Building AI Agents with Code and Low-Code Tools Program
(24 Hours)
This course is tailored for software engineers, developers, and technical professionals eager to dive into AI Agents and Agentic AI. Through a hands-on 4-course AGI Developer program, participants learn to design, build, and deploy AI agents using frameworks like LangGraph and AutoGen, with both code-based and low-code/no-code approaches. The program explores real-world use cases, equipping learners with practical skills to thrive in the evolving AI landscape.
Enroll in AppliedTech Retrieval Augmented Generation (RAG) for AI Applications Program
This comprehensive course is designed for software engineers, developers, architects, and other technical professionals looking to upskill in the domain of AI Agents and Agentic AI. As the industry shifts toward intelligent, autonomous systems, understanding the foundations of AI agent design and deployment is becoming a key differentiator for tech professionals. The course begins with an in-depth overview of AI agents, including core concepts such as agent workflows, reflection, tool usage, planning, and multi-agent collaboration.
Participants will explore both code-based and low-code/no-code development frameworks, gaining hands-on experience with tools like LangGraph and AutoGen. Real-world business and technical use cases across industries, including SDLC automation and vertical-specific applications, provide a contextual understanding of how Agentic AI is revolutionizing workflows. Through live code demonstrations, labs, and collaborative design activities, learners will gain practical expertise in building and optimizing AI agents for production environments.
In the final module, the course focuses on architectural and deployment considerations, including security, scalability, and observability of AI agents in the cloud. Participants will also explore the disruptive pricing models of Agentic AI and their impact on traditional SaaS structures. By the end of this 20-hour program, learners will be equipped with the skills and confidence to build sophisticated AI-driven solutions, paving the way for career advancement and leadership in the AGI development space.
An overview of what you will learn from this program.
Learn the core concepts of AI Agents and Agentic AI, including their roles, workflows, and how they interact with LLMs/SLMs.
Understand key design patterns such as reflection, tool/function calling, planning, reasoning, and multi-agent collaboration.
Explore how AI agents are transforming industries and functions through vertical-specific and SDLC automation use cases.
Build AI agents using both Python-based frameworks and low-code/no-code tools with live demos, labs, and real-world scenarios.
Gain insights into agent performance, observability, security, cloud deployment strategies, and post-production best practices.
Equip yourself with practical AGI development skills that open doors to career growth and roles in the future of AI engineering.
Program Highlights
Hands-on Labs
Code demos and most updated labs to sharpen your skills and practice your learnings. Access latest and powerful LLM models through our online platform and be up-to-date.
Personalized Mentorship
Receive personalized guidance from experienced faculty and mentors, benefiting from a low student-to-instructor ratio that ensures you receive tailored support and assistance.
Experienced Faculty Members
Learn from top industry experts. A low student-to-instructor ratio guarantees close interaction with faculty, enabling a personalized learning experience and effective support.
Enhancing Employability
At Our Academy, mentors develop skilled talent for Industry 4.0 by providing comprehensive support, ensuring you gain the expertise employers need.
Internship Certificates Based on Performance
At AppliedTech, our internship certificates reflect real skills, not just attendance. Every certificate is earned through performance, project work, and practical impact.
About the Building AI Agents with Code and Low-Code Tools Program Course
Building AI Agents with Code and Low-Code Tools Program
The AGI Developer Program – Building AI Agents with Code and Low-Code Tools Program is a specialized course crafted for technical professionals aiming to master the development of AI Agents using both code-based and low-code/no-code tools. It begins by building a strong foundation in Agentic AI concepts, exploring how agents leverage LLMs/SLMs to perform tasks autonomously through reflection, tool use, planning, and collaboration. Participants will gain clarity on how these intelligent systems fit into broader workflows and disrupt traditional software models across business and technical domains.
The course combines theory with practical experience, featuring in-depth modules on tools like LangGraph and AutoGen, hands-on labs, real-world use cases, and collaborative design activities. Learners will also delve into architecture, deployment, observability, and security of AI agents, with a focus on scalable and cloud-ready solutions. By the end, participants will have the confidence and capability to build and deploy AI agents, giving them a competitive edge in the fast-growing landscape of AGI and intelligent automation.
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.
Get Ahead with Building AI Agents with Code and Low-Code Tools Program
Certificate of Completion for the Program
Internship Certificate from Participating Companies
Letter of Recommendation
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
Building AI Agents with Code and Low-Code Tools Program Course Outline
- Overview of AI Agents/ Agentic AI & role of LLMs/ SLMs
- Design patterns of AI Agentic workflows- Reflection, Tool use/ function calling, Planning, Multi-agent collaboration
- Business use cases of AI Agents across Industries and Horizontals/
- Functions incl Vertical AI Agents
- Technical use cases of AI Agents (in SDLC automation)
- AI Agent Building Tools- Code based and low code-no code
- AI Agent/ Agentic AI Pricing models & how they are disruptive to existing SAAS models
- Introduction to Langgraph and Autogen for AI Agent Building
- LLM/SLM selection for AI Agents/ Agentic AI
- Deepdive with scenarios/ use cases- Reflection, Tool use/ function calling, Planning, Reasoning, Multi-agent collaboration/ other
- Code demos- Reflection, Tool use/ function calling, Planning,
- Reasoning, Multi-agent collaboration/ other
- Hands-on Labs- AI Agent development (5 scenarios)
- Breakout Room Activities (2) – AI Agent Design (Functional Specs)
- Agent Performance evaluation & observability
- Agent Security
- Agent deployment & scaling options on Cloud
- Post production considerations for AI Agents
- BreakOut Room Activity – Design Cloud architecture for AI Agent Deployment
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)
Ready to be a AI Agents with Code and Low-Code Tools Program?
Enroll in our Building AI Agents with Code and Low-Code Tools program at AppliedTech Academy and develop the skills needed to lead in the age of autonomous, intelligent systems!
As AI agents rapidly become central to digital transformation, there’s a growing demand for professionals who can design, build, and deploy these intelligent systems efficiently. This hands-on program is designed to equip you with practical experience using Python-based and low-code frameworks, large language models (LLMs/SLMs), and advanced AI agent architectures. With a perfect balance of foundational theory, real-world scenarios, and guided labs, you’ll gain the expertise to create and scale AI agents across cloud environments. Whether you’re advancing your personal career or building capacity in your organization, this course opens the door to the future of Agentic AI development.
Enroll Today!
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Building AI Agents with Code and Low-Code Tools FAQs
This course is designed for software engineers, developers, architects, and other technical professionals who want to build or enhance their skills in AI and machine learning.
No prior experience is required. The course starts with Python basics and gradually builds up to more advanced topics in machine learning and AI.
The course includes hands-on labs using Google Colab and covers popular Python libraries such as NumPy, Pandas, Scikit-learn, TensorFlow, Keras, NLTK, and SpaCy.
Yes, each module includes hands-on coding labs, code demos, and take-home exercises to reinforce learning and practical application.
Yes, participants who complete all modules and exercises successfully will receive an AGI Developer Certification.
LLMs (Large Language Models) are advanced AI models used for tasks like text generation and understanding. The course includes an introduction to LLMs, how they work, and practical prompting techniques.
The course includes approximately 22 hours of instruction and hands-on practice, spread across multiple modules.
The course completion certificate is valid for a lifetime and does not have an expiration date.
To enroll in the Retrieval Augmented Generation (RAG) for AI Applications Program course, visit the AppliedTech website and complete the enrollment form.
This program equips you with in-demand AI and machine learning skills, opening up opportunities in data science, AI development, and other high-growth tech roles.