Introduction to AGI and AI Agents for Business Practitioners

(20 Hours)

This course introduces non-technical and business professionals to the potential of Artificial General Intelligence (AGI) and Large Language Models (LLMs) in real-world work settings. Participants will learn core concepts, practice prompt engineering, and explore AI agents, including their types, workflows, and business use cases. Through hands-on labs, learners will design AI agents and discover how AGI can drive innovation in SaaS and other applications.

Enroll in AppliedTech Introduction to AGI and AI Agents for Business Practitioners

This course is tailored for non-technical and business professionals seeking to explore the transformative potential of Artificial General Intelligence (AGI) and Large Language Models (LLMs) in their work. Participants will develop a foundational understanding of AGI, how it differs from Narrow AI, and its potential applications. With a focus on practical skills, the course will delve into prompt engineering techniques and provide hands-on labs to help learners create and optimize prompts. This empowers participants to start leveraging AGI in their professional environments and enhances their ability to drive innovation.

In addition to AGI and LLMs, the course explores AI agents, which are integral to next-generation AI applications. The curriculum covers different types of AI agents, including Vertical AI Agents, AI Copilots, and AI Chatbots, helping participants understand their roles and capabilities. Through discussions and demos, learners will be introduced to agentic workflow patterns such as tool use, planning, and reasoning. This section of the course aims to help participants design and apply AI agents to solve business problems, with a focus on the growing importance of Vertical AI Agents in the Software-as-a-Service (SaaS) industry.

The program also includes breakout room activities where participants will work on designing functional specifications for AI agents, promoting collaborative learning. By the end of the course, participants will have the skills and knowledge needed to harness AGI and AI agents in their business processes, opening doors to new use cases and innovative applications. This course is an ideal starting point for those looking to integrate AGI into their workflows and identify opportunities to stay ahead of the curve in a rapidly evolving AI landscape.

An overview of what you will learn from this program.

Understand the fundamentals of Artificial General Intelligence (AGI) and Large Language Models (LLMs), exploring their potential and key differences from Narrow AI.

Learn effective prompt engineering methods with practical demos and hands-on labs to enhance the use of LLMs in various applications.

Explore prompt templates, cataloging, and chaining techniques to optimize and automate complex tasks with LLMs.

Gain insights into AI agents, their functions, and how they differ from AI Copilots and AI Chatbots, focusing on their real-world applications

Discover agentic workflows such as planning, reasoning, tool use, and multi-agent collaboration, essential for building advanced AI systems.

Learn to design AI agents by creating functional specifications and exploring pricing models for Vertical AI Agents in SaaS environments

Engage in interactive breakout room activities where participants collaborate on designing AI agents and their functional specifications for business use cases.

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 Introduction to AGI and AI Agents for Business Practitioners

Introduction to AGI and AI Agents for Business Practitioners

This course introduces non-technical and business professionals to the world of Artificial General Intelligence (AGI) and Large Language Models (LLMs). Participants will gain a foundational understanding of AGI, its capabilities, and how it differs from Narrow AI. The course covers key topics like prompt engineering, where learners will engage in hands-on labs to create and optimize prompts, as well as explore techniques like prompt chaining and using prompt catalogs for efficient task automation. By learning the basics of LLMs, attendees will develop practical skills for integrating AGI into their work.

In addition to AGI and LLMs, the course delves into AI agents, their workflows, and applications. Participants will explore the differences between AI agents, AI Copilots, and AI Chatbots, and learn about agentic workflows such as planning, reasoning, and multi-agent collaboration. The course also covers designing AI agents, creating functional specifications, and understanding the disruptive role of Vertical AI Agents in the SaaS industry. Through breakout room activities, learners will collaborate on designing AI agents for real-world business scenarios, equipping them with the knowledge to leverage AGI and AI agents effectively in their professional environments.

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 Introduction to AGI and AI Agents for Business Practitioners Program

Certificate of Completion for the Program
Internship Certificate from Participating Companies
Letter of Recommendation

Program Eligibility Criteria and Prerequisites :

Introduction to AGI and AI Agents for Business Practitioners Course Outline

  • Narrow AI, General AI/ Artificial General Intelligence (AGI)
  • Overview of Large Language Models (LLMs)
  • Overview of Prompt Engineering Techniques with demos (20+)
  • Hands-on Labs: Prompt Engineering (30+ prompts)
  • Prompt Catalog, Prompt Templates & Prompt Chaining
  • What are AI Agents and Vertical AI Agents
  • AI Agents vs AI Copilots vs AI Chatbots
  • Agentic workflow patterns- Reflection, Tool use/ function calling,
  • Planning, Reasoning, Multi-agent collaboration
  • AI Agent use cases
  • AI Agent examples
  • Demo- AI Agents
  • Designing AI Agents, agent pricing, why Vertical AI Agents are disruptive to SAAS
  • Breakout Room Activities (2) – AI Agent Design (Functional Specs)

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)
  •  
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  •  
    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)
  •  
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    1.  

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    Ayaan Software Engineer

    I was completely new to IT, but this course helped me build a solid foundation in cybersecurity. The step-by-step approach, hands-on projects, and support from instructors gave me the confidence to pursue a career in this field. I’m now preparing for my first job as a cybersecurity analyst

    Divya Jain IT security Analyst

    I was impressed by the practical aspects of the course. It didn’t just teach the theory but also provided opportunities to work on real-world cybersecurity issues. The mentorship and guidance from industry experts made it easier to understand the challenges of the cybersecurity world

    David M. Network Engineer

    Introduction to AGI and AI Agents for Business Practitioners Program FAQs

    This course is designed for non-technical and business personas who want to gain a foundational understanding of Docker and Kubernetes, focusing on containerization and cloud orchestration.

    You will learn how to create and optimize containers using Docker, manage multi-container applications with Docker Compose, and orchestrate containerized apps using Kubernetes, including deployment, security, and scaling.

    While prior experience with cloud computing can be helpful, the course is designed to accommodate both beginners and intermediate professionals. The foundational modules will help you grasp key concepts, and the hands-on case studies will reinforce your learning.

    The course is divided into two main sections: Docker basics (covering Docker architecture, commands, and Docker Compose) and Kubernetes (covering architecture, deployment, networking, and CI/CD practices). It includes demos and hands-on labs to reinforce learning.

    You will work with Docker, Minikube, and managed Kubernetes services like AWS EKS, Google GKE, and Azure AKS. You’ll also get hands-on experience with Docker Compose and Swarm for container orchestration.

    By mastering Docker and Kubernetes, you’ll gain valuable cloud engineering skills that are highly sought after in today’s tech landscape. This course will equip you with the expertise to deploy and manage containerized applications, enhancing your career prospects in cloud engineering and related fields.

     

    The course completion certificate is valid for a lifetime and does not have an expiration date.

    To enroll in the Introduction to AGI and AI Agents for Business Practitioners 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.