Generative AI Job Oriented Certificate Program
(8 Weeks)
Elevate your career with AppliedTech’s 8-week Generative AI program, designed to provide you with essential skills in the fast-growing field of artificial intelligence. This course offers expert-led training on key tools like ChatGPT, DALL-E, and MidJourney, ensuring you gain both theoretical knowledge and practical experience. By the end of the program, you’ll be equipped to apply Generative AI techniques in real-world scenarios, propelling your career forward in this innovative landscape. Join AppliedTech and unlock new opportunities in Generative AI!
Enroll in AppliedTech Generative AI Training Program: Upgrade Your Skills and Advance Your Career!
a Singapore based Technology Education Enterprise.Embark on an exciting journey with our Generative AI Training Program, meticulously designed to elevate your skills and position you at the cutting edge of technological innovation. This comprehensive course is perfect for individuals eager to explore and harness the full potential of generative artificial intelligence. Whether you’re an aspiring data scientist, a seasoned developer, or a creative professional, our program offers a unique opportunity to learn the latest AI techniques, empowering you to innovate and lead in a rapidly evolving industry.
Throughout this program, you’ll dive deep into advanced methodologies that are transforming the AI landscape. You’ll gain hands-on experience with the tools and techniques behind generative models, allowing you to not only understand how these technologies work but also to apply them in practical, creative ways. From generating content to developing new solutions for complex problems, this program is your gateway to mastering the capabilities of AI in the real world.
Our training sessions are led by industry experts who ensure you move beyond just theoretical understanding. You’ll be guided through practical exercises and real-world projects, gaining the confidence to implement generative AI across various domains. The curriculum emphasizes not just learning but also doing—equipping you with skills that are highly sought after in today’s job market. By the end of the program, you’ll have built a solid foundation that can propel you into exciting new career opportunities in AI.
Don’t miss this chance to transform your career and be part of the future of technology. Our Generative AI Training Program offers you the tools, knowledge, and practical experience needed to become a leader in this field. Join us and unlock the potential of generative AI to shape not only your career but also the future of artificial intelligence.
An overview of what you will learn from this program.
– Introduction to Generative AI
– Understanding Generative AI and its
potential applications
Content Creation with ChatGPT, Bard and Open-Source Tools
Communication and high-quality prompt design
Using Generative AI for Image Generation in Advertising, e-commerce,
content creation and others!
How to create high-quality audio using
Generative AI
Using Generative AI tools to synthesize videos!
Identifying trends and opportunities, customer segmentation and analysis and others!
Program Highlights
Hands-On Learning Experience
Engage in real-world projects where you apply advanced generative AI techniques within practical scenarios. Work with diverse datasets and tools to build confidence in implementing solutions.
Personalized Mentorship
Receive personalized, 1-on-1 mentorship aligned with your career aspirations. Our dedicated mentors offer tailored guidance, assisting you in effectively navigating your learning journey.
Expert-Led Training
Learn from experienced industry experts who teach both theoretical concepts and practical applications of generative AI, enhancing your understanding through their valuable insights and experiences.
Enhancing Employability
To cultivate skilled and employable talent that meets industry demands, mentors at Our Academy provide support, preparing you for the challenges of Industry 4.0.
Internship Certification
Join us for an immersive 1-month certified internship as we dive into action, exploring exciting developments in this new technological era!
Career Counseling Services
Utilize our career counseling services to enhance soft skills, equipping you for job opportunities in the rapidly evolving field of artificial intelligence.
About the Generative AI Certification Course
Generative AI Certification Courses: Your Path to Professional Excellence
Welcome to AppliedTech’s Generative AI Certification Course, where innovation and expertise converge to create a dynamic learning experience. This course is meticulously designed for individuals who aspire to harness the immense potential of generative artificial intelligence and make impactful contributions in their respective fields. Whether you are a seasoned professional looking to upskill or an enthusiastic newcomer eager to explore the creative possibilities of AI, this program offers a structured pathway to mastery in generative AI.
The Generative AI Certification Course delves into both the theoretical foundations and practical applications of generative AI technologies. Participants will gain a comprehensive understanding of essential concepts, tools, and techniques that drive the development and deployment of generative AI systems. Throughout the course, learners will explore a wide range of topics, including the principles of machine learning, the architecture of generative models, and the latest advancements in AI-driven content creation.
One of the key features of this certification course is its emphasis on hands-on learning. Participants will engage in practical exercises and real-world projects that allow them to apply their knowledge and skills to solve complex problems. Through these projects, you will gain valuable experience in using tools such as ChatGPT, DALL-E, MidJourney, and other state-of-the-art generative AI technologies. This hands-on approach ensures that you not only understand the theory behind generative AI but also develop the technical skills necessary to implement these technologies in various contexts.
Our course is led by industry experts who bring a wealth of knowledge and experience to the classroom. They will guide you through interactive sessions, case studies, and discussions that foster a collaborative learning environment. With a focus on real-world applications, you will gain insights into how generative AI is transforming industries such as entertainment, marketing, healthcare, and beyond.
As part of the program, you will also receive personalized mentorship and career counseling support, helping you navigate your career path in the rapidly evolving field of AI. This comprehensive support system ensures that you are well-prepared to tackle the challenges of the job market and seize exciting career opportunities in generative AI.
By the end of the Generative AI Certification Course, you will not only earn a certification that validates your expertise but also acquire the skills and confidence needed to lead and innovate in the AI landscape. Whether you aim to pursue a career in data science, software development, or creative industries, this certification will serve as a valuable asset in your professional journey.
Join AppliedTech’s Generative AI Certification Course today and embark on a transformative learning experience that will equip you with the knowledge and skills to thrive in the exciting world of generative artificial intelligence. Your journey towards becoming a leader in AI starts here!
Hours Live Sessions from Renowned Practitioners
Get Ahead with Generative AI Certificate!
Certificate of Completion for the 8 Weeks Program
Internship Certificate from Participating Companies
Letter of Recommendation
Languages and Tools Covered :
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.
Program Eligibility Criteria and Prerequisites :
- No programming experience needed
- All tools used in this course are free for you to use.
- Internet, Laptop/PC
- We start from the very basics
Ready to be a Gen AI Professional?
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
- Confidence Interval
- 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)
- 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
- Confidence Interval
- 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)
Testimonials
What they say
Generative AI Course FAQs
This course is designed for a diverse audience, accommodating both professionals and enthusiasts eager to master generative artificial intelligence. Whether you are a data scientist, developer, creative professional, or simply passionate about AI, this course offers a thorough foundation for understanding and applying generative AI concepts.
The Generative AI Certification Course is open to participants who have a basic understanding of programming and a strong interest in artificial intelligence. While familiarity with Python and a basic knowledge of machine learning concepts is helpful, it is not required.
The curriculum spans a variety of topics, from the fundamentals of generative models to hands-on implementation and real-world applications, including the integration of generative AI across different industries. Participants will acquire both theoretical knowledge and practical skills through expert-led sessions and immersive exercises.
Definitely! The course is structured to accommodate participants of all experience levels, including beginners. Our comprehensive curriculum and hands-on approach ensure that anyone, regardless of their background, can understand the concepts and apply them successfully.
You will receive the Generative AI course material through our Learning Management System.
The course completion certificate is valid for life! There is no expiry on the certificate.
You can enroll for the Generative AI course by visiting the AppliedTech website and filling out the enrollment form.
Yes, AppliedTech offers practice exams related to the Generative AI course to help you prepare effectively for assessments and certifications.