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
We have all reached a point where we need to do a Summer Internship to obtain experience in the field we want to work in and thrive in the future, right? Tru Projects is primarily concerned with providing our students with the best skills for their professional lives. Every one of us searches for Student Internship Opportunities in any industry, such as Engineering, Finance, Marketing, Aeronautics, and so on, to help us develop our skills and gain a better grasp of the domain we want to thrive in. With the rise of virtual technology, we now have access to some of the Best Online Summer Internship Trainings, making our lives easier and more convenient. TruProjects is the ideal pick for you to choose from the various Summer Internship Programs now available. Our organisation can supply you with not just face-to-face internships, but also function as an Online Summer Internship Institute, providing you more flexibility. These days, we’re all extremely familiar with the Online Summer Internship Program and how it operates. You will be guided and trained by qualified specialists with the highest care. You can choose a specific domain and work towards it through the Student Summer Internship programme. Throughout the process, all of your mentors will be available to monitor and track your progress. TruProjects is the place to go if you’re looking for Summer Internships for 3rd year students. We primarily focus on delivering only the best Summer Internships for Students in order to assist them in achieving their career objectives. With the highest quality and resources, our Student Summer Internship Opportunities will undoubtedly assist you in improving yourself both conceptually and practically. Every year, TruProjects conducts its Summer Internship Training in a very organised and consistent manner. Mentors are allocated to students and will assist them throughout their internship, whether it be with training or anything else. The IT Summer Internship is one of our unique internships, in which we train students in the most up-to-date technologies and softwares used in the major industries, allowing them to keep on top of the newest trends.
We’d like to welcome you aboard to begin your professional path with us, given the Summer Internship Openings this year. The Online Summer Internship will certainly provide you with the most up-to-date materials and well-trained mentors. Our online Summer Internship with Certificate will undoubtedly assist you in your career search for full-time positions at reputable companies. This gives you an advantage because the certificate and training will have already assisted you in learning more effectively. If you would like to choose the Software Engineering Summer Internship, it will definitely make you understand the fundamentals of data structures, core programming and so on. This will help you perform well during interviews. The main benefit of the Virtual Summer Internship at Tru Projects is that you can plan your schedule and work accordingly. You need not travel long distances in order to do your internship. Summer Training for Students Is very extremely significant according to us, as it allows students to adequately prepare well for their bigger ventures into service and product based companies. By doing so, one can take advantage of TruProjects Summer Internship online for students and analyse their own interests and strengths, allowing them to strategise their professional aspirations. If you are in your third year of college and are concerned about placements in your final year, doing our Summer Internship for 3rd year students will ensure that you study and physically execute the relevant industry related concepts in the shortest amount of time. You will have a better chance of performing well than many other students if you do the Internship for 3rd year students at Tru Projects. Summer Internships for final year students are available around the time you finish your third year. In either case, you will get valuable experience in a variety of ways. The Internship for final year students at Tru Projects has assisted numerous engineering students meet their technical requirements and land top jobs at well known companies.
Our Summer Final Year Internship is designed to help students discover what each industry in the world has to offer and where they can fit in. The Final Year Summer Internships assist them in determining which areas of interest and aptitude they have. And our Online Summer Internships courses For Students is a great way to get them started. Not only do we provide training in our institute, but we also offer an online Summer Internship for students with certificate, which adds a lot of value to their resume when applying for higher education and jobs. Student Internship Opportunities are what you should constantly look for if you want to be a part of any industry in our ever-changing world. Internship programs for college students play a significant part in determining credits during the last year of any Indian college. As a result, by participating in the Tru Projects Internship Program, you may be assured of receiving those credits in full as well as high-quality services at your convenience. It is fairly common for your college professor or placement officer to advise you to enrol in an Internship Training Program in order to improve your performance. Tru Projects without a doubt provides the best internship training program available. We are dedicated to assisting kids in having fun while learning and achieving success. If you require Internship certificate training, you should come to us because our staff are teachers with years of experience in a variety of fields. For example, the Software Development Internship will assist students in understanding the entire software development life cycle (SDLC). You will be able to grasp the complete technique of producing software in your own unique approach with the Software Engineering Internship. Tru Projects also has a number of internships for freshers. When students initially enter college, they are likely to be intimidated or concerned by the number of books and sources they will have to learn from. Students who enroll in our Online Internship Training Program will have access to trained mentors who will assist them in understanding concepts in a better way.
You might choose to be a Software Engineer Intern or a Software Development Engineer Intern by working with us. The Software Development Engineer Internship has been carefully designed for students who want to further their education or find work in this industry. It is especially beneficial to younger students because it may be tailored to one of the best long-term internship training programmes available. The Long-Term Internship Training will be tailored to the needs of the students. Because we recognise that each student has unique objectives, the Long Term Internship Program will allow them to study the domains at their own pace. When a person wishes to alter their job role and try something new, many working professionals look forward to having a Professional Internship. Our organisation will be able to assist you in teaching you what you need and relevant according to the requirements by completing a real-time summer internship. The real-time summer internship programme will undoubtedly benefit anyone who aspires to improve their technical skills. Students are increasingly interested in online internships since they allow them to experience new things on a regular basis. The Full Time Summer Internship Program at Tru Projects is a good fit for those who have a lot of free time and can work whenever they want. TruProjects also offers a full-time paid summer internship, allowing students to work in a real-world setting. Summer Internships for College Students can bring a variety of benefits to students. Students can learn the value of the College Students Paid Summer Internship as it prepares them for the workforce over time by participating in it.
The Summer College Student Internship is intended to provide training for college students in their subjects of interest. We offer the best internships for college students, as well as long-term internship opportunities. Students can also get online summer internship training through the Summer Internship Training Course. On our website, you can find many Online Internships Courses For Students, or you can always contact us for more information about Programming Internships. Students might get Summer Programming Internships at TruProjects throughout the holidays. If you are a computer science student, the Software Engineering Internship Program will be an added benefit. During your college semester, you will be a master of the subject by the time you complete the Software Engineering Internship.
When you have the opportunity to be a Software Engineer Summer Intern, take a chance since we encourage our students to always study and discover new things. Our organisation also offers an Internship Program For Graduates. If you are fresh to college and want to learn more than what you can learn from books, the Internship Program For Freshers is for you. Because most of our applications deal with data, students enjoy playing with it. If you want to work in analytics, the Summer Analyst Internship at TruProjects is a great place to start. Summer internships for Undergraduates are available at Tru Projects for students who are eager to learn. Because, as mentors, we recognise the significant benefits that any summer internship for undergraduate students may give. Tru Projects also assists in the provision of Summer Research Internships for those who seek to conduct in-depth research into the topics. Students who desire to explore the basic ideas in depth can benefit from the Summer Research Internship Program. We believe that the Summer Research Internship for Students can assist students in exploring the diverse sectors of today’s ICT businesses. Summer Programs for Undergraduate Research are also for students who are just starting college. Tru Projects has the greatest Summer Research Internship Program for students and undergraduate research internships. The main goal of these Summer Undergraduate Research Programs is to assist students in achieving their research objectives. Summer Technical Internships are also available for students from other branches. Mentors appointed to the Summer Technical Internship Program are highly skilled professionals who specialise in various disciplines. We are truly the correct choice for you whether you are looking for the Best Online Summer Internship Trainings or a Paid Software Summer Internship Program. Our organisation also offers a Software Intern Program for Undergraduates as well as a Paid Software Summer Intern Program. Tru Projects is also known for providing the Best online Summer Programming Internship Trainings.
The Programming Internship for Undergraduates is for people who enjoy coding. They will be able to understand major programming concepts and even more beneficial if they choose the Paid Programming Summer Internship. All Programming Intern Undergraduates are typically educated to implement the most recent projects in a unique method. Every year, the company hires Paid Programming Summer Interns, allowing students the opportunity to work while learning. The Certification Internship for Undergraduates is crucial since the certificate adds a lot of value to the candidate’s skill set. We would love to have you work with us if you are looking for a Paid Certification Summer Internship. We will be able to influence students to choose the proper sector based on their strengths because they are aware of the benefits of being a Certification Intern Undergraduate. You are more likely to perform better in your employment as a Paid Certification Summer Intern since you will have a greater understanding of how the organisation operates as a result of the training.Internship opportunities for Undergraduates are absolutely worth looking into, especially if you are an engineering student. Paid Summer Internships will undoubtedly assist you in a variety of ways as you begin your professional career. At Tru Projects, you may locate Paid Summer Internship Programs and Paid Summer Intern Opportunities at any moment. The Intern Program for Undergraduates has been carefully created to assist young professionals in their transition into the technical industry. The same can be said about our Paid Summer InternProgram, which is designed to help you learn while having fun. We would like to have you a part of our company and help you in your journey, so feel free to contact and reach out to us.
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
