SUMMER INTERNSHIPS
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
In the past couple of years its become highly common for college students to have an internship in their profile after they graduate. In this competitive world of learnings, study and excellence its necessary to apply and have a meaningful internship in your hands.
We Tru Interns have Summer Internship for 2020. Tru Interns is into Summer Internship Training, Programs and with this we have Summer Intern Openings.
Internship Tracks
Machine Learning
Day - 1: Introduction to Machine Learning
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.
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
1.Introduction to Python
2.Anaconda Installation and Introduction to Jupyter Notebook
2.Anaconda Installation and Introduction to Jupyter Notebook
Day - 3: Python Basics
1. Data Structures in Python (Lists, Tuples, Dictionaries, sets)
Day - 4: Python Baiscs
1.Loops, conditional arguments, Comprehensions, Inbuilt functions , string manipulation etc.
Day - 5: Python Baiscs
1.Introuction to OOPS, Inheritence,Polymorphism,Encapsualtion,Abstraction
Day - 6: Python for Data Science - Numpy
1. Introduction to Numpy.
2. Operations in Numpy
2. Operations in Numpy
Day - 7: Python for Data Science - Pandas
1. Introduction to 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.
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
1. Introduction to Matplotlib.
2. Types of plots with ExamplesInheritence,Polymorphism,Encapsualtion,Abstraction
2. Types of plots with ExamplesInheritence,Polymorphism,Encapsualtion,Abstraction
Day - 9: Introduction to SQL
1. Introduction to Database design,.
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.
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
1. Business and Data Understanding
2. CRISP-DM Framework – Data Preparation, Modelling, Evaluation and Deployment
2. CRISP-DM Framework – Data Preparation, Modelling, Evaluation and Deployment
Day - 11: Data Visualization in Python
1.Introduction to visualization and Importance of Visualization
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
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
1. Introduction to Data Sourcing and various sources available for data collection
2. Data Cleaning – Fixing rows and columns, Missing value Treatment, standardizing values, handling invalid values, Filtering data
2. Data Cleaning – Fixing rows and columns, Missing value Treatment, standardizing values, handling invalid values, Filtering data
Day - 13: Exploratory Data Analysis
1. Data types – Numerical, Categorical (ordered and unordered)
2. Univariate Analysis, Bivariate Analysis, Segmented univariate Analysis
3. Derived Metrics and Feature Engineering
2. Univariate Analysis, Bivariate Analysis, Segmented univariate Analysis
3. Derived Metrics and Feature Engineering
Day - 14: Exploratory Data Analysis
1. Introduction to Outliers.
2. Identify Outliers
3. Outliers Handling using Imputation Techniques
2. Identify Outliers
3. Outliers Handling using Imputation Techniques
Day - 15: Inferential Statistics
1. Introduction to inferential statistics – basics of probability, Random Variables, Expected value, Probability Distributions
2. Discrete and Continuous Probability Distributions
3. Central Limit Theorem – Introduction and Industrial applications
2. Discrete and Continuous Probability Distributions
3. Central Limit Theorem – Introduction and Industrial applications
Day - 16: Hypothesis Testing
1. understanding Hypothesis Testing, Null and Alternate Hypothesis, Industry Relevance
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
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
1. Credit Analysis EDA
2. GDP EDA Analysis
2. GDP EDA Analysis
Machine Learning - I
Day - 18: Introduction to Machine Learning
1. Introduction to Machine Learning – Supervised and Unsupervised learning Methods
Day - 19: Simple Linear Regression
1. Introduction to Regression and Best Fit Line
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
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
1. Using Multiple Predictors for 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
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
1. Introduction to Classification
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
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
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 - 23: 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 - 24: Unsupervised Learning:Principal Component Analysis(PCA)
1. Intuition behind PCA and practical examples
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
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
SVM:
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
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
KNN:
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
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
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
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
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 - 29: Boosting
1. Intuition behind 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
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
Correlation and Regression Analysis of Physicochemical Parameters of River Water for the Evaluation of Percentage
Day - 31: Case Study
Telecom Churn – Group Case Study
Day - 32: Time Series
1. Introduction to Time Series
2. Trend and seasonality
3. Decomposition
4. moothing (moving average)
5. SES, Holt & Holt-Winter Model
2. Trend and seasonality
3. Decomposition
4. moothing (moving average)
5. SES, Holt & Holt-Winter Model
Day - 33: Time Series
1. AutoRegression, Lag Series, ACF, PACF
2. IADF, Random walk and Auto Arima
2. IADF, Random walk and Auto Arima
Day - 34: Text Mining
1. Introduction to 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
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
sentiment analysis Twiter Data
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
1.Anaconda Installation and Introduction to Jupyter Notebook
2.Data Structures in Python (Lists, Tuples, Dictionaries, sets)
2.Data Structures in Python (Lists, Tuples, Dictionaries, sets)
Day - 3: Introduction to Python
1. Loops, conditional arguments, Comprehensions, Inbuilt functions , string manipulation etc.
2. Introuction to OOPS
2. Introuction to OOPS
Day - 4: Python for Data Science
1. Introduction to Numpy and operations in Numpy
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
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
1. Introduction to Database design, OLAP vs OLTP, Star Schema etc.
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.
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
1. Business and Data Understanding
2. CRISP-DM Framework – Data Preparation, Modelling, Evaluation and Deployment
2. CRISP-DM Framework – Data Preparation, Modelling, Evaluation and Deployment
Day - 7: Data Visualization in Python
1.Introduction to visualization and Importance of Visualization
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
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
1. Introduction to Data Sourcing and various sources available for data collection
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
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
1. Introduction to inferential statistics – basics of probability, Random Variables, Expected value, Probability Distributions
2. Discrete and Continuous Probability Distributions
3. Central Limit Theorem – Introduction and Industrial applications
2. Discrete and Continuous Probability Distributions
3. Central Limit Theorem – Introduction and Industrial applications
Day - 10: Hypothesis Testing
1. understanding Hypothesis Testing, Null and Alternate Hypothesis, Industry Relevance
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
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
1. Introduction to Machine Learning – Supervised and Unsupervised learning Methods
Day - 12: Simple Linear Regression
1. Introduction to Regression and Best Fit Line
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
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
1. Using Multiple Predictors for 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
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
1. Bias – Variance Trade off, Occam’s Razor, Curse of Dimensionality
2. Cross Validation and how to avoid overfitting
3. Hyper parameter tuning using GridSearchCV, RandomSearchCV and other libraries
2. Cross Validation and how to avoid overfitting
3. Hyper parameter tuning using GridSearchCV, RandomSearchCV and other libraries
Day - 15: Logistic Regression
1. Introduction to Classification
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
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
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
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)
1. Intuition behind PCA and practical examples
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
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
1. Intuition behind 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
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
1. Introduction to Other Models such as SVM, KNN, Navie Bayes etc.
Day - 22: Time Series
1. Introduction to Time Series with ARIMA
Day - 23: Text Mining
1. Introduction to Text Mining
Deep Learning
Day - 24: Introuction
1.Introduction to deep learning
2.Neural Networks Basics
2.Neural Networks Basics
Day - 25: Neural Networks
1. Introducntion to Artificial Neural Networks
Day - 26: Neural Networks
1. Introducntion to Recurrent Neural Networks
Day - 27: Neural Networks
1. Introduction to Convolutional Neural Networks
Day - 28: Neural Networks
1. Introducntion to Generative Adversarial Networks
Day - 29: Reinforcement Learning
1. Introduction to Reinformant Learning
Natural Language Processing
Day - 30: Introduction
1. Introduction
2. NLP tasks in syntax, semantics, and pragmatics.
3.Applications such as information extraction, question answering, and machine translation.
2. NLP tasks in syntax, semantics, and pragmatics.
3.Applications such as information extraction, question answering, and machine translation.
Day - 31: NLP
1.N-gram Language Models
2.Part Of Speech Tagging and Sequence Labeling
Day – 32: NLP
2.Part Of Speech Tagging and Sequence Labeling
Day – 32: NLP
Day - 32: NLP
1. Basic Neural Networks
2. LSTM Recurrent Neural Networks
2. LSTM Recurrent Neural Networks
Day - 33: NLP
1.Syntactic parsing
2.Semantic Analysis
2.Semantic Analysis
Big Data
Day - 34: Introduction to Big Data storage and Analytics
1. Introduction to Big Data
2. Big Data Storage and processing framework – Hadoop
2. Big Data Storage and processing framework – Hadoop
Day - 35: Hive , sqoop and Spark
1. Big Data ingestion with Hive and sqoop
2.Big Data processing using Apache 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
Introduction to 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.
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
1.Introduction to Python
2.Anaconda Installation and Introduction to Jupyter Notebook
2.Anaconda Installation and Introduction to Jupyter Notebook
Day - 3: Python Basics
1. Data Structures in Python (Lists, Tuples, Dictionaries, sets)
Day - 4: Python Baiscs
1.Loops, conditional arguments, Comprehensions, Inbuilt functions , string manipulation etc.
Day - 5: Python Baiscs
Day - 6: Python for Data Science - Numpy
1. Introduction to Numpy.
2. Operations in Numpy
2. Operations in Numpy
Day - 7: Python for Data Science - Pandas
1. Introduction to 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.
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
1. Introduction to Matplotlib.
2. Types of plots with Examples
2. Types of plots with Examples
Day - 9: Introduction to SQL
1. Introduction to Database design, OLAP vs OLTP, Star Schema etc.
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.
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
1. Business and Data Understanding
2. CRISP-DM Framework – Data Preparation, Modelling, Evaluation and Deployment
2. CRISP-DM Framework – Data Preparation, Modelling, Evaluation and Deployment
Day - 11: Data Visualization in Python
1.Introduction to visualization and Importance of Visualization
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
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
1. Introduction to Data Sourcing and various sources available for data collection
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
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
1. Introduction to inferential statistics – basics of probability, Random Variables, Expected value, Probability Distributions
2. Discrete and Continuous Probability Distributions
3. Central Limit Theorem – Introduction and Industrial applications
2. Discrete and Continuous Probability Distributions
3. Central Limit Theorem – Introduction and Industrial applications
Machine Learning - I
Day - 14: Introduction to Machine Learning
1. Introduction to Machine Learning – Supervised and Unsupervised learning Methods
2. Simple Linear Regression
3. Multiple Linear Regression
2. Simple Linear Regression
3. Multiple Linear Regression
Day - 15: Logistic Regression
1. Introduction to Classification
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
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
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
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
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
SVM:
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
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
KNN:
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
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
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
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
Introduction to Deep Learning
Day - 21: Introuction
1. Evolution of Deep Learning from Artificial Intelligence and Machine Learning
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
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
1. Artificial neurons with a comparison to biological neurons.
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
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
1. Understand how ANN is trained using Perceptron learning rule.
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).
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
1. Regularize and minimize the cost function in a neural network
2. Backpropagation to adjust weights in a neural network.
3. Inspect convergence in a multilayer ANN
4. Implement forward propagation in multilayer perceptron (MLP)
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
1. Introducntion 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
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
1. Initialization Backpropagation
2. Optimization & hyperparameters.
3. Solutions to speed up neural networks
4. Regularization techniques to reduce overfitting
2. Optimization & hyperparameters.
3. Solutions to speed up neural networks
4. Regularization techniques to reduce overfitting
Day - 27: Convolutional Neural Networks
1. Introduction to CNN and Their Applications
2. Implementation of CNNs within Keras
2. Implementation of CNNs within Keras
Day - 28: Convolutional Neural Networks
1. Process of convolution and how it works for image Classification.
2. Zero padding works with variations in kernel weights
3.Elaborate the pooling concepts in CNNs
2. Zero padding works with variations in kernel weights
3.Elaborate the pooling concepts in CNNs
Day - 29: Applications of CNN
1. Object detection using CNN
2. Dense Pridiction
2. Dense Pridiction
Day - 30: Recurrent Neural Networks
1. Introdunction Recurrent Neural Networks (RNN).
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
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
1. Introduction to LSTM
2. Implmentation of LSTM RNN using Keras ,
3. Introducntion to GRU and Implementation uisng Keras
4. Introdunction Encoder, Decoder architectures
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
1. Introdunction to memory models.
2. Introdunction to Dynamic memory networks
3. Introduction to Image Genrative Models
4. GANs, CycleGAN Algotithms
2. Introdunction to Dynamic memory networks
3. Introduction to Image Genrative Models
4. GANs, CycleGAN Algotithms
Day - 33: Computer Vision
1. Image segmentation, object detection, automatic image captioning, Image generation with Generative adversarial network
2. Video to text with LSTM models. Attention models for computer vision tasks.
2. Video to text with LSTM models. Attention models for computer vision tasks.
Day - 34: Natural Language Processing
1. Introduction to NLP
2. Vector Model Space models of Semantics
3. Word Vector Representation
4. Skip Gram Model
5. Bag of Words Model
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
1. Glove, Evaluation
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
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
1. Introduction to Python and Django
2. Advantages of Django
3. Applications using Django.
4. Course Overview
2. Advantages of Django
3. Applications using Django.
4. Course Overview
Python Module
Day - 2: Introduction to Python
1. 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
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
1. Installation of Python,
2. Variables(Create, Assign, Multiple Assign)
3. Standard Data Types(Numbers, Strings)
4. Casting
2. Variables(Create, Assign, Multiple Assign)
3. Standard Data Types(Numbers, Strings)
4. Casting
Day - 4: Collections
1. Collections-Arrays(Lists,Sets,Tuples and Dict)
Day - 5: Operators and Control Statements
1. Operators
2. Control Statements
2. Control Statements
Day - 6: Loops And Functions
1. Making Decsions and Loop Control
2. Functions basics
3. Functions with Multiple Arguments
2. Functions basics
3. Functions with Multiple Arguments
Day - 7: Functions
1. Anonymous Functions
2. User-Defined Modules.
3. Module Namespaces
4. Iterators
2. User-Defined Modules.
3. Module Namespaces
4. Iterators
Day - 8: Exception and File handling
1. Errors and Exception Handling
2. File Handling
2. File Handling
Python Advanced
Day - 9: OOPs Introduction
1. Introduction to OOPs
2. Class and Object
3. Constructor
4. Destructor
2. Class and Object
3. Constructor
4. Destructor
Day - 10: Inheritance and Encapsulation
1. Inheritance
2. Encapsulation
2. Encapsulation
Day - 11: Polymorphism and Abstraction
1, Polymorphism
2. Abstraction
2. Abstraction
Day - 12: Garbage Collector
1. Python Memory manager
2. Garbage Collector
2. Garbage Collector
Day - 13: Advanced concepts
1. Generators
2. Closures
3. Decorators
2. Closures
3. Decorators
Day - 14: Modules and Regular Expressions
1. Modules and Packges
2. Regular Expressions
2. Regular Expressions
Day - 15: Introduction to SQL
1. Introduction to Database design,.
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.
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
1. Introduction to UI
2. Font and colors, root window, Components and Events
3. Frame, Cnavas.
4. Widget-Text, Label, Message
5. Buttons – Radio, Check, List Box
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
1. Introduction to HTML
2. Introduction CSS
3. Introducion JavaScript
4. Introdunction to Bootstrap
2. Introduction CSS
3. Introducion JavaScript
4. Introdunction to Bootstrap
Day - 18: Introduction to Django
1. What is Django? ,Django and Python
2. Features of Django
3. Installing Django
4. Understanding Django Environment
5. A simple “”Hello World”” Application
2. Features of Django
3. Installing Django
4. Understanding Django Environment
5. A simple “”Hello World”” Application
Day - 19: Introduction to Django
1. Django Architecture
2. Frameworks – MVC and MVT
3. HTTP concepts
2. Frameworks – MVC and MVT
3. HTTP concepts
Day - 20: Creating With Django Views
1. Creating Project
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
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
1. About URLsConf
2. Regular Expressions and Expression Examples
3. Simple URLConf Examples and Using Multiple URLConf’s
4. Passing URL Arguments
2. Regular Expressions and Expression Examples
3. Simple URLConf Examples and Using Multiple URLConf’s
4. Passing URL Arguments
Day - 22: Django templates
1. Template Fundamentals
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
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
1. Form classes
2. Validation
3. Authentication
4. Advanced Forms processing techniques
2. Validation
3. Authentication
4. Advanced Forms processing techniques
Day - 24: Django RestAPIs
1. Django REST framework
2. Django-piston
3. CRUD Operations
2. Django-piston
3. CRUD Operations
Day - 25: Unit Testing with Django
1. Using Python’s unittest2 library
2. Test
3. Test Databases
4. Doctests
5. Debugging
2. Test
3. Test Databases
4. Doctests
5. Debugging
Day - 26: Django Database Models
1. About Database Models and Configuring Django for Database Access
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
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
1. Primary Keys and the Model
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
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
1. Enabling Admin Interface
2. Customizing Admin Interface
3. aAdding Users
4. Data Access and Modification Using admin panel
5. Giving Permissions to users
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
1. Introduction to Cookie and Session and Their Differences
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
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
1. Using Sqlite
2. Confiuring Mysql/Oracle Database
3. Working With MySql/Oracle Database
2. Confiuring Mysql/Oracle Database
3. Working With MySql/Oracle Database
Day - 31: Generic Views
1. Simple Generic View
2. Usng Generic Rediects
3. Create/Update/Delete Generic Views
2. Usng Generic Rediects
3. Create/Update/Delete Generic Views
Day - 32: Data Caching for performance
1. Introduction to Caching
2. Enabling Cahing in Django
3. Setting up per-veiw Caching
4. Site Chacing
2. Enabling Cahing in Django
3. Setting up per-veiw Caching
4. Site Chacing
Day - 33: Django Emails Functionality
1. Configuring Mail Settings
2. Sending Email
3. Other Email Functions
2. Sending Email
3. Other Email Functions
Day - 34: Integrating Bootstrap with Django
1. Creating tables,Grids,Carousels
Day - 35: Live Project Implementation
1.Project Life Cycle
2. Creating Functional Website in Django
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
1.VLSI Technology Over View
2.Applications
3.Comparision with Other technologies.
2.Applications
3.Comparision with Other technologies.
Day - 2: Digital Electronics on NUMBERING SYSTEM
1.Digital Numbers
2.Conversions
3.Logic with Number systems
2.Conversions
3.Logic with Number systems
Day - 3: Digital Electronics on NUMBERING SYSTEM
1. K – Maps
2. Binary Codes
3.Code Converters
2. Binary Codes
3.Code Converters
Day - 4:Digital Electronics on Combinational Blocks
1. Adders
2. Subtractors
3.Logic with Combinational blocks
2. Subtractors
3.Logic with Combinational blocks
Day - 5: Digital Electronics on Combinational Blocks
1. Multiplexers.
2. BLogic with Combinational blocks.
2. BLogic with Combinational blocks.
Day - 6: Digital Electronics on Combinational Blocks
1. Demultiplexers
2. Logic with Combinational blocks
2. Logic with Combinational blocks
Day - 7:Digital Electronics on Combinational Blocks
1. Encoders
2. Decoders
3. Logic with Combinational blocks
2. Decoders
3. Logic with Combinational blocks
Day - 8:Digital Electronics on Combinational Blocks
1. Comparators
2.Logic with Combinational blocks
2.Logic with Combinational blocks
Day - 9:Digital Electronics on Sequential Elocks
1. Clock & triggers
2.Latches
3.Flips flops
4.Logic with Sequential blocks
2.Latches
3.Flips flops
4.Logic with Sequential blocks
Day - 10:Digital Electronics on Sequential Elocks
1.Registers
2.Counters
3.Logic with Sequential blocks
2.Counters
3.Logic with Sequential blocks
Day - 11:Memory Blocks
1.RAM
2.ROM
2.ROM
Day - 12:Xilinx and Modelism Tools
1.Explanation of Installation of XILINX tool
2.Design flow of XILINX
3.Explanation of installation of MODELISM TOOL
4.Design flow of MODELSIM
5.Difference between XILINx and MODELISM
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
1.Explanation of logic gate programming
2.Explanation of combinational blocks programming
3.Explanation of sequential blocks programming
2.Explanation of combinational blocks programming
3.Explanation of sequential blocks programming
Day - 14:practicals
1.Programs on logic gates
2.Programs on combinational blocks
3.Programs on sequential blocks
2.Programs on combinational blocks
3.Programs on sequential blocks
Day - 15:Practicals
1.Programs using concurrent signal assignment
2.Logic gates
3.Combinational & sequential blocks
2.Logic gates
3.Combinational & sequential blocks
Day - 16:Operators
1.Arithmetic 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
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
1.Explanation of logic gate programming
2.Explanation of combinational blocks programming
3.Explanation of sequential blocks programming
2.Explanation of combinational blocks programming
3.Explanation of sequential blocks programming
Day - 18:Practicals
1.Programs on logic gates
2.Programs on combinational blocks
3.Programs on sequential blocks
2.Programs on combinational blocks
3.Programs on sequential blocks
Day - 19:Instantiation Methods
1.Port connections
2.Port order connections
Practicals
1.Programs using Port connections
2.Programs using Port order connections
2.Port order connections
Practicals
1.Programs using Port connections
2.Programs using Port order connections
Day - 20:Behavioural Modeling Technique
1.Explanation of logic gate programming
2.Explanation of combinational blocks programming
3.Explanation of sequential blocks programming
2.Explanation of combinational blocks programming
3.Explanation of sequential blocks programming
Day - 21:Practicals
1.Programs on logic gates
2.Programs on combinational blocks
3.Programs on sequential blocks
2.Programs on combinational blocks
3.Programs on sequential blocks
Day - 22:Memory Blocks Programming
1.RAM
2.ROM
2.ROM
Day - 23:Practicals
1.Programs on RAM
2.Programs on ROM
2.Programs on ROM
Day - 24:Project Work & Documentation
1.Designing project block
2.Verifying function of block
3.Simulating with Xilinx/modelsim software.
4.Documentation
2.Verifying function of block
3.Simulating with Xilinx/modelsim software.
4.Documentation
Day - 25:Project Work & Documentation
1.Designing project block
2.Verifying function of block
3.Simulating with Xilinx/modelsim software.
4.Documentation
2.Verifying function of block
3.Simulating with Xilinx/modelsim software.
4.Documentation
Day - 26:Project Work & Documentation
1.Designing project block
2.Verifying function of block
3.Simulating with Xilinx/modelsim software.
4.Documentation
2.Verifying function of block
3.Simulating with Xilinx/modelsim software.
4.Documentation
Day - 27:Project Work & Documentation
1.Verifying bugs in project & solving
2.Synthesis/simulation
3.Documentation work
2.Synthesis/simulation
3.Documentation work
Day - 28:Project Work & Documentation
1.Designing project block
2.Verifying function of block
3.Simulating with Xilinx/modelsim software
4.Documentation
2.Verifying function of block
3.Simulating with Xilinx/modelsim software
4.Documentation
Day - 29:Project Work & Documentation
1.Designing project block
2.Verifying function of block
3.Simulating with Xilinx/modelsim software
4.Documentation
2.Verifying function of block
3.Simulating with Xilinx/modelsim software
4.Documentation
Day - 30:Completion of Project Work & Documentation
1.Final RTL diagram
2.Final code submission
3.Final document submission
2.Final code submission
3.Final document submission
