unlock a world of opportunities and significantly boost your career growth in the ever-evolving tech industry.

Who can Pursue Python Full Stack?

Any Degree pursuing or Graduated,
 Bachelor’s, Master’s, PhD, and
 Anyone interested in course

Enroll to Course Now

COURSE CURRICULUM

Python

Introduction
 Installation
 Fundamental of Python
 Variables
 Comments
 Print Statement
 Operators
 Mutable Data Types
Data Types
Special Data Types
Conditions
Loops
Functions
Break, Continue and Pass Statements
String Object and working
List
Tuple
Set
Dictionaries
Map
Reduce
Filter
Classes
Objects
Inheritance
Multiple Inheritance
Modules
Error Handling

Data Visualization

Matplotlib
 Seaborn
 Plotly
 Cuflinks
 Bokesh

Libraries

Numpy
Pandas
Random
Math
Scipy
sklearn
Keras
Tensorflow
OpenCV
NLTK
Spacy
Lot more…

Tableau

Introduction to Data Visualization
Introduction to Tableau
Basics charts and dashboards
Special Char Types
Dashboard design and principles
Connections with servers
Local file access
Hands on experience with worksheet

Web Scraping

 Url
 Beautiful Soup

Data base

 SQL
 MongoDB

Statistics

Basics of Statistics
Descriptive Statistics
Inferential Statistics
Qualitative Analysis
Quantitative Analysis
Hypothesis Testing
Data Distribution
Probability Distribution
Normal Distribution
Poison Distribution
Outlier Detection
Other Statistical Fundamentals

Machine Learning

Supervised Learning
Unsupervised Learning
Semi Supervised Learning
Reinforcement Learning

Supervised Learning

 Regression Models
Classification Models

Regression Models

Introduction to Regression Models
Linear Models
Linear Regression
Multiple Regression
Ordinary Least Square Method
Non-Linear Models
Support Vector Regressor
Random Forest Regressor
Decision Tree

Evaluation Metrics for Regression Models

R-Square
 Adjusted R Square
 Mean Square Error
Root Mean Square Error
Mean Absolute Error

Classification Models

Introduction to Classification Models
 Logistics Regression
 Naïve Bayes
 Support Vector Classifier
 K-NN Classifier
 Decision Tree Classifier
 Random Forest Classifier

Evaluation Metrics for Classification Models

Accuracy
Precision
Recall
F1 Score
ROC Curve

Unsupervised Learning

Introduction to Unsupervised Learning

Clustering Models

 Introduction to Clustering Models
 K- Mean Clustering
 Hierarchical Clustering
High Dimensional Clustering

Dimension Reduction

Principal Component Analysis (PCA) Reinforcement Learning

Reinforcement Learning

Introduction to Reinforcement Learning

Featurization, Model Selection & Tuning

Feature Extraction
Model Defects & Evaluation Metrics
Model Selection and Tuning
Comparison of machine learning models

Neural Networks

Introduction to Deep Learning
Fundamental of Neural Networks
TensorFlow and Keras
Artificial Neural Networks (ANN)
Convolution Neural Networks (CNN)
Recurrent Neural Networks (RNN)
Evaluation of Deep Learning
Neural Networks Basics
Gradient Descent
Introduction to Perceptron & Neural Networks
Batch Normalization
Activation and Loss Functions
Hyper parameter tuning
SoftMax
Deep Neural Networks
Weights initialization

Image Pre-processing

Computer Vision
Open CV
Noise Detection
Noise Reduce
Low Pass Filters
Forward Propagation
Backward Propagation
Pooling & Padding

Natural Language Processing

Introduction to NLP
 Corpus
 Natural Language Understanding (NLU)
 Natural Language Generator (NLG)
Tokenization (Word, Sentence, Blank, RegEx)
Frequency Distribution
Filtering
Stemming
Lemmatization
Stop Words
Regular Expressions
 POS Tagging
 Syntax Tree
 Chunking
 Lemmas
 Hypernyms
 Hyponyms
 Synonyms
 Antonyms
 Distance Between words
Wordnet
 Name Entity Recognition
 Bag of words or Document Matrix
 Count Vectorization
 Term Frequency
 Inverse Document Frequency
 Sentimental Analysis

Job Roles

Python Full Stack Developer
Web Application Developer
Software Engineer
DevOps Engineer
Data Scientist
Automation Engineer
IT Consultant
Technical Project Manager
UI/UX Designer
Freelance Developer

Components of Python Full Stack

Front-End Technologies
Back-End Technologies
Version Control
Development Tools
Database Management
API Testing Tools
Web Servers
Deployment and Containerization
Front-End Libraries
Task Runners and Build Tools
Testing Frameworks
Cloud Services
Security and Authentication
Continuous Integration/Continuous Deployment (CI/CD)
Web Application Frameworks
Content Management Systems (CMS)
Agile and Project Management Tools