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Cloud Engineering with Docker and Kubernetes

(20 Hours)

This 20-hour course is tailored for non-technical and business professionals seeking foundational knowledge in containerization and cloud computing. Participants will explore Docker and Kubernetes, learning to build, deploy, and manage containers and applications in cloud environments through hands-on labs and real-world scenarios. By the end, learners will be equipped with practical skills to support cloud engineering initiatives and boost their career prospects.

Enroll Today

Enroll in AppliedTech Cloud Engineering with Docker and Kubernetes

This 20-hour course is tailored for non-technical and business personas who are looking to develop foundational skills in cloud technologies, particularly in the areas of containerization and container orchestration. Participants will be introduced to Docker, one of the most widely used tools for creating, deploying, and managing containers. They will learn the basics of Docker architecture, commands, and how to create custom containers using Dockerfiles. The course also includes practical labs and demos, ensuring participants gain hands-on experience in container creation and management.

In the second part of the course, participants will explore container orchestration with Kubernetes, a powerful system used to automate the deployment, scaling, and management of containerized applications. The course covers Kubernetes architecture, deploying applications with Minikube, and using managed Kubernetes services such as AWS EKS, Google GKE, and Azure AKS. Learners will also understand Kubernetes CI/CD processes, deployment strategies, and essential concepts like networking, security, logging, monitoring, and scaling.

By the end of this course, participants will have a solid understanding of both Docker and Kubernetes, enabling them to optimize containerization processes and deploy applications in cloud environments. The skills gained will be valuable for those looking to progress in cloud engineering or seek better career opportunities in the rapidly evolving tech landscape. With the help of hands-on labs, demos, and practical use cases, participants will be well-prepared to leverage container technologies in real-world scenarios.

An overview of what you will learn from this program.

Learn the fundamentals of Docker, including its architecture, key components (engine, daemon, client, registry), and installation on various platforms (Windows, MacOS, Linux).

Get hands-on experience with essential Docker commands like docker run, docker pull, docker build, and docker ps to manage containers and images.

Understand how to build custom images using Dockerfiles and best practices for writing efficient and maintainable Dockerfiles.

Learn how to manage multi-container applications using Docker Compose by defining services in a docker-compose.yml file and running applications with commands like docker-compose up and docker-compose down.

Explore Docker Swarm for orchestrating containers in a cluster, ensuring high availability and scalability in containerized environments.

Dive into Kubernetes architecture and gain practical knowledge in deploying applications using Minikube and managed Kubernetes services like AWS EKS, Google GKE, and Azure AKS.

Learn about Kubernetes deployment strategies, CI/CD processes, networking, security, logging, monitoring, and scaling techniques for efficient cloud application management.

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 Cloud Engineering with Docker and Kubernetes

Cloud Engineering with Docker and Kubernetes

This course provides a comprehensive introduction to Docker and Kubernetes for non-technical and business personas looking to enhance their understanding of containerization and cloud computing. Participants will learn the fundamentals of Docker, including its architecture, commands, and how to create custom images using Dockerfiles. Hands-on labs will guide them through container creation, management, and using Docker Compose for multi-container applications. The course also covers Docker Swarm for container orchestration, offering insights into scaling and high availability for containerized services.

In addition to Docker, the course delves into container orchestration with Kubernetes. Participants will explore Kubernetes architecture, deploy applications using Minikube, and work with managed Kubernetes services like AWS EKS, Google GKE, and Azure AKS. The course includes practical demonstrations on CI/CD processes, security, networking, and monitoring within Kubernetes, ensuring participants can effectively manage and scale cloud-native applications. By the end of the course, learners will have the skills to optimize and deploy containerized applications in real-world cloud 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 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 Engineering with Docker and Kubernetes Course Outline

  • Containerization and Container Orchestration
  • Docker Architecture (Engine, Daemon, Client, Registry), Install
  • Docker (Windows, MacOS, Linux)
  • Docker Images, Basic Commands (docker run, docker pull, docker build, docker ps, docker stop, docker rm)
  • Demo: Container creation with Docker
  • Hands-on Lab: Container creation with Docker
  • Dockerfile, build custom images using Dockerfile, Best Practices for Writing Dockerfiles
  • Docker Compose, define Services in docker-compose.yml, manage Multi-Container Apps (docker-compose up, docker-compose down)
  • Docker Swarm
  • Hands-on Labs (2) on various docker use cases
  • Docker Networking
  • Docker volumes and persistent storage
  • Kubernetes Architecture
  • Deploying simple app with Minikube- Demo and Lab
  • Managed Kubernetes service (AWS EKS, Google GKE, Azure AKS) with demo and lab
  • Kubernetes CI-CD/ deployment (incl manifests, Helm)
  • Kubernetes Networking, Security, logging & monitoring, scaling

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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Enroll in our Cloud Engineering Program: Master Docker and Kubernetes at AppliedTech Academy and gain hands-on expertise in containerization and cloud orchestration!

As the demand for cloud-native solutions grows, professionals with the ability to design and manage scalable, efficient containerized applications are in high demand. This practical program will teach you the fundamentals of Docker and Kubernetes, enabling you to create and optimize containers and orchestrate them in cloud environments. Through a combination of foundational knowledge and real-world labs, you’ll learn to deploy, manage, and scale applications with Docker and Kubernetes across leading platforms like AWS, Google Cloud, and Azure. Whether you’re looking to advance your career in cloud engineering or enhance your organization’s cloud capabilities, this course provides the essential skills to excel in the world of cloud technology.

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    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

    Cloud Engineering with Docker and Kubernetes 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 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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