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
Are you looking for Summer Internship in Bangalore or the best Online Summer Internship Institute in Bangalore and planning to create a fleshed-out profile in your resumes? Hey hurry up then! Please feel free to contact us and give us a tinkle on the blower!! Don’t bite your nails any longer, because services are now available near to your home town in Bangalore (Malleswaram, Vijaya nagar , R. T Nagar , Yellanka , Jaya nagar , Uttrahall , mathikere , J.p nagar , yellanka new town , Indira nagar , Marathahalli , Banaswadi , electronic city, hbr layout , hebbal , yelachenahalli , rajaji nagar , Bommanahalli , Nagarbhavi , Bommasandra, chandra layout, Wilson garden, bellandur , Chikbanavara , Bannerghata road , mahadevapura , sanjay nagar , nagarathpet , basavanagudi , wilson garden , white field, vignana nagar, shivaji nagar, seshadripuram , ramamurthy nagar , rajaji nagar , kumaraswamy layout, koramangala, k.r puram, jaya nagar east, h s r layout , Jalahalli , Roopena agrahara , Singasandra , Domlur layout , Basaveshwara nagar , Btm iind stage , Btm ist stage , Btm layout , Banashankari iii stage , Basavanagudi , Bannerghata road , Hennur , Adugodi , Mahadevapura , Sheshadripuram) Mysore (Saraswathipuram, Vijay Nagar, Kuvempnagar, Yadavagiri, Kuvempunagar, vidyaranyapuram, Aravind Nagar, Bogadi, V.V.Mohalla, N.R. Mohalla, Vivekananda Nagar, Jayalakshmipuram, J.P Nagar, Mysuru, Kuvempu Nagar, Gokulam Road, Mysore City), Mangalore (Muchur, Srinivasnagar PO, Balmatta, Kodiabail, Kankanady, Kodailbail, Bendoorwell, Mangalore, Hampankatta, Dakshina Kannada) Hassan, Mandya (Chamundeshwari Nagar Mandya, Ashoka Nagar Mandya, Pandavapura), Kolar, Tumkur (Ashok nagar, Tumkur HO), Udupi (Udupi, Pangala), Shimoga, Davangere, Chitradurga(Maniyur), Bellary (Bellary Gandhinagar, Bellary, Kurekuppa1), Dharwad, Hubli (Vidya Nagar, Vidyanagar Hubli, Hubli, Deshpandenagar, Vidyanagar Hubli, Vishveshwara Nagar, Jp Nagar Hubli, Old Hubli, Hubli Ho), Raichur, Bidar, Gulbarga, Bijapur (Adarsh Nagar), Belgaum, Chikmagalur (Chikmagalur Fort, Narasimharajapura Chikmagalur), Vidyagiri, Bagalkot (Navanagar Bagalkot, Jamkhandi), Vijayapura.
Tru Projects offers the Best Online Summer Internship Trainingsin Bangalore to help you improve your practical skills.Students are often anxious to intern because they are concerned that their Summer Internship Programmein Bangalore would be filled with uninteresting activities for little or no pay. Despite the fact that not all internships pay in financially, some do pay in other ways. AStudent Summer Internship in Bangalore offered byTru Projects gives you the foundation for a successful career.
Tru Projects is proud to be one of the best academic project consultants in Hyderabad, offering students a wide range of customized B TECH major and mini projects, M TECH major and mini projects, IEEE major and mini projects, MBA major and mini projects, and so on. Among the domains in which we specialize are Data Mining, Artificial Intelligence, Big Data, Deep Learning, Data Science, Android, cloud computing, and Cyber security.
College Students Paid Summer Internship in Bangalore allow you to expand your professional network in the following ways: An Internship Program in Bangalore helps you to meet individuals from different walks of life and broaden your contacts, which is a crucial part of job growth. As an intern in Full Time Summer Internship Program in Bangalore, you will have additional opportunity to form relationships with corporate personnel that you may utilise later in your career with Long Term Internship Opportunity in Bangalore at Tru Projects.
Summer Research Internship in Bangalore give real-world experience and exposure to Beginners and to acquire hands-on experience working in the real world. For students with limited professional experience, there is a learning curve with the Summer Analyst Internship in Bangalore. They get to work on actual projects, communicate with different teams and clients, and get a better understanding of Programming Internships in Bangalore. APaid Certification Summer Internship in Bangalore allows you to put your knowledge to the test while also gaining workplace interpersonal skills, preparing you for employment in paid Summer Intern opportunities in Bangalore.
We provide our students and clients with every resource required to complete a project with the highest level of quality, including error-free source code, documentation, abstracts, presentations, and plagiarized free content. Our goal is to give you the greatest engineering projects and to assist you in every way we can. Please contact us if you have any questions or need more information about the project.
A Paid Summer Internship Programme in Bangalore is a fantastic method to learn about different careers with Certification Internship Undergraduates in Bangalore. Because it usually does not require prior experience, it is easier to get than full-time work in Best Online Summer Programming Internship Trainings in Bangalore. When it comes time to look for a full-time job after interning, you will be a more desirable candidate than your contemporaries by attending Summer Technical Internship Program in Bangalore. Employers are more interested in your practical experience than your academic credentials. Best Internships For College Studentsin Bangalore allow you to put certain classroom methods to the test before entering the workforce. Internship Program For Graduate in Bangalore as well as Internship Program For Fresher in Bangaloregives you a chance to put what you’ve learned into practise in a safe setting where errors are expected, rather than learning the hard way in your first job after college.
Summer College Student Internship in Bangalore makes you to get transition from student to a full-time work exposure in Programming Internships Summer in Bangalore. Graduating and immediately starting a new career might be difficult at times in Internship for final year students in Bangalore. Not every boss is skilled at mentoring new employees at IT Summer Internship in Bangalore. It might be disastrous if you are unable to adjust swiftly. Employers are always on the lookout for candidates who can seamlessly move from student to full-time work with minimal assistance of Summer Internship Training in Bangalore. Many firms may offer interns a full-time employment if they have gained all of the essential skills and are contributing to the organisation in real time summer Internship program in Bangalore. They don’t want to let go of you since you’re a solid investment for the Summer Technical Internship in Bangalore.
Identifying your areas of interest at Online Summer Internship in Bangalore is a different functions and specialties exist in each industry at real time summer Internship in Bangalore. You will gain knowledge in several facts of Online Summer Internship Program in Bangalore so that you may make a better educated career decision. Paid Summer Intern Programme in Bangalore will assist you in identifying your areas of interest within your major Programming Internships in Bangalore. You’re on the right track to achieving your goals. An internship will help you obtain new knowledge and abilities, as well as learn a lot about the intricacies of your desired employment.
Our internship opportunities Undergraduates in Bangalore is meant to assist students learn about the many industries throughout the world and how they might fit in. Paid Software Summer Internship Program in their last year help students figure out what areas of interest and ability they have Certification Intern Undergraduates in Bangalore. And our Programming Intern Undergraduates Online Summer Internships Courses For Students in Bangalore are a terrific way to get them started. We not only give training at our school, but we also provide an online Internships for students in Bangalore who have completed their certificate, which adds a lot of value to their profile when applying for higher education and jobs. If you want to be a part of any sector in our ever-changing world, you should always be on the lookout for Summer Research Internship Program for students in Bangalore.
Summer Internship online for students in Bangalore are a terrific opportunity to get real-world experience and insight into a sector you’re interested in. If you’re a student or just starting out in your profession, an Summer Final Year Internship or Internship for 3rd year students in Bangalore can provide you with the necessary experience to prepare you for full-time employment. Final Year Summer internships in Bangalore can be paid or unpaid, with some unpaid internships providing additional perks like college credit. Paid internships are becoming increasingly prevalent, as opposed to the conventional internship paradigm, which involves labour in return for experience rather than compensation. Intern Programme Undergraduates in Bangalore or Summer Programs for Undergraduate Research in Bangaloremay be available depending on aspects such as industry or professional, role duties, experience necessary, internship length, and inclusion of class credit.
An intern works for a Software Development Internship in Bangalore firm for a set amount of time, generally between semesters or concurrently with them. Not everyone has six months or a year will have Software Engineering Internship in Bangalore to gain the necessary work experience for a job right after graduation. Longterm Internship Program in Bangalore are more likely to be intensive and concentrated. Longterm Internship Training in Bangalore are appropriate for students in their last year of graduation.
online Summer Internship with certificate in Bangalore can last anything from three months to two years. Internship for Freshers are not appropriate in some fields, such as fashion or editorial, because they do not provide enough time for the intern or the company to profit. Software Development Engineer Internship in Bangalore provide additional depth in the workplace. Online Internship Training Program in Bangalore allows you to polish your talents as a Software Development Engineer Intern in Bangalore, work on larger projects for longer periods of time, and become a more valuable member of the team in Tru Projects.
Summer Internships for College Students in Bangalore might be advantageous even for a seasoned worker considering a career shift Summer Internship Training Course in Bangalore. Online Internships Courses For Students in Bangalore have soft talents such as teamwork and leadership. Technical skills and industry expertise, on the other hand, are required before entering a new area of Summer Research Internship Program in Bangalore. It can assist a student in gaining industrial exposure and applying the knowledge obtained in the studies to real-world settings. The internship allows you to obtain relevant knowledge and skills in the subject while also adding a valuable component to your resume. Getting relevant experience and building professional contacts through internships, co-ops, service learning, or externships. There are two things students can do to improve their chances of landing a job in their field of study after graduation.
If you are looking for Student Internship Opportunities or Virtual Summer Internship in Bangalore join Tru Projects immediately to earn valuable experience. If you don’t want to deter anybody from looking for Summer Intern Openings in Bangalore in the traditional manner, start by tapping into Tru Projects website for Software Engineering Summer Internship in Bangalore or Full Time Paid Summer Internship in Bangalore, depending on personal interest. But, because internet searches can help you locate internships and focus on what you want, but may not provide you the unique one like online Summer Internship for students with certificate in Bangalore. So don’t get freak out the Student Internship Opportunities in Bangalore is opened for both Summer Internship for 3rd year students in Bangalore and Summer Internship for final year students in Bangalore.
Without a doubt, Tru Projects offers the greatest Paid Summer Internship opportunities in Bangalore accessible. We are committed to helping the students to have fun while learning and in Paid Programming Summer Internship in Bangalore. You should come to us if you need Summer Research Internship for Students in Bangalore training since our team are teachers with years of expertise in a number of industries in Internship certificate Training in Bangalore. The Software engineer intern in Bangalore will help students comprehend the whole software development life cycle (SDLC). With the Software Engineering Internship Program in Bangalore, you will be able to comprehend the entire process of generating software in your own unique way in Tru Projects.
Working with us might lead to you becoming a Software Engineer Summer Intern in Bangalore or a Software Intern Program Undergraduate in Bangalore. The Programming Internship Undergraduates in Bangalore was created with care for students who desire to pursue their study or work in this field. Paid Programming Summer Intern in Bangalore may be adapted to one of the greatest Summer Undergraduate Research Programs in Bangalore available, which is especially advantageous to younger students. The summer Internship for undergraduate students in Bangalore Training will be adapted to their specific needs. The research Internships for undergraduates in Bangalore will allow students to study the domains at their own speed because we recognise that each student has distinct goals. Many working professionals look forward to having a Software Engineering Summer Internship in Bangalore when they want to change their job position and try something new.
Overall, internship with Tru projects was a rewarding and enjoyable experience with Excellent Services near to your home towns at the Bangalore (Malleswaram, Vijaya nagar , R. T Nagar , Yellanka , Jaya nagar , Uttrahall , mathikere , J.p nagar , yellanka new town , Indira nagar , Marathahalli , Banaswadi , electronic city, hbr layout , hebbal , yelachenahalli , rajaji nagar , Bommanahalli , Nagarbhavi , Bommasandra, chandra layout, Wilson garden, bellandur , Chikbanavara , Bannerghata road , mahadevapura , sanjay nagar , nagarathpet , basavanagudi , wilson garden , white field, vignana nagar, shivaji nagar, seshadripuram , ramamurthy nagar , rajaji nagar , kumaraswamy layout, koramangala, k.r puram, jaya nagar east, h s r layout , Jalahalli , Roopena agrahara , Singasandra , Domlur layout , Basaveshwara nagar , Btm iind stage , Btm ist stage , Btm layout , Banashankari iii stage , Basavanagudi , Bannerghata road , Hennur , Adugodi , Mahadevapura , Sheshadripuram) Mysore (Saraswathipuram, Vijay Nagar, Kuvempnagar, Yadavagiri, Kuvempunagar, vidyaranyapuram, Aravind Nagar, Bogadi, V.V.Mohalla, N.R. Mohalla, Vivekananda Nagar, Jayalakshmipuram, J.P Nagar, Mysuru, Kuvempu Nagar, Gokulam Road, Mysore City), Mangalore (Muchur, Srinivasnagar PO, Balmatta, Kodiabail, Kankanady, Kodailbail, Bendoorwell, Mangalore, Hampankatta, Dakshina Kannada) Hassan, Mandya (Chamundeshwari Nagar Mandya, Ashoka Nagar Mandya, Pandavapura), Kolar, Tumkur (Ashok nagar, Tumkur HO), Udupi (Udupi, Pangala), Shimoga, Davangere, Chitradurga(Maniyur), Bellary (Bellary Gandhinagar, Bellary, Kurekuppa1), Dharwad, Hubli (Vidya Nagar, Vidyanagar Hubli, Hubli, Deshpandenagar, Vidyanagar Hubli, Vishveshwara Nagar, Jp Nagar Hubli, Old Hubli, Hubli Ho), Raichur, Bidar, Gulbarga, Bijapur (Adarsh Nagar), Belgaum, Chikmagalur (Chikmagalur Fort, Narasimharajapura Chikmagalur), Vidyagiri, Bagalkot (Navanagar Bagalkot, Jamkhandi), Vijayapura.
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
