SUMMER INTERNSHIPS
- MACHINE LEARNING
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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
