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Technology Internship Program (8 weeks)

AppliedTech’s Technology Internship Program offers an 8-week immersive experience in Data Science, Software Engineering, Cybersecurity, and Artificial Intelligence. Designed for students, recent graduates, and tech enthusiasts, this program provides hands-on projects, mentorship from industry experts, and a globally recognized certificate. Gain practical skills, enhance your resume, and unlock new career opportunities with real-world projects and professional guidance. Enroll now to advance your career in technology and take advantage of bonus workshops and exclusive networking opportunities with AppliedTech.

Enroll in AppliedTech Technology Internship Program (8 weeks)

In association with
Xaltius Logoa Singapore based Technology Education Enterprise.

Enroll in AppliedTech’s Technology Internship Program (8 weeks), an immersive and comprehensive experience that offers aspiring tech enthusiasts the opportunity to dive deep into the world of Data Science, Software Engineering, Cybersecurity, and Artificial Intelligence. Whether you’re a student, recent graduate, or someone looking to switch careers, this internship is designed to give you practical, hands-on experience under the guidance of experienced industry professionals.

Throughout the 8-week journey, you will work on real-world projects that challenge your creativity, critical thinking, and problem-solving skills. With dedicated mentorship and 4 key industry projects, you’ll not only gain technical expertise but also develop the soft skills needed to succeed in today’s tech-driven world.

By the end of the program, you’ll receive a globally recognized certificate of internship completion, a letter of recommendation, and a letter of internship to showcase your experience. Additionally, you will be eligible for free enrollment in exclusive workshops on Generative AI, Data Science, and Software Engineering, and gain access to valuable networking groups and alumni channels, allowing you to connect with like-minded professionals from across the globe.

This internship is a perfect opportunity for current students, recent graduates, career switchers, and innovators looking to expand their knowledge and make a mark in the technology field. Take the first step towards a rewarding career in tech with AppliedTech’s Technology Internship Program today!

Program Highlights

Comprehensive Study Resources

Access a wide range of learning materials, case studies, and tutorials to solidify your understanding and help you excel in your projects.

Industry Mentorship

Benefit from personalized mentorship by experts who will provide career advice, help you navigate challenges, and ensure your success in the tech field.

Expert Instructors

Learn from industry professionals with extensive experience in cutting-edge technologies, ensuring you receive top-tier guidance and insights.

4 Industry-Driven Projects

Work on four real-world projects designed to give you practical experience and boost your confidence in applying new skills to actual industry problems.

8-Week Immersive Training

A focused, hands-on 8-week program that combines intensive learning with project work and professional mentorship, preparing you for a successful career.

Hands-On Experience

Apply your knowledge through real-world challenges and effectively build a professional portfolio that showcases your skills to future employers.

The Technology Internship Program (TIP) is designed for ambitious individuals who have a passion for technology and a desire to gain practical experience in the field. It is open to a wide range of individuals, including:

Why choose AppliedTech :

At AppliedTech, we envision a future where individuals are empowered to navigate and excel in the rapidly evolving realm of technology. Our dedicated team is committed to revolutionizing the learning experience, instilling innovative thinking and adaptability to keep pace with the ever-changing technological landscape.

Our mission at AppliedTech is to cultivate a culture of continuous learning and development, nurturing individuals from diverse backgrounds, whether rooted in the world of IT or branching out into non-IT domains. We firmly believe that knowledge has no boundaries, and we are dedicated to breaking down barriers to make technology education accessible to all.

Who is this Technology Internship Program (TIP) for?

Current Students

Those looking to complement their studies with practical, industry-related experience.

Recent Graduates

Fresh graduates aiming to enhance their employability by gaining real-world skills.

Career Switchers

Professionals seeking a transition into the tech industry, regardless of previous background.

Tech Enthusiasts

Individuals passionate about technology and eager to expand their knowledge and skill set.

Innovators

Creative thinkers ready to push the boundaries and explore new technological solutions.

Post internship completion you will receive the following!

Globally Recognized Certificate of Internship Completion
Letter of Internship
Letter of Recommendation

Enroll today to claim these bonuses!

Bonus 1: Free Workshops!

Bonus 1: Free Workshops!

Enjoy complimentary enrollment in our workshops on Generative AI, Data Science, and Software Engineering! Don't miss this opportunity to enhance your skills and knowledge.

Bonus 2: Networking Channels

Bonus 2: Networking Channels

Addition into our networking groups and alumni channels. This will give you the opportunity to talk to different people from various geographies!

Technology Internship Program [8Weeks]

Available internship streams:

  • Data Science
  • Software Engineering
  • Cybersecurity
  • Artificial Intelligence


Are you prepared to dive into an exhilarating adventure in the realm of innovation and advanced technologies? Our program is tailored to equip aspiring tech enthusiasts with practical experience, guidance from industry experts, and a platform to express and enhance your creativity.

Step 1: Apply by clicking on “Enroll now” in the payment panel.

Step 2: Receive welcome package with instructions and get attached to a mentor.

Step 3: Complete the tasks required as per the internship instructions.

Step 4: Receive your certificate and letter at the end of the internship.

Enroll Today!

    Course Syllabus In Detail :

    • Session 1: Python Basics
      • About Python
      • Python Data Types
      • Python Variables
      • Python comments
      • Python Keywords and Identifiers
      • Python User Input
      • Python Type conversion
      • Python Literals
    •  Session 2: Python Operators + if-else + Loops
      • Python Operators
      • Python if-else
      • Python While Loop
      • Python for loop
      • Break, continue, pass statement in loops
    • Session 3: Python Strings
      • String indexing
      • String slicing
      • Common String functions
    • Assignments and Interview Questions
    • Session 4: Python Lists
      • Array vs List
      • How lists are stored in a memory
      • All Operations on List
      • List Functions
    •  Session 5: Tuples + Set + Dictionary
      • Tuple
      • Operations on tuple
      • Set functions
    • Session 6: Dictionary
      • Operations on dictionary
      • Dictionary functions
    •  Assignments and Interview Questions
    • Create functions.
    • Arguments and parameters
    • args and kwargs
    • map(), filter(), reduce()
    • Assignments and Interview Questions
    • Session 7: OOP Part1
      • What is OOP?
      • What are classes and Objects?
      • Methods vs Functions
      • Magic/Dunder methods
      • What is the true benefit of constructor?
      • Concept of ‘self’
      • __str__, __add__, __sub__ , __mul__ , __truediv__
    •  Session 8: OOP Part2 
      • Encapsulation
      • Collection of objects
    •  Session 9: OOP Part3
      • Class Relationship
      • Inheritance and Inheritance class diagram
      • Constructor example
      • Types of Inheritance (Single, Multilevel, Hierarchical,Multiple )
      • Code example and diamond problem
      • Polymorphism
      • Method Overriding and Method Overloading
    • Session on Abstraction
      • What is Abstraction?
      • Abstract class
    •  3 Interview Questions
    • Session 10: File Handling + Serialization & Deserialization
      • How File I/O is done
      • Writing to a new text file
      • append()
      • Reading a file -> read() and readline()
      • Seek and tell
      • Working with Binary file
      • Serialization and Deserialization
      • JSON module -> dump() and load()
      • Pickling
    • Session 11: Exception Handling
      • Syntax/Runtime Error with Examples
      • Why we need to handle Exception?
      • Exception Handling (Try-Except-Else-Finally)
      • Handling Specific Error
      • Raise Exception
      • Create custom Exception
      • Exception Logging
    • Session 12: Decorators
      • Decorators with Examples
    • Session on Generator
      • What is a generator?
      • Why to use Generator?
      • Yield vs Return
    •  4 Interview Questions
    • Session 13: Numpy Fundamentals
      • Numpy Theory
      • Numpy array
      • Matrix in numpy
      • Array operations
      • Scalar and Vector operations
    • Session 14: Advanced Numpy
      • Numpy array vs Python List
      • Broadcasting
      • Mathematical operations in numpy
      • Sigmoid in numpy
      • Mean Squared Error in numpy
      • Various functions like sort, append, concatenate, percentile, flip, Set functions, etc.
    • Session 16: Pandas Series
      • What is Pandas?
      • Introduction to Pandas Series
      • Series Methods
    • Session 17: Pandas DataFrame
      • Introduction Pandas DataFrame
      • Creating DataFrame and read_csv()
      • Selecting cols and rows from dataframe
      • Filtering a Dataframe
      • Adding new columns
    • Session 18: Important DataFrame Methods
      • Sort, index, reset_index, isnull, dropna, fillna, drop_duplicates, value_counts, apply etc.
    • Session 19: GroupBy Object
      • What is GroupBy?
      • Applying builtin aggregation fuctions on groupby objects
    • Session 20: Merging, Joining, Concatenating
      • Pandas concat method
      • Merge and join methods
      • Practical implementations
    • Session 21: MultiIndex Series and DataFrames
    • Session on Pandas Case Study
    • Session 23: Plotting Using Matplotlib
      • Get started with Matplotlib
      • Plotting simple functions, labels, legends, multiple plots
      • About scatter plots
      • Bar chart
      • Histogram
      • Pie chart
      • Changing styles of plots
    • Session 25: Plotting Using Seaborn
      • Why seaborn?
      • Categorical Plots
      • Stripplot
      • Swarmplot
      • Categorical Distribution Plots
      • Boxplot
      • Violinplot
      • Barplot
    • Session on Data Cleaning and Data Preprocessing Case Study 
      • Quality issues
      • Tidiness issues
      • Data Cleaning
    • Session 29: Exploratory Data Analysis (EDA)
      • Introduction to EDA
      • Why EDA?
      • Steps for EDA
      • Univariate, Bivariate Analysis
      • Feature Engineering
    •  Data Preprocessing steps.
    • Session 30: Database Fundamentals
      • Introduction to Data and Database
      • CRUD operations
      • Types of Database
      • MySQL workbench
      • DDL ,DML ,DQL ,DCL Commands
      • Selecting & Retrieving Data with SQL
      • Filtering, Sorting, and Calculating Data with SQL
      • Sub Queries and Joins in SQL
    • Session 38: Descriptive Statistics Part 1
      • What is Statistics?
      • Types of Statistics
      • Population vs Sample
      • Types of Data
      • Measures of central tendency
      • Measure of Dispersion
      • Quantiles and Percentiles
      • Five Number Summary
      • Boxplots
      • Scatterplots
      • Covariance
      • Correlation
    • Probability Distribution Functions (PDF, CDF, PMF)
      • Random Variables
      • Probability Distributions
      • Probability Distribution Functions and its types
      • Probability Mass Function (PMF)
      • Cumulative Distribution Function (CDF) of PMF
      • Probability Density Function (PDF)
      • Density Estimation
      • Parametric and Non-parametric Density Estimation
      • Kernel Density Estimate (KDE)
      • Cumulative Distribution Function (CDF) of PDF.
    • Session 41: Normal Distribution
      • How to use PDF in Data Science?
      • 2D density plots
      • Normal Distribution (importance, equation, parameter, intuition)
      • Standard Normal Variate (importance, z-table, empirical rule)
      • Skewness
      • Use of Normal Distribution in Data Science
    • Session 42: Non-Gaussian Probability Distributions
      • Kurtosis and Types
      • Transformation
        • Mathematical Transformation
        • Log Transform
        • Reciprocal Transform / Square or sqrt Transform
        • Power Transformer
        • Box-Cox Transform
    • Session 43: Central Limit Theorem
      • Bernouli Distribution
      • Binomial Distribution
      • Intuition of Central Limit Theorem (CLT)
      • CLT in code
    • Session 44: Confidence Intervals
      • Confidence Interval
        • Ways to calculate CI
        • Applications of CI
        • Confidence Intervals in code
    • Session 45: Hypothesis Testing (Part 1)
      • Key idea of hypothesis testing
      • Null and alternate hypothesis
      • Steps in Hypothesis testing
      • Performing z-test
      • Rejection region and Significance level
      • Type-1 error and Type-2 Error
      • One tailed vs. two tailed test
      • Applications of Hypothesis Testing
      • Hypothesis Testing in Machine Learning
    • Session 46: Hypothesis Testing (Part 2) | p-value and t-tests
      • What is p-value?
      • Interpreting p-value
      • T-test
      • Types of t-test 
        • Single sample t-Test
        • Independent 2-sample t-Test
        • Paired 2 sample t-Test
        • Code examples of all of the above
    • Session on Chi-square test
      • Chi-square test
      • Goodness of fit test (Steps, Assumptions, Examples)
      • Test for Independence (Steps, Assumptions, Examples)
      • Applications in machine learning
    • Session on ANOVA
      • F-distribution
      • One/Two-way ANOVA
    • Session on Tensors | Linear Algebra part 1(a)
      • What are tensors?
      • 0D, 1D and 2D Tensors
      • Nd tensors
      • Example of 1D, 2D, 3D, 4D, 5D tensors
    • Session on Vectors | Linear Algebra part 1(b)
      • What is Linear Algebra?
      • What are Vectors?
      • Vector example in ML
      • Row and Column vector
      • Distance from Origin
      • Euclidean Distance
      • Scalar Addition/Subtraction (Shifting)
      • Vector Addition/Subtraction
      • Dot product
      • Angle between 2 vectors
    • Linear Algebra Part 2 | Matrices (computation)
      • What are matrices?
      • Types of Matrices
      • Matrix Equality
      • Scalar Operation
      • Matrix Addition, Subtraction, multiplication
      • Transpose of a Matrix
      • Determinant
      • Inverse of Matrix
    • Linear Algebra Part 3 | Matrices (Intuition)
      • Basis vector
      • Linear Transformations
      • Linear Transformation in 3D
      • Matrix Multiplication as Composition
      • Determinant and Inverse
      • Transformation for non-square matrix?
    • Session 48: Introduction to Machine Learning
      • About Machine Learning (History and Definition)
      • Types of ML
        • Supervised Machine Learning
        • Unsupervised Machine Learning
        • Semi supervised Machine Learning
        • Reinforcement Learning
      •  Batch/Offline Machine Learning
      • Instance based learning
      • model-based learning
      • Instance vs model-based learning
      • Challenges in ML
        • Data collection
        • Insufficient/Labelled data
        • Non-representative date
        • Poor quality data
        • Irrelevant features
        • Overfitting and Underfitting
        • Offline learning
        • Cost
      •  Machine Learning Development Life-cycle
      • Different Job roles in Data Science
      • Framing a ML problem | How to plan a Data Science project
    •  Session 49: Simple Linear regression
      • Introduction and Types of Linear Regression
      • Intuition of simple linear regression
      • How to find m and b?
      • Regression Metrics
      • MAE, MSE, RMSE, R2 score, Adjusted R2 score
    •  Session 50: Multiple Linear Regression
      • Introduction to Multiple Linear Regression (MLR)
      • Mathematical Formulation of MLR
      • Error function of MLR
      • Session on Polynomial Regression
        • Why we need Polynomial Regression?
        • Formulation of Polynomial Regression
      • Session on Assumptions of Linear Regression
      • Session 53: Multicollinearity
        • What is multicollinearity?
        • How to detect and remove Multicollinearity
        • Correlation
        • VIF (Variance Inflation Factor)
    •  
    •  
    •  
      1.  
    • Session 1: Python Basics
      • About Python
      • Python Data Types
      • Python Variables
      • Python comments
      • Python Keywords and Identifiers
      • Python User Input
      • Python Type conversion
      • Python Literals
    •  Session 2: Python Operators + if-else + Loops
      • Python Operators
      • Python if-else
      • Python While Loop
      • Python for loop
      • Break, continue, pass statement in loops
    • Session 3: Python Strings
      • String indexing
      • String slicing
      • Common String functions
    • Assignments and Interview Questions
    • Session 4: Python Lists
      • Array vs List
      • How lists are stored in a memory
      • All Operations on List
      • List Functions
    •  Session 5: Tuples + Set + Dictionary
      • Tuple
      • Operations on tuple
      • Set functions
    • Session 6: Dictionary
      • Operations on dictionary
      • Dictionary functions
    •  Assignments and Interview Questions
    • Create functions.
    • Arguments and parameters
    • args and kwargs
    • map(), filter(), reduce()
    • Assignments and Interview Questions
    • Session 7: OOP Part1
      • What is OOP?
      • What are classes and Objects?
      • Methods vs Functions
      • Magic/Dunder methods
      • What is the true benefit of constructor?
      • Concept of ‘self’
      • __str__, __add__, __sub__ , __mul__ , __truediv__
    •  Session 8: OOP Part2 
      • Encapsulation
      • Collection of objects
    •  Session 9: OOP Part3
      • Class Relationship
      • Inheritance and Inheritance class diagram
      • Constructor example
      • Types of Inheritance (Single, Multilevel, Hierarchical,Multiple )
      • Code example and diamond problem
      • Polymorphism
      • Method Overriding and Method Overloading
    • Session on Abstraction
      • What is Abstraction?
      • Abstract class
    •  3 Interview Questions
    • Session 10: File Handling + Serialization & Deserialization
      • How File I/O is done
      • Writing to a new text file
      • append()
      • Reading a file -> read() and readline()
      • Seek and tell
      • Working with Binary file
      • Serialization and Deserialization
      • JSON module -> dump() and load()
      • Pickling
    • Session 11: Exception Handling
      • Syntax/Runtime Error with Examples
      • Why we need to handle Exception?
      • Exception Handling (Try-Except-Else-Finally)
      • Handling Specific Error
      • Raise Exception
      • Create custom Exception
      • Exception Logging
    • Session 12: Decorators
      • Decorators with Examples
    • Session on Generator
      • What is a generator?
      • Why to use Generator?
      • Yield vs Return
    •  4 Interview Questions
    • Session 13: Numpy Fundamentals
      • Numpy Theory
      • Numpy array
      • Matrix in numpy
      • Array operations
      • Scalar and Vector operations
    • Session 14: Advanced Numpy
      • Numpy array vs Python List
      • Broadcasting
      • Mathematical operations in numpy
      • Sigmoid in numpy
      • Mean Squared Error in numpy
      • Various functions like sort, append, concatenate, percentile, flip, Set functions, etc.
    • Session 16: Pandas Series
      • What is Pandas?
      • Introduction to Pandas Series
      • Series Methods
    • Session 17: Pandas DataFrame
      • Introduction Pandas DataFrame
      • Creating DataFrame and read_csv()
      • Selecting cols and rows from dataframe
      • Filtering a Dataframe
      • Adding new columns
    • Session 18: Important DataFrame Methods
      • Sort, index, reset_index, isnull, dropna, fillna, drop_duplicates, value_counts, apply etc.
    • Session 19: GroupBy Object
      • What is GroupBy?
      • Applying builtin aggregation fuctions on groupby objects
    • Session 20: Merging, Joining, Concatenating
      • Pandas concat method
      • Merge and join methods
      • Practical implementations
    • Session 21: MultiIndex Series and DataFrames
    • Session on Pandas Case Study
    • Session 23: Plotting Using Matplotlib
      • Get started with Matplotlib
      • Plotting simple functions, labels, legends, multiple plots
      • About scatter plots
      • Bar chart
      • Histogram
      • Pie chart
      • Changing styles of plots
    • Session 25: Plotting Using Seaborn
      • Why seaborn?
      • Categorical Plots
      • Stripplot
      • Swarmplot
      • Categorical Distribution Plots
      • Boxplot
      • Violinplot
      • Barplot
    • Session on Data Cleaning and Data Preprocessing Case Study 
      • Quality issues
      • Tidiness issues
      • Data Cleaning
    • Session 29: Exploratory Data Analysis (EDA)
      • Introduction to EDA
      • Why EDA?
      • Steps for EDA
      • Univariate, Bivariate Analysis
      • Feature Engineering
    •  Data Preprocessing steps.
    • Session 30: Database Fundamentals
      • Introduction to Data and Database
      • CRUD operations
      • Types of Database
      • MySQL workbench
      • DDL ,DML ,DQL ,DCL Commands
      • Selecting & Retrieving Data with SQL
      • Filtering, Sorting, and Calculating Data with SQL
      • Sub Queries and Joins in SQL
    • Session 38: Descriptive Statistics Part 1
      • What is Statistics?
      • Types of Statistics
      • Population vs Sample
      • Types of Data
      • Measures of central tendency
      • Measure of Dispersion
      • Quantiles and Percentiles
      • Five Number Summary
      • Boxplots
      • Scatterplots
      • Covariance
      • Correlation
    • Probability Distribution Functions (PDF, CDF, PMF)
      • Random Variables
      • Probability Distributions
      • Probability Distribution Functions and its types
      • Probability Mass Function (PMF)
      • Cumulative Distribution Function (CDF) of PMF
      • Probability Density Function (PDF)
      • Density Estimation
      • Parametric and Non-parametric Density Estimation
      • Kernel Density Estimate (KDE)
      • Cumulative Distribution Function (CDF) of PDF.
    • Session 41: Normal Distribution
      • How to use PDF in Data Science?
      • 2D density plots
      • Normal Distribution (importance, equation, parameter, intuition)
      • Standard Normal Variate (importance, z-table, empirical rule)
      • Skewness
      • Use of Normal Distribution in Data Science
    • Session 42: Non-Gaussian Probability Distributions
      • Kurtosis and Types
      • Transformation
        • Mathematical Transformation
        • Log Transform
        • Reciprocal Transform / Square or sqrt Transform
        • Power Transformer
        • Box-Cox Transform
    • Session 43: Central Limit Theorem
      • Bernouli Distribution
      • Binomial Distribution
      • Intuition of Central Limit Theorem (CLT)
      • CLT in code
    • Session 44: Confidence Intervals
      • Confidence Interval
        • Ways to calculate CI
        • Applications of CI
        • Confidence Intervals in code
    • Session 45: Hypothesis Testing (Part 1)
      • Key idea of hypothesis testing
      • Null and alternate hypothesis
      • Steps in Hypothesis testing
      • Performing z-test
      • Rejection region and Significance level
      • Type-1 error and Type-2 Error
      • One tailed vs. two tailed test
      • Applications of Hypothesis Testing
      • Hypothesis Testing in Machine Learning
    • Session 46: Hypothesis Testing (Part 2) | p-value and t-tests
      • What is p-value?
      • Interpreting p-value
      • T-test
      • Types of t-test 
        • Single sample t-Test
        • Independent 2-sample t-Test
        • Paired 2 sample t-Test
        • Code examples of all of the above
    • Session on Chi-square test
      • Chi-square test
      • Goodness of fit test (Steps, Assumptions, Examples)
      • Test for Independence (Steps, Assumptions, Examples)
      • Applications in machine learning
    • Session on ANOVA
      • F-distribution
      • One/Two-way ANOVA
    • Session on Tensors | Linear Algebra part 1(a)
      • What are tensors?
      • 0D, 1D and 2D Tensors
      • Nd tensors
      • Example of 1D, 2D, 3D, 4D, 5D tensors
    • Session on Vectors | Linear Algebra part 1(b)
      • What is Linear Algebra?
      • What are Vectors?
      • Vector example in ML
      • Row and Column vector
      • Distance from Origin
      • Euclidean Distance
      • Scalar Addition/Subtraction (Shifting)
      • Vector Addition/Subtraction
      • Dot product
      • Angle between 2 vectors
    • Linear Algebra Part 2 | Matrices (computation)
      • What are matrices?
      • Types of Matrices
      • Matrix Equality
      • Scalar Operation
      • Matrix Addition, Subtraction, multiplication
      • Transpose of a Matrix
      • Determinant
      • Inverse of Matrix
    • Linear Algebra Part 3 | Matrices (Intuition)
      • Basis vector
      • Linear Transformations
      • Linear Transformation in 3D
      • Matrix Multiplication as Composition
      • Determinant and Inverse
      • Transformation for non-square matrix?
    • Session 48: Introduction to Machine Learning
      • About Machine Learning (History and Definition)
      • Types of ML
        • Supervised Machine Learning
        • Unsupervised Machine Learning
        • Semi supervised Machine Learning
        • Reinforcement Learning
      •  Batch/Offline Machine Learning
      • Instance based learning
      • model-based learning
      • Instance vs model-based learning
      • Challenges in ML
        • Data collection
        • Insufficient/Labelled data
        • Non-representative date
        • Poor quality data
        • Irrelevant features
        • Overfitting and Underfitting
        • Offline learning
        • Cost
      •  Machine Learning Development Life-cycle
      • Different Job roles in Data Science
      • Framing a ML problem | How to plan a Data Science project
    •  Session 49: Simple Linear regression
      • Introduction and Types of Linear Regression
      • Intuition of simple linear regression
      • How to find m and b?
      • Regression Metrics
      • MAE, MSE, RMSE, R2 score, Adjusted R2 score
    •  Session 50: Multiple Linear Regression
      • Introduction to Multiple Linear Regression (MLR)
      • Mathematical Formulation of MLR
      • Error function of MLR
      • Session on Polynomial Regression
        • Why we need Polynomial Regression?
        • Formulation of Polynomial Regression
      • Session on Assumptions of Linear Regression
      • Session 53: Multicollinearity
        • What is multicollinearity?
        • How to detect and remove Multicollinearity
        • Correlation
        • VIF (Variance Inflation Factor)
    •  
    •  
    •  
      1.  

    Testimonials

    What they say

    “The technology internship was an invaluable experience for me. I had the opportunity to work on real-world projects, write production-level code, and collaborate with experienced developers. The hands-on exposure helped me improve my coding skills and taught me how to navigate challenges in a fast-paced environment."
    Karan G Software Development Intern
    “During my internship, I gained practical experience working with large datasets, building predictive models, and using advanced tools like Python and TensorFlow. The mentorship I received through AppliedTech was excellent, and I left the internship with a strong portfolio of projects that have already helped me land a full-time role."
    Ritu JadhavData Science Intern
    “This internship was the perfect blend of learning and practical application. I worked on security audits, vulnerability assessments, and even helped implement cybersecurity protocols. The experience boosted my confidence and gave me a solid foundation in cybersecurity, which will be essential for my future career"
    Varun MehtaCybersecurity Intern
    “The technology internship allowed me to work closely with designers and developers, where I learned how to design user-friendly interfaces and improve user experience through testing and feedback. I loved being part of the creative process, and the experience has prepared me well for a career in UX/UI design."
    Priya JainUX/UI Design Intern
    “My internship in cloud engineering gave me hands-on experience with cloud platforms like AWS and Azure. I worked on setting up scalable infrastructure and deploying applications. This experience provided me with critical knowledge of cloud technologies, and I’m now confident in pursuing a cloud engineering role.”
    Nikhil Reddy Cloud Engineering Intern

    Technology Internship Program FAQs

    TIP welcomes undergraduate and graduate students, recent graduates, career changers, tech enthusiasts, and anyone passionate about technology. Eligibility requirements may differ, so we encourage you to consult the application guidelines for specific details.

    The duration of the internship program is 8 weeks

    Interns will have the chance to engage in real-world projects tailored to their skills and interests. These projects will span various technology domains, including software development, data analysis, cybersecurity, artificial intelligence, and more. The primary objective is to offer a valuable learning experience that provides practical exposure to their chosen field.

    Absolutely! Mentorship and guidance are key components of the TIP program. Interns will be matched with seasoned professionals who will offer support, insights, and mentorship throughout their internship experience. These mentors will assist interns in navigating their projects, provide career advice, and deliver guidance aimed at fostering their professional development.

    Yes, TIP provides a range of training and learning opportunities to help interns enhance their skills. These offerings may include workshops, seminars, online courses, and access to resources and materials designed to deepen their understanding of relevant technologies and industry practices.

    Outstanding interns may be considered for full-time employment opportunities. Although employment is not guaranteed, we proactively seek out talented individuals throughout the internship program.

    To apply for TIP, please visit our website and navigate to the top section and click on Enroll Now.
    Yes, international students are welcome to apply for TIP.

    Empowering future through AppliedTech excellence.

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