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AGI Developer Certification Program

(24 Hours)

This course is designed for software engineers, developers, and technical professionals aiming to advance their skills in AGI development. Through a comprehensive 4-course program, participants gain hands-on experience with Python, machine learning, deep learning, NLP, MLOps, and tools like TensorFlow and Keras. The curriculum also covers Generative AI, LLMs, and prompt engineering, preparing learners for advanced roles in AI-driven industries.

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Enroll in AppliedTech AGI Developer Certification Program

AGI Developer Certification Program  is a comprehensive program offered by AppliedTech Academy, designed to equip technical professionals with essential and advanced skills in Python programming, machine learning, and large language models (LLMs). Tailored for software engineers, developers, and solution architects, the course lays a strong foundation in Python basics, object-oriented programming, and key libraries like NumPy, Pandas, and Scikit-learn. Participants engage in practical exercises and coding labs to build confidence in solving real-world machine learning problems.

The course progresses into supervised and unsupervised learning techniques, deep neural networks, and natural language processing (NLP), offering a well-rounded understanding of the ML lifecycle. Through hands-on projects using tools like TensorFlow, Keras, NLTK, and Spacy, learners not only gain technical knowledge but also experience the practical applications of these tools in modern AI systems. Key aspects like model evaluation, deployment, and MLOps are also covered, ensuring that participants are prepared to handle end-to-end AI development.

A unique and forward-looking component of this course is its focus on Generative AI, LLMs, and Prompt Engineering. Learners will understand the fundamentals of how large language models work, including tokenization, embedding, and finetuning techniques. A dedicated module on prompt engineering provides 30+ practical prompts and exercises to help participants master this emerging skill. By the end of the program, students will be ready to build, deploy, and interact with LLMs effectively, positioning themselves at the forefront of the rapidly evolving AI and AGI landscape.

An overview of what you will learn from this program.

Learn Python syntax, OOP concepts, and essential libraries to build a strong programming base for AI and ML development

Gain practical experience in supervised and unsupervised learning techniques using libraries like Scikit-learn, Pandas, and Matplotlib.

Understand the structure of neural networks and implement deep learning models using TensorFlow and Keras in guided labs.

Explore NLP techniques and tools such as NLTK and SpaCy, and learn to build text-based AI models.

Dive into the workings of Large Language Models (LLMs), including tokenization, embeddings, and fine-tuning strategies.

Practice over 30 real-world prompts and learn effective prompting techniques to interact with LLMs efficiently and creatively.

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 AGI Developer Certification Program Course

AGI Developer Certification Program – From Python to Generative AI

The AGI Developer Certification Program – From Python to Generative AI is tailored for software engineers, developers, architects, and other technical professionals aiming to build a solid foundation in artificial general intelligence. This hands-on program begins with Python programming and progresses through essential machine learning concepts, including supervised and unsupervised techniques, deep learning, and NLP.

Participants will gain practical experience using popular Python libraries and tools like TensorFlow, Keras, Scikit-learn, and Google Colab. The course also covers advanced topics such as MLOps, Generative AI, large language models (LLMs), and prompt engineering, preparing learners for real-world AI applications and enhancing their career prospects in the rapidly evolving tech landscape.

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 AGI Developer Program Certificate!

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

AGI Developer Certification Program Course Outline

  • OOPS, Syntax and features, Standard libraries
  • Functions, Classes, Decorators
  • Take-home Exercises
  • Machine Learning basics incl ML types
  • Exploratory data analysis, data cleaning / transformation, feature extraction
  • Python libraries/ packages – sklearn, numpy, os, sys, matplotlib, pandas
  • Code demos and hands-on coding labs (on Google Colab)
  • Take-home Exercises
  • Regression basics, examples of Regression use cases
  • Overview of Regression Algorithms
  • Code demos and hands-on coding labs (on Google Colab)
  • Take-home Exercises
  • Classification basics, examples of Classification use cases
  • Overview of Classification Algorithms
  • Code demos and hands-on coding labs (on Google Colab)
  • Take-home Exercises
  • Basics of Clustering & Dimensionality Reduction and where to use/ use cases
  • Overview of Clustering and Dimensionality Reduction techniques
  • Code demos and hands-on coding labs (on Google Colab)
  • Take-home assignment
  • Deep Learning & Deep Neural Networks
  • Tensorflow for working with Deep neural networks
  • Code demos and hands-on coding labs (on Google Colab)
  • Take-home Exercises
  • Neurons, neural network, activation function, batch, epochs
  • Code demos and hands-on coding labs (on Google Colab)
  • Take-home Exercises
  • Natural Language Processing & NLP Pipeline
  • NLP libraries/ packages – NLTK, Spacy
  • Code demos and hands-on coding labs (on Google Colab)
  • Take-home Exercises
  • Concepts of Model evaluation and testing (Incl role of software testers in ML projects)
  • Python libraries/ packages\
  • Code demos and hands-on coding labs (on Google Colab)
  • Take-home Exercises
  • Deploying ML Models on Cloud
  • AI project lifecycle
  • What is Generative AI
  • What are LLMs and how they work
  • Tokenization & Embedding
  • LLM Finetuning
  • Prompting techniques & practice prompts (30+) for take-home exercises

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.  

Ready to be a AGI Developer Certification Program?

Enroll in our AGI Developer Certification Program today and gain the cutting-edge skills needed to thrive in the evolving world of AI!

The demand for AGI and AI development expertise is rapidly growing, as businesses across industries increasingly adopt intelligent systems and automation. Professionals skilled in Python, machine learning, deep learning, and large language models are becoming essential for building next-generation AI solutions. This certification program equips you with the practical knowledge and hands-on experience needed to design, develop, and deploy advanced AI applications. Whether you’re an individual looking to advance your career or an organization aiming to upskill your team, this program is your gateway to success in the AI-driven future.

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    What they say

    As someone with a background in software development, I was initially worried about how I’d transition into cybersecurity. But this course made it so easy. The instructors broke down complex topics, and the mentorship was invaluable. Highly recommend it for anyone looking to enter the field.

    Aarav Sharma Software Engineer

    This course offered the perfect balance of theory and practice. The detailed modules on network security and ethical hacking were eye-opening, and I could immediately apply my new knowledge to safeguard sensitive information. The personalized learning path was exactly what I needed

    Shreyas Patil IT security Analyst

    I took this course to upskill, and it was one of the best decisions for my career. The modules on threat detection, risk management, and ethical hacking were incredibly detailed and well-structured. I’m now better equipped to protect my company’s systems from cyber threats

    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

    AGI Developer Certification Program Course 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 AGI Developer 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.

    Empowering future through AppliedTech excellence.

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