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Cloud Engineer Program: Building Cloud Skills for System Design

(30 Hours)

This Cloud Engineer program trains software engineers, developers, and architects to design scalable and efficient cloud-native systems. It blends core concepts in cloud computing, networking, security, and DevOps with real-world system design case studies from companies like Amazon, Netflix, and Uber. Through hands-on projects and architectural best practices, participants learn to build high-performance, fault-tolerant, and cost-effective cloud solutions.

Enroll Today

Enroll in AppliedTech Foundations of Cloud Computing: AWS and Azure Essentials Program

This specialized Cloud Engineer program is tailored for software engineers, developers, architects, and other technical professionals looking to deepen their understanding of cloud system design. The course offers a comprehensive curriculum focused on the well-architected principles of cloud design, enabling participants to build a solid foundation in designing scalable, reliable, and efficient cloud systems. Through this program, learners will not only grasp essential concepts but also gain practical insights by analyzing 10 high-level system design case studies based on popular applications like Amazon, Netflix, Spotify, and Uber.

The program is structured around two key modules: well-architected cloud design and cloud system design case studies. In the first module, participants will explore the core principles of cloud architecture, including designing cloud compute, storage, networking, security, and DevOps infrastructure. The focus is on building cloud-native microservices architectures, designing for performance, reliability, fault tolerance, and cost efficiency. In the second module, participants will apply their knowledge through case studies, where they will work on high-level system designs for well-known apps. These case studies simulate real-world challenges and provide hands-on learning experiences for designing complex systems.

Upon completing this program, participants will be equipped with the skills needed to design cloud systems that align with best practices in scalability, performance, and operational excellence. They will be able to create robust architectures for high-demand applications and implement strategies for reliability and fault tolerance. Additionally, learners will gain expertise in optimizing cloud infrastructure for cost efficiency and performance, as well as in API lifecycle management and cloud API gateways. This program is not just about theory but also includes practical assignments and exercises to ensure that participants are well-prepared to take on real-world cloud engineering challenges.

An overview of what you will learn from this program.

Learn the key principles of designing distributed cloud systems, focusing on reliability, scalability, and performance.

Analyze 10 high-level system design case studies, including apps like Amazon, Netflix, Spotify, and Uber, to understand real-world application architecture.

Gain practical experience in designing cloud compute, storage, networking, and security infrastructure to support scalable applications.

Learn to design cloud-native microservices architectures and integrate API management strategies for optimal performance.

Explore strategies and techniques to build fault-tolerant systems that ensure continuous availability and reliability.

Discover how to design cloud systems that are cost-effective while maintaining high operational performance and efficiency.

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 Foundations of Cloud Computing: AWS and Azure Essentials Program

Foundations of Cloud Computing: AWS and Azure Essentials Program

This course is designed for technical professionals such as Software Engineers, Developers, and Architects who want to build a strong foundation in cloud computing. It focuses on core cloud services offered by AWS and Azure, including cloud compute (virtual machines, containers, serverless), storage, networking, DevOps, and security. Through practical demos and hands-on labs, participants will gain real-world experience with deploying cloud infrastructure, managing storage solutions, configuring networks, and implementing CI/CD pipelines. This foundational course will prepare participants for more advanced cloud engineering and AGI development opportunities.

Over the course’s 10 hours, learners will explore critical topics such as virtual machine deployment, cloud storage configurations, object storage, and managed databases. They will also dive into networking concepts like VPC and IP subnetting, as well as cloud security practices essential for safe and efficient cloud operations. Additionally, the course covers cloud-native DevOps workflows, including CI/CD, to help participants integrate modern software development practices into cloud environments. By the end of the course, learners will have a solid understanding of both AWS and Azure cloud platforms, ready to tackle more complex cloud projects.

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 Foundations of Cloud Computing: AWS and Azure Essentials 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

Foundations of Cloud Computing: AWS and Azure Essentials Program Course Outline

  • Designing Distributed Cloud systems with Well Architected Framework
  • Designing Cloud Compute Infra
  • Designing Cloud Storage Infra
  • Designing Cloud Networking Infra
  • Designing Cloud Deployment/ DevOps Infra
  • Designing Cloud Security Infra
  • Building Cloud Native Microservices architectures
  • API Design & Cloud API Gateways for API lifecycle mgt
  • Design for Reliability – Strategies, Techniques
  • Design for High Availability & Fault Tolerance – Strategies, Techniques
  • Design for Scalability- Strategies, Techniques
  • Design for Optimal Performance- Strategies, Techniques
  • Design for Cost efficiency- Strategies, Techniques
  • Design for Operational Excellence/ Efficiency – Strategies, Techniques
  • Case Study- High Level System Design – Clone of Amazon.com Ecom App
  • Case Study – High Level System Design – Clone of Netflix/ Youtube Video Streaming App
  • Case Study – High Level System Design – Clone of Spotify Audio Streaming App
  • Case Study – High Level System Design – Clone of UPI/ Payment Gateway App
  • Case Study- High Level System Design – Clone of Instagram
  • Case Study- High Level System Design- Clone of Zepto/ Blinkit Quick Commerce App
  • Case Study- High Level System Design – Clone of Robinhood/ Zerodha Stock Trading App
  • Case Study- High Level System Design- Clone of Uber/Lyft App
  • Case Study – High Level System Design – Clone of Telegram App
  • Case Study- High Level System Design – Clone of Whatsapp
  • Home assignments/ Exercises – B2B App High Level System Design (2)

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 Foundations of Cloud Computing: AWS and Azure Essentials Expert?

Enroll in our Cloud Engineer Program: Building Cloud Skills for System Design at AppliedTech Academy and master the fundamentals of cloud system architecture!

As cloud computing continues to revolutionize industries, the demand for skilled professionals who can design scalable, reliable, and efficient cloud systems is on the rise. This hands-on program will equip you with essential skills in cloud infrastructure, microservices architecture, and API management across leading platforms like AWS and Azure. Through a blend of foundational knowledge and practical case studies, you’ll learn how to design high-availability, fault-tolerant systems while optimizing for cost and performance. Whether you’re aiming to elevate your career or boost your organization’s cloud capabilities, this course will empower you with the expertise needed to thrive in the cloud engineering field.

Enroll Today!

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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

    Foundations of Cloud Computing: AWS and Azure Essentials Program 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.

    You’ll learn the core principles of cloud system design, including designing for reliability, scalability, performance, and cost efficiency. The program also covers cloud infrastructure design, microservices architecture, and API management, alongside case studies of popular applications like Amazon and Netflix.

    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.

    You’ll gain the skills to design and implement distributed cloud systems, optimize cloud resources for performance and cost, and apply best practices in microservices, security, and cloud infrastructure. You’ll also work on high-level system design projects to apply your learning in real-world contexts.

    Yes, upon successful completion of the program, you will receive a certificate from AppliedTech Academy to showcase your expertise in cloud system design and engineering.

    The course includes approximately 30 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 Foundations of Cloud Computing: AWS and Azure Essentials 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.

    Empowering future through AppliedTech excellence.

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