# SUMMER INTERNSHIPS

- MACHINE LEARNING
- ARTIFICIAL INTELLIGENCE
- DEEP LEARNING
- PYTHON WEB DEVELOPMENT
- EMBEDDED SYSTEMS
- VLSI
- IOT
- Data science Internship Program
- Image processing Internship Program
- Java Internship Program
- Mathlab Internship Program
- Natural language processing Internship Program
- Web development Internship Program
- Summer Internship 2022
- Summer Internship 2023
- Summer Internship B.Tech
- Summer Internship for ECE Students
- Summer Internship for CSE Students
- Online Summer Internship

COMPLETE TRAINING ON TECHNOLOGY | PROJECT DEVELOPEMENT

#### Training Fee

One Day Internship : Rs. 3,500/-

45 Days Internship : Rs. 6,500/-

May June July

#### Contact Us

MANOJ: +91 9676190678

#### HYDERABAD

407, 4th Floor, Pavani Prestige (R.S Brothers)Building, Ameerpet, Hyderabad, India Opposite Image Hospital & Beside KLM Fashion Mall.

## Registration Process

## About Summer Internship

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## Internship Tracks

## Machine Learning

##### Day - 1: Introduction to Machine Learning

2. How Machine Learning Useful in Daily Life

3. Machine Learning Goals and Deliverables.

4. Why Machine Learning

5. Machine Learning Tools.

##### Programming Essentials

##### Day - 2: Introduction to Python

2.Anaconda Installation and Introduction to Jupyter Notebook

##### Day - 3: Python Basics

##### Day - 4: Python Baiscs

##### Day - 5: Python Baiscs

##### Day - 6: Python for Data Science - Numpy

2. Operations in Numpy

##### Day - 7: Python for Data Science - Pandas

2. Operations in Pandas – Pandas Basics, Indexing and selecting Data,Merge and Append, Grouping and Summarizing, Lambda functions and Pivot tables

3. Introduction to Reading.

##### Day - 8: Python for Data Science - Matplotlub

2. Types of plots with ExamplesInheritence,Polymorphism,Encapsualtion,Abstraction

##### Day - 9: Introduction to SQL

2. Basics of SQL, Data Retrieval, sorting, compound functions and relational operators, pattern matching with wild cards.

3. Basics on Table creation, updating, modifying etc.

4. Overall Structure of data retrieval queries, Merging tables, User Defined Functions (UDF), Frames.

##### Statistics & Exploratory Data Analysis (EDA)

##### Day - 10: Introduction to Data Analytics

2. CRISP-DM Framework – Data Preparation, Modelling, Evaluation and Deployment

##### Day - 11: Data Visualization in Python

2. Introduction to various charts

3. Data visualization toolkit in Python (Libraries or modules available in Python)

4. Plotting Data in Python using matplotlib and seaborn – Univariate Distributions, Bi-variate Distributions

5. Plotting Time series data

##### Day - 12: Exploratory Data Analysis

2. Data Cleaning – Fixing rows and columns, Missing value Treatment, standardizing values, handling invalid values, Filtering data

##### Day - 13: Exploratory Data Analysis

2. Univariate Analysis, Bivariate Analysis, Segmented univariate Analysis

3. Derived Metrics and Feature Engineering

##### Day - 14: Exploratory Data Analysis

2. Identify Outliers

3. Outliers Handling using Imputation Techniques

##### Day - 15: Inferential Statistics

2. Discrete and Continuous Probability Distributions

3. Central Limit Theorem – Introduction and Industrial applications

##### Day - 16: Hypothesis Testing

2. Concepts of Hypothesis Testing – p value method, critical value method

3. Types of Errors, T Distribution, other types of tests

4. Industry Demonstration and A/B Testing

##### Day - 17: Case Study

2. GDP EDA Analysis

##### Machine Learning - I

##### Day - 18: Introduction to Machine Learning

##### Day - 19: Simple Linear Regression

2. Assumptions of Linear Regression (LINE)

3. Cost Functions, Strength of Linear relationship – OLS, coefficient of correlation, coefficient of Determination

4. Intuition to Gradient Descent for optimizing cost function

5. Hypothesis Testing in Linear Regression

6. Building a Linear Model – Reading Data, Cleaning Data, Libraries available – Sklearn, Statsmodel.api

7. Model Building using Sklearn and Training and Test Data, Model Development, Model validation using Residual Analysis, Evaluation against the test Data

##### Day - 20: Multiple Linear Regression

2. Introduction to overfitting, Multi-collinearity

3. Dealing with Categorical variables – OHE, Dummies, Label Encoding

4. Building the model using statesmodel.api and importance of p-values

5. Model Evaluation Metrics – Coefficient of Determination, Adjusted R2, RMSE, AIC, BIC and other model evaluation Metrics

6. Variable Selection – RFE, Step wise selection etc.

7. Gradient Descent and Normal Equation for Multiple Linear Regression

8. Industry Demonstration: Linear Regression Case Study

##### Day - 21: Logistic Regression

2. Binary classification using univariate logistic regression

3. Maximum Likelihood function, Sigmoid Curve and Best Fit

4. Intuition of odds and log-odds

5. Feature selection using RFE

6. Model evaluation – Confusion Matrix and Accuracy

7. Why Accuracy is not Enough and introduction to sensitivity, specificity, precision, recall, area under curve

8. Logistic Regression Case Study

##### Day - 22: Unsupervised Learning:Clustering

1. Understanding clustering with practical examples

2. KMeans Clustering – understanding the algorithm

3. Practical consideration for KMeans Clustering – Elbow curve, silhouette metric and hopkings test for clustering tendency of data, impact of outliers

##### Day - 23: Unsupervised Learning

1. Hierarchical clustering Algorithm

2. Interpreting the dendogram and Types of Linkages

3. Comparison of Kmeans clustering and Hierarchical clustering – advantages and disadvantages

##### Day - 24: Unsupervised Learning:Principal Component Analysis(PCA)

2. Variance as information and basis transformation of vectors

3. Singular Value Decomposition and Identifying optimum principal components using scree plots

4. Model building with PCA

5. Advantages of PCA and Limitations

##### Machine Learning - II

##### Day - 25: Support Vector Machine Algorithm

1. Introduction to SVM and How does it works.

2. Advantages and Disadvantages of SVM

3. Kernal Functions in used in SVM

4. Applications of SVM

5. Implementation of SVM using Python

##### Day - 26: K Nearest Neighbors Algorithm

1. Introduction to KNN and How does it works.

2. Advantages and Disadvantages of KNN

3. Applications of KNN

4. Implementation of KNN using Python

##### Day - 27: Naive Bayes Algorithm

1. Intoduction to Naive Bayes

2. Advantage and Disadvantage of Naive Bayes

3. Applications of Naive Bayes

4. Implementation of Naive Bayes using Python

##### Day - 28: Tree Models

1. Introduction to decision trees and Interpretation

2. Homogeneity measures for splitting a node 1. Gini Index 2. Entropy 3. R2

3. Understanding Hyper parameters – Truncation and Pruning

4. Advantages and Disadvantages

Random Forest:

1. Introduction to ensembling, bagging and intuition

2. Random Forest – Introduction and Hyperparamters

3. Building a model using Random Forest

4. Hyper-parameters impact on model and tuning them

5. Importance of predictors using Random Forrest

##### Day - 29: Boosting

2. Introduction to Boosting Algorithms : XGBoost, lightGBM, Catboost

3. Advantages of Boosting Algorithms

4.XGBoost Model Building and importance of various Hyper parameters

5. Hyper-parameter tuning for XGBoost

##### Day - 30: Case Study

##### Day - 31: Case Study

##### Day - 32: Time Series

2. Trend and seasonality

3. Decomposition

4. moothing (moving average)

5. SES, Holt & Holt-Winter Model

##### Day - 33: Time Series

2. IADF, Random walk and Auto Arima

##### Day - 34: Text Mining

2. Text cleaning, regular expressions, Stemming, Lemmatization

3. Word cloud, Principal Component Analysis, Bigrams & Trigrams

4. Text classification, Document vectors, Text classification using Doc2vec

##### Day - 35: Case Study

##### Day - 36: Project Development

##### Day - 37: Project Development

##### Day - 38: Project Development

##### Day - 39: Project Development

##### Day - 40: Project Development

##### Day - 41: Project Development

##### Day - 42: Project Development

##### Day - 43: Project Development

##### Day - 44: Project Development

##### Day - 45: Project Development

## Artificial Intelligence

##### Day - 1 Introduction to Artificail Intelligence

**Introduction to Python**

1.Importance of Artifical Intelligence and Use Cases

2.Differnce betwwen AI, Data Science, Machine Learning and Deep Learning

##### Programming Essentials

##### Day - 2: Introduction to Python

2.Data Structures in Python (Lists, Tuples, Dictionaries, sets)

##### Day - 3: Introduction to Python

2. Introuction to OOPS

##### Day - 4: Python for Data Science

2. Introduction to Pandas and Operations in Pandas – Pandas Basics, Indexing and selecting Data, Merge and Append, Grouping and Summarizing, Lambda functions and Pivot tables

3. Introduction to Reading and Cleaning Data

##### Day - 5: Introduction to SQL

2. Basics of SQL, Data Retrieval, sorting, compound functions and relational operators, pattern matching with wild cards.

3. Basics on Table creation, updating, modifying etc.

4. Overall Structure of data retrieval queries, Merging tables, User Defined Functions (UDF), Frames.

##### Statistics & Exploratory Data Analysis (EDA)

##### Day - 6: Introduction To Data Analytics

2. CRISP-DM Framework – Data Preparation, Modelling, Evaluation and Deployment

##### Day - 7: Data Visualization in Python

2. Introduction to various charts

3. Data visualization toolkit in Python (Libraries or modules available in Python)

4. Plotting Data in Python using matplotlib and seaborn – Univariate Distributions, Bi-variate Distributions

5. Plotting Time series data

##### Day - 8: Exploratory Data AnalysisPurpose of IoT Gateway

2. Data Cleaning – Fixing rows and columns, Missing value Treatment, standardizing values, handling invalid values, Filtering data

3. Data types – Numerical, Categorical (ordered and unordered)

4. Univariate Analysis, Bivariate Analysis, Segmented univariate Analysis

5. Derived Metrics and Feature Engineering

6. Introduction to Outliers and their handling

##### Day - 9: Inferential Statistics

2. Discrete and Continuous Probability Distributions

3. Central Limit Theorem – Introduction and Industrial applications

##### Day - 10: Hypothesis Testing

2. Concepts of Hypothesis Testing – p value method, critical value method

3. Types of Errors, T Distribution, other types of tests

4. Industry Demonstration and A/B Testing

##### Machine Learning - I

##### Day - 11: Introduction to Machine Learning

##### Day - 12: Simple Linear Regression

2. Assumptions of Linear Regression (LINE)

2. Cost Functions, Strength of Linear relationship – OLS, coefficient of correlation, coefficient of Determination

3. Intuition to Gradient Descent for optimizing cost function

4. Hypothesis Testing in Linear Regression

5. Building a Linear Model – Reading Data, Cleaning Data, Libraries available – Sklearn, Statsmodel.api

6. Model Building using Sklearn and Training and Test Data, Model Development, Model validation using Residual Analysis, Evaluation against the test Data

##### Day - 13: Multiple Linear Regression

2. Introduction to overfitting, Multi-collinearity

3. Dealing with Categorical variables – OHE, Dummies, Label Encoding

4. Building the model using statesmodel.api and importance of p-values

5. Model Evaluation Metrics – Coefficient of Determination, Adjusted R2, RMSE, AIC, BIC and other model evaluation Metrics

6. Variable Selection – RFE, Step wise selection etc.

8. Gradient Descent and Normal Equation for Multiple Linear Regression

7. Industry Demonstration: Linear Regression Case Study

##### Day - 14: Model Selection and Best Practices

2. Cross Validation and how to avoid overfitting

3. Hyper parameter tuning using GridSearchCV, RandomSearchCV and other libraries

##### Day - 15: Logistic Regression

2. Binary classification using univariate logistic regression

3. Maximum Likelihood function, Sigmoid Curve and Best Fit

4. Intuition of odds and log-odds

5. Feature selection using RFE

6. Model evaluation – Confusion Matrix and Accuracy

7. Why Accuracy is not Enough and introduction to sensitivity, specificity, precision, recall, area under curve

8. Logistic Regression Case Study

##### Day - 16: unsupervised Learning:Means Clustering

1. Understanding clustering with practical examples

2. KMeans Clustering – understanding the algorithm

3. Practical consideration for KMeans Clustering – Elbow curve, silhouette metric and hopkings test for clustering tendency of data, impact of outliers

##### Day - 17: unsupervised Learning:Hierarchical Clustering

1. Hierarchical clustering Algorithm

2. Interpreting the dendogram and Types of Linkages

3. Comparison of Kmeans clustering and Hierarchical clustering – advantages and disadvantages

##### Day - 18: unsupervised Learning:Principal Component Analysis(PCA)

2. Variance as information and basis transformation of vectors

3. Singular Value Decomposition and Identifying optimum principal components using scree plots

4. Model building with PCA

5. Advantages of PCA and Limitations

##### Machine Learning - II

##### Day - 19: Tree Models

**Decision Trees:**

1. Introduction to decision trees and Interpretation

2. Homogeneity measures for splitting a node 1. Gini Index 2. Entropy 3. R2

3. Understanding Hyper parameters – Truncation and Pruning

4. Advantages and Disadvantages

**Random Forest:**

1. Introduction to ensembling, bagging and intuition

2. Random Forest – Introduction and Hyperparamters

3. Building a model using Random Forest

4. Hyper-parameters impact on model and tuning them

5. Importance of predictors using Random Forrest

##### Day - 20: Boosting

2. Adaboost Algorithm – Understanding and Model Building

3. Understanding Gradient Boosting

4. Introduction to Boosting Algorithms : XGBoost, lightGBM, Catboost

5. Advantages of Boosting Algorithms

6.XGBoost Model Building and importance of various Hyper parameters

7. Hyper-parameter tuning for XGBoost

##### Day - 21: Other Models

##### Day - 22: Time Series

##### Day - 23: Text Mining

##### Deep Learning

##### Day - 24: Introuction

2.Neural Networks Basics

##### Day - 25: Neural Networks

##### Day - 26: Neural Networks

##### Day - 27: Neural Networks

##### Day - 28: Neural Networks

##### Day - 29: Reinforcement Learning

##### Natural Language Processing

##### Day - 30: Introduction

2. NLP tasks in syntax, semantics, and pragmatics.

3.Applications such as information extraction, question answering, and machine translation.

##### Day - 31: NLP

2.Part Of Speech Tagging and Sequence Labeling

Day – 32: NLP

##### Day - 32: NLP

2. LSTM Recurrent Neural Networks

##### Day - 33: NLP

2.Semantic Analysis

##### Big Data

##### Day - 34: Introduction to Big Data storage and Analytics

2. Big Data Storage and processing framework – Hadoop

##### Day - 35: Hive , sqoop and Spark

2.Big Data processing using Apache Spark

##### Day - 36: Project Development

##### Day - 37: Project Development

##### Day - 38: Project Development

##### Day - 39: Project Development

##### Day - 40: Project Development

##### Day - 41: Project Development

##### Day - 42: Project Development

##### Day - 43: Project Development

##### Day - 44: Project Development

##### Day - 45: Project Development

## Deep Learning

##### Day - 1: Introduction to Machine Learning & Deep Learning

Diffenrence between Machine and Deep Learning

How Deep Learning Useful in Daily Life

Deep Learning Goals and Deliverables.

Why Deep Learning

Deep Learning Tools.

##### Programming Essentials

##### Day - 2: Introduction to Python

2.Anaconda Installation and Introduction to Jupyter Notebook

##### Day - 3: Python Basics

##### Day - 4: Python Baiscs

##### Day - 5: Python Baiscs

##### Day - 6: Python for Data Science - Numpy

2. Operations in Numpy

##### Day - 7: Python for Data Science - Pandas

2. Operations in Pandas – Pandas Basics, Indexing and selecting Data,Merge and Append, Grouping and Summarizing, Lambda functions and Pivot tables

3. Introduction to Reading.

##### Day - 8: Python for Data Science - Matplotlub

2. Types of plots with Examples

##### Day - 9: Introduction to SQL

2. Basics of SQL, Data Retrieval, sorting, compound functions and relational operators, pattern matching with wild cards.

3. Basics on Table creation, updating, modifying etc.

4. Overall Structure of data retrieval queries, Merging tables, User Defined Functions (UDF), Frames.

##### Statistics & Exploratory Data Analysis (EDA)

##### Day - 10: Introduction to Data Analytics

2. CRISP-DM Framework – Data Preparation, Modelling, Evaluation and Deployment

##### Day - 11: Data Visualization in Python

2. Introduction to various charts

3. Data visualization toolkit in Python (Libraries or modules available in Python)

4. Plotting Data in Python using matplotlib and seaborn – Univariate Distributions, Bi-variate Distributions

5. Plotting Time series data

##### Day - 12: Exploratory Data Analysis

2. Data Cleaning – Fixing rows and columns, Missing value Treatment, standardizing values, handling invalid values, Filtering data

3. Data types – Numerical, Categorical (ordered and unordered)

4.Derived Metrics and Feature Engineering

5. Identify Outliers and Handling

##### Day - 13: Inferential Statistics

2. Discrete and Continuous Probability Distributions

3. Central Limit Theorem – Introduction and Industrial applications

##### Machine Learning - I

##### Day - 14: Introduction to Machine Learning

2. Simple Linear Regression

3. Multiple Linear Regression

##### Day - 15: Logistic Regression

2. Binary classification using univariate logistic regression

3. Maximum Likelihood function, Sigmoid Curve and Best Fit

4. Intuition of odds and log-odds

5. Feature selection using RFE

6. Model evaluation – Confusion Matrix and Accuracy

7. Why Accuracy is not Enough and introduction to sensitivity, specificity, precision, recall, area under curve

8. Logistic Regression Case Study

##### Day - 16: unsupervised Learning:Clustering

1. Understanding clustering with practical examples

2. KMeans Clustering – understanding the algorithm

3. Practical consideration for KMeans Clustering – Elbow curve, silhouette metric and hopkings test for clustering tendency of data, impact of outliers

Hierarchical Clustering:

1. Hierarchical clustering Algorithm

2. Interpreting the dendogram and Types of Linkages

3. Comparison of Kmeans clustering and Hierarchical clustering – advantages and disadvantages

##### Machine Learning - II

##### Day - 17: Support Vector Machine Algorithm

1. Introduction to SVM and How does it works.

2. Advantages and Disadvantages of SVM

3. Kernal Functions in used in SVM

4. Applications of SVM

5. Implementation of SVM using Python

##### Day - 18: K Nearest Neighbors and Naive Bayes Algorithm

1. Introduction to KNN and How does it works.

2. Advantages and Disadvantages of KNN

3. Applications of KNN

4. Implementation of KNN using Python

Naive Bayes:

1. Intoduction to Naive Bayes

2. Advantage and Disadvantage of Naive Bayes

3. Applications of Naive Bayes

4. Implementation of Naive Bayes using Python

##### Day - 19: Tree Models

1. Introduction to decision trees and Interpretation

2. Homogeneity measures for splitting a node 1. Gini Index 2. Entropy 3. R2

3. Understanding Hyper parameters – Truncation and Pruning

4. Advantages and Disadvantages

Random Forest:

1. Introduction to ensembling, bagging and intuition

2. Random Forest – Introduction and Hyperparamters

3. Building a model using Random Forest

4. Hyper-parameters impact on model and tuning them

5. Importance of predictors using Random Forrest

##### Day - 20: Deep Learning

##### Day - 21: Introuction

2. Understanding Deep Learning with the help of a case study.

3. Explore the meaning, process, and types of neural networks with a comparison to human neurons

4. Identify the platforms and programming stacks used in Deep Learning

##### Day - 22: Perceptron

2. Implement logic gates with Perceptron.

3. Sigmoid units and Sigmoid activation function in Neural Network

4. ReLU and Softmax Activation Functions.

5. Hyperbolic Tangent Activation Function

##### Day - 23: Artificial Neural Network

2. Implementation of Adaline rule in training ANN.

3. Minimizing cost functions using Gradient Descent rule.

4. Analyze how learning rate is tuned to converge an ANN.

5. Explore the layers of an Artificial Neural Network(ANN).

##### Day - 24: Multilayer ANN

2. Backpropagation to adjust weights in a neural network.

3. Inspect convergence in a multilayer ANN

4. Implement forward propagation in multilayer perceptron (MLP)

##### Day - 25: Introduction to TensorFlow

2. Create a computational and default graph in TensorFlow

3. Implement Linear Regression and Gradient Descent in TensorFlow.

4. Application of Layers and Keras in TensorFlow

5. Uses of TensorBoard

##### Day - 26: Training Neural Networks

2. Optimization & hyperparameters.

3. Solutions to speed up neural networks

4. Regularization techniques to reduce overfitting

##### Day - 27: Convolutional Neural Networks

2. Implementation of CNNs within Keras

##### Day - 28: Convolutional Neural Networks

2. Zero padding works with variations in kernel weights

3.Elaborate the pooling concepts in CNNs

##### Day - 29: Applications of CNN

2. Dense Pridiction

##### Day - 30: Recurrent Neural Networks

2. Understand the working of recurrent neurons and their layers.

3. Interpret how memory cells of recurrent neurons interact

4. Implement RNN in Keras

5. Demonstrate variable length input and output sequences

##### Day - 31: Recurrent Neural Networks

2. Implmentation of LSTM RNN using Keras ,

3. Introducntion to GRU and Implementation uisng Keras

4. Introdunction Encoder, Decoder architectures

##### Day - 32: Memory Models/Networks

2. Introdunction to Dynamic memory networks

3. Introduction to Image Genrative Models

4. GANs, CycleGAN Algotithms

##### Day - 33: Computer Vision

2. Video to text with LSTM models. Attention models for computer vision tasks.

##### Day - 34: Natural Language Processing

2. Vector Model Space models of Semantics

3. Word Vector Representation

4. Skip Gram Model

5. Bag of Words Model

##### Day - 35: Natural Language Processing

2. Applications in word similarity and analogy Recognition

3. Named Entity Recognition.

4. Opinion Mining using RNN

5. Parsing and Setiment Analysis using RNN

6. Sentence Classification using CNN

##### Day - 36: Project Development

##### Day - 37: Project Development

##### Day - 38: Project Development

##### Day - 39: Project Development

##### Day - 40: Project Development

##### Day - 41: Project Development

##### Day - 42: Project Development

##### Day - 43: Project Development

##### Day - 44: Project Development

##### Day - 45: Project Development

## Python

##### Day - 1: Inroduction to Web Deveopment

2. Advantages of Django

3. Applications using Django.

4. Course Overview

##### Python Module

##### Day - 2: Introduction to Python

2. What python can Do? Why Python?

3. History of Python

4. Features of of Python

5. Flavours of Python

6. Advantages and Disadvantages of Python

##### Python Basics

##### Day - 3: Introduction and Data Types

2. Variables(Create, Assign, Multiple Assign)

3. Standard Data Types(Numbers, Strings)

4. Casting

##### Day - 4: Collections

##### Day - 5: Operators and Control Statements

2. Control Statements

##### Day - 6: Loops And Functions

2. Functions basics

3. Functions with Multiple Arguments

##### Day - 7: Functions

2. User-Defined Modules.

3. Module Namespaces

4. Iterators

##### Day - 8: Exception and File handling

2. File Handling

##### Python Advanced

##### Day - 9: OOPs Introduction

2. Class and Object

3. Constructor

4. Destructor

##### Day - 10: Inheritance and Encapsulation

2. Encapsulation

##### Day - 11: Polymorphism and Abstraction

2. Abstraction

##### Day - 12: Garbage Collector

2. Garbage Collector

##### Day - 13: Advanced concepts

2. Closures

3. Decorators

##### Day - 14: Modules and Regular Expressions

2. Regular Expressions

##### Day - 15: Introduction to SQL

2. Basics of SQL, Data Retrieval, sorting, compound functions and relational operators, pattern matching with wild cards.

3. Basics on Table creation, updating, modifying etc.

4. Overall Structure of data retrieval queries, Merging tables, User Defined Functions (UDF), Frames.

##### Day - 16: Introducntion to GUI Programming

2. Font and colors, root window, Components and Events

3. Frame, Cnavas.

4. Widget-Text, Label, Message

5. Buttons – Radio, Check, List Box

##### Django

##### Day - 17: Introduction to Front End

2. Introduction CSS

3. Introducion JavaScript

4. Introdunction to Bootstrap

##### Day - 18: Introduction to Django

2. Features of Django

3. Installing Django

4. Understanding Django Environment

5. A simple “”Hello World”” Application

##### Day - 19: Introduction to Django

2. Frameworks – MVC and MVT

3. HTTP concepts

##### Day - 20: Creating With Django Views

2. About View Functions, Using Django’s HttpResponse Class, Understanding HttpRequest Objects

3. Understanding HttpRequest Objects, Using QueryDict Objects

4. Create an polls app and write first view

##### Day - 21: Configuring URLs

2. Regular Expressions and Expression Examples

3. Simple URLConf Examples and Using Multiple URLConf’s

4. Passing URL Arguments

##### Day - 22: Django templates

2. Creating Template Objects

3. Loading Template Files

4. Filling in Template Content (Context Objects)

5. Template Tags and Filters

6. Template Inheritance

7. Sending Data from url to View and view to Template

##### Day - 23: Django Forms

2. Validation

3. Authentication

4. Advanced Forms processing techniques

##### Day - 24: Django RestAPIs

2. Django-piston

3. CRUD Operations

##### Day - 25: Unit Testing with Django

2. Test

3. Test Databases

4. Doctests

5. Debugging

##### Day - 26: Django Database Models

2. Understanding Django Apps and Defining Django Models

3. Understanding Model Fields & Options and Table Naming Conventions

4. Creating A Django Model and Adding the App to Your Project

5. Validating the App

6. Generating & Reviewing the SQL and Adding Data to the Model

##### Day - 27: Django Database Models

2. Simple Data Retrieval Using a Model

3. Understanding QuerySets and Applying Filters

4. Specifying Field Lookups and Lookup Types

5. Slicing QuerySets and Specifying Ordering in QuerySets

6. Common QuerySet Methods and Deleting Records

7. Managing and Retreving Related Records

8 . Using Q Objects

9 .Creating Forms from Models

##### Day - 28: Admin Interface

2. Customizing Admin Interface

3. aAdding Users

4. Data Access and Modification Using admin panel

5. Giving Permissions to users

##### Day - 29: Access Control with Sessions and Users

2. Creating Cookies/Sessions in Django

3. Sessions in Views and Tuning Sessions

4. Installing Django User Authentication

5. Building your own Login/Logout views

6. Adding and deactivating Users

7. Asyschronous Messaging and Managing Permissions

##### Day - 30: Other Database in Django

2. Confiuring Mysql/Oracle Database

3. Working With MySql/Oracle Database

##### Day - 31: Generic Views

2. Usng Generic Rediects

3. Create/Update/Delete Generic Views

##### Day - 32: Data Caching for performance

2. Enabling Cahing in Django

3. Setting up per-veiw Caching

4. Site Chacing

##### Day - 33: Django Emails Functionality

2. Sending Email

3. Other Email Functions

##### Day - 34: Integrating Bootstrap with Django

##### Day - 35: Live Project Implementation

2. Creating Functional Website in Django

##### Day - 36: Project Development

##### Day - 37: Project Development

##### Day - 38: Project Development

##### Day - 39: Project Development

##### Day - 40: Project Development

##### Day - 41: Project Development

##### Day - 42: Project Development

##### Day - 43: Project Development

##### Day - 44: Project Development

##### Day - 45: Project Development

## VLSI

##### Day - 1: Importance of VLSI

2.Applications

3.Comparision with Other technologies.

##### Day - 2: Digital Electronics on NUMBERING SYSTEM

2.Conversions

3.Logic with Number systems

##### Day - 3: Digital Electronics on NUMBERING SYSTEM

2. Binary Codes

3.Code Converters

##### Day - 4:Digital Electronics on Combinational Blocks

2. Subtractors

3.Logic with Combinational blocks

##### Day - 5: Digital Electronics on Combinational Blocks

2. BLogic with Combinational blocks.

##### Day - 6: Digital Electronics on Combinational Blocks

2. Logic with Combinational blocks

##### Day - 7:Digital Electronics on Combinational Blocks

2. Decoders

3. Logic with Combinational blocks

##### Day - 8:Digital Electronics on Combinational Blocks

2.Logic with Combinational blocks

##### Day - 9:Digital Electronics on Sequential Elocks

2.Latches

3.Flips flops

4.Logic with Sequential blocks

##### Day - 10:Digital Electronics on Sequential Elocks

2.Counters

3.Logic with Sequential blocks

##### Day - 11:Memory Blocks

2.ROM

##### Day - 12:Xilinx and Modelism Tools

2.Design flow of XILINX

3.Explanation of installation of MODELISM TOOL

4.Design flow of MODELSIM

5.Difference between XILINx and MODELISM

Practicals

1.Installation of XILINX TOOL

2.Procedure to use XILINX

3.Installation of MODELISM TOOL

4.Procedure to use MODELSIM

##### Day - 13:Data Flow Modeling Technique

2.Explanation of combinational blocks programming

3.Explanation of sequential blocks programming

##### Day - 14:practicals

2.Programs on combinational blocks

3.Programs on sequential blocks

##### Day - 15:Practicals

2.Logic gates

3.Combinational & sequential blocks

##### Day - 16:Operators

2.Relational Operators

3.Equality Operators

4.Logical Operators

5.Bitwise Operators

6.Shift Operators

7.Reduction Operators

8.Concatenation Operators

9.Replication Operators

10.Conditional Operators

Practicals

1.Programs using OPERATORS

2.Logic gates

3.Combinational & Sequential blocks

##### Day - 17:Structural Modeling Technique

2.Explanation of combinational blocks programming

3.Explanation of sequential blocks programming

##### Day - 18:Practicals

2.Programs on combinational blocks

3.Programs on sequential blocks

##### Day - 19:Instantiation Methods

2.Port order connections

Practicals

1.Programs using Port connections

2.Programs using Port order connections

##### Day - 20:Behavioural Modeling Technique

2.Explanation of combinational blocks programming

3.Explanation of sequential blocks programming

##### Day - 21:Practicals

2.Programs on combinational blocks

3.Programs on sequential blocks

##### Day - 22:Memory Blocks Programming

2.ROM

##### Day - 23:Practicals

2.Programs on ROM

##### Day - 24:Project Work & Documentation

2.Verifying function of block

3.Simulating with Xilinx/modelsim software.

4.Documentation

##### Day - 25:Project Work & Documentation

2.Verifying function of block

3.Simulating with Xilinx/modelsim software.

4.Documentation

##### Day - 26:Project Work & Documentation

2.Verifying function of block

3.Simulating with Xilinx/modelsim software.

4.Documentation

##### Day - 27:Project Work & Documentation

2.Synthesis/simulation

3.Documentation work

##### Day - 28:Project Work & Documentation

2.Verifying function of block

3.Simulating with Xilinx/modelsim software

4.Documentation

##### Day - 29:Project Work & Documentation

2.Verifying function of block

3.Simulating with Xilinx/modelsim software

4.Documentation

##### Day - 30:Completion of Project Work & Documentation

2.Final code submission

3.Final document submission