Write your awesome label here.

๐ŸŽ“ Certification Included ๐ŸŽ“ 

Upon completion of project material

Prerequisites:

๐Ÿคฉ Even if you lack experience in any of the areas below, this project provides the necessary tools and resources to learn and fill the gap.

  • Python and Pandas programming skills: Basic to intermediate knowledge of Python and familiarity with the Pandas library for data manipulation.

  • Machine Learning concepts: Basic understanding of machine learning principles and fundamental experience building machine learning models. 

  • Google Cloud Platform account: Set up a free trial on Google Cloud Platform (GCP) to access various services used in the project.

  • Ready to learn: No matter your current skill level, this project is designed to help you succeed. Come eager to learn and ready to tackle new challenges!

โฐ Estimated Completion Time:

  • For Beginners (1+ year of programming experience with Python and basic understanding Machine Learning concepts): 3-4 weeks.         

  • For Experienced Data/Software Professionals (1+ years of professional experience with Python and Machine Learning models or a Computer Science degree): 2 weeks.

Machine Learning Engineering Project

Build a Production-Grade Machine Learning Pipeline on Google Cloud Platform

Data & Cloud Engineering Project #1

Build a modern data pipeline on Google Cloud Platform from scratch. This project will provide you with the skills to automate the extraction, transformation, and storage of data using advanced Cloud technologies.
Write your awesome label here.

What you will learn:

Master machine learning pipelines in a cloud environment with this end-to-end project. By following industry best practices you will create a real-world production-grade machine learning pipeline on Google Cloud Platform (GCP) to predict future Cryptocurrency prices.

๐ŸŽฏ Professionals typically need at least six months to acquire the skills that this project can teach you in two-three weeks!

What you will learn:

  • Extract data from public APIs: Utilise Python to fetch data historical and real-time cryptocurrency data from CoinAPI.
  • Create end-to-end data pipelines: Transform and Load the extracted API data in a modern Data Lake (Cloud Storage) and a Data Warehouse (BigQuery).
  • Build production-grade machine learning pipelines: Implement and Deploy the three main components of a production-grade machine learning pipeline: 1. feature pipeline, 2. training pipeline and 3. inference pipeline.
  • Deploy data and machine learning pipelines: Deploy your pipelines using Google Cloud Functions to automate their execution.
  • Implement modern job orchestration: Orchestrate the sequential and conditional execution of the data and machine learning pipelines using a modern, serverless tool like Cloud Workflows.
  • Automate data workflows: Leverage Cloud Scheduler to automate the hourly execution of the machine learning pipeline.
  • Create interactive visualizations: Use Looker Studio to create dynamic dashboards that visually represent your machine learning models' predictions.
  • Prepare for interviews: Gain valuable insights on presenting this project to potential employers effectively and handling common interview questions 
  • Extract data from public APIs: Utilise Python to fetch data from public APIs, which is crucial for many data-driven applications.
  • Transform data with Python: Utilize libraries like Pandas to clean and structure raw data, preparing it for storage and analysis.
  • Deploy and manage Data Pipeline: Deploy your data pipeline using Google Cloud services such as Cloud Storage, Cloud Functions, and BigQuery.
  • Automate data workflows: Leverage Cloud Scheduler to automate the daily execution of your data pipeline.
  • Create Interactive Visualizations: Use Looker Studio to create dynamic dashboards that visually represent your data insights.
  • Prepare for interviews: Gain valuable insights on presenting this project to potential employers effectively and handling common interview questions 

Prerequisites:

  • Python and Pandas programming skills: Basic to intermediate knowledge of Python and familiarity with the Pandas library for data manipulation.

  • Google Cloud Platform account: Set up a free trial on Google Cloud Platform (GCP) to access various services used in the project.

  • SQL and relational databases: Basic familiarity with SQL and how relational databases work will help you understand data storage and querying processes.

๐Ÿคฉ Even if you lack experience in any of the above areas, this project provides the necessary resources to learn and fill the gap.

๐ŸŽ“ Certification Included ๐ŸŽ“ 

Upon completion of project material

Machine Learning Engineering Project

Build a Production-Grade Machine Learning Pipeline on Google Cloud Platform

Master machine learning pipelines in a cloud environment with this end-to-end project. By following industry best practices you will create a real-world production-grade machine learning pipeline on Google Cloud Platform (GCP) to predict future Cryptocurrency prices.

๐ŸŽฏ Professionals typically need at least six months to acquire the skills that this project can teach you in two-three weeks!
Write your awesome label here.

What you will learn:

  • Extract data from public APIs: Utilise Python to fetch data historical and real-time cryptocurrency data from CoinAPI.
  • Create end-to-end data pipelines: Transform and Load the extracted API data in a modern Data Lake (Cloud Storage) and a Data Warehouse (BigQuery).
  • Build production-grade machine learning pipelines: Implement and Deploy the three main components of a production-grade machine learning pipeline: 1. feature pipeline, 2. training pipeline and 3. inference pipeline.
  • Deploy data and machine learning pipelines: Deploy your pipelines using Google Cloud Functions to automate their execution.
  • Implement modern job orchestration: Orchestrate the sequential and conditional execution of the data and machine learning pipelines using a modern, serverless tool like Cloud Workflows.
  • Automate data workflows: Leverage Cloud Scheduler to automate the hourly execution of the machine learning pipeline.
  • Create interactive visualizations: Use Looker Studio to create dynamic dashboards that visually represent your machine learning models' predictions.
  • Prepare for interviews: Gain valuable insights on presenting this project to potential employers effectively and handling common interview questions 

Prerequisites:

๐Ÿคฉ Even if you lack experience in any of the areas below, this project provides the necessary tools and resources to learn and fill the gap.

  • Python and Pandas programming skills: Basic to intermediate knowledge of Python and familiarity with the Pandas library for data manipulation.

  • Machine Learning concepts: Basic understanding of machine learning principles and fundamental experience building machine learning models. 

  • Google Cloud Platform account: Set up a free trial on Google Cloud Platform (GCP) to access various services used in the project.

  • Ready to learn: No matter your current skill level, this project is designed to help you succeed. Come eager to learn and ready to tackle new challenges!

โฐ Estimated Completion Time:

  • For Beginners (1+ year of programming experience with Python and basic understanding Machine Learning concepts): 3-4 weeks.         

  • For Experienced Data/Software Professionals (1+ years of professional experience with Python and Machine Learning models or a Computer Science degree): 2 weeks.

๐ŸŽ“ Certification Included ๐ŸŽ“ 

Upon completion of project material

Machine Learning Engineering Project

Build a Production-Grade Machine Learning Pipeline on Google Cloud Platform

Master machine learning pipelines in a cloud environment with this end-to-end project. By following industry best practices you will create a real-world production-grade machine learning pipeline on Google Cloud Platform (GCP) to predict future Cryptocurrency prices.

๐ŸŽฏ Professionals typically need at least six months to acquire the skills that this project can teach you in two-three weeks!
Write your awesome label here.

What you will learn:

  • Extract data from public APIs: Utilise Python to fetch data historical and real-time cryptocurrency data from CoinAPI.
  • Create end-to-end data pipelines: Transform and Load the extracted API data in a modern Data Lake (Cloud Storage) and a Data Warehouse (BigQuery).
  • Build production-grade machine learning pipelines: Implement and Deploy the three main components of a production-grade machine learning pipeline: 1. feature pipeline, 2. training pipeline and 3. inference pipeline.
  • Deploy data and machine learning pipelines: Deploy your pipelines using Google Cloud Functions to automate their execution.
  • Implement modern job orchestration: Orchestrate the sequential and conditional execution of the data and machine learning pipelines using a modern, serverless tool like Cloud Workflows.
  • Automate data workflows: Leverage Cloud Scheduler to automate the hourly execution of the machine learning pipeline.
  • Create interactive visualizations: Use Looker Studio to create dynamic dashboards that visually represent your machine learning models' predictions.
  • Prepare for interviews: Gain valuable insights on presenting this project to potential employers effectively and handling common interview questions 

Prerequisites:

๐Ÿคฉ Even if you lack experience in any of the areas below, this project provides the necessary tools and resources to learn and fill the gap.

  • Python and Pandas programming skills: Basic to intermediate knowledge of Python and familiarity with the Pandas library for data manipulation.

  • Machine Learning concepts: Basic understanding of machine learning principles and fundamental experience building machine learning models. 

  • Google Cloud Platform account: Set up a free trial on Google Cloud Platform (GCP) to access various services used in the project.

  • Ready to learn: No matter your current skill level, this project is designed to help you succeed. Come eager to learn and ready to tackle new challenges!

โฐ Estimated Completion Time:

  • For Beginners (1+ year of programming experience with Python and basic understanding Machine Learning concepts): 3-4 weeks.         

  • For Experienced Data/Software Professionals (1+ years of professional experience with Python and Machine Learning models or a Computer Science degree): 2 weeks.

๐ŸŽ“ Certification Included ๐ŸŽ“ 

Upon completion of project material

Machine Learning Engineering Project

Build a Production-Grade Machine Learning Pipeline on Google Cloud Platform

Master machine learning pipelines in a cloud environment with this end-to-end project. By following industry best practices you will create a real-world production-grade machine learning pipeline on Google Cloud Platform (GCP) to predict future Cryptocurrency prices.

๐ŸŽฏ Professionals typically need at least six months to acquire the skills that this project can teach you in two-three weeks!
Write your awesome label here.

What you will learn:

  • Extract data from public APIs: Utilise Python to fetch data historical and real-time cryptocurrency data from CoinAPI.
  • Create end-to-end data pipelines: Transform and Load the extracted API data in a modern Data Lake (Cloud Storage) and a Data Warehouse (BigQuery).
  • Build production-grade machine learning pipelines: Implement and Deploy the three main components of a production-grade machine learning pipeline: 1. feature pipeline, 2. training pipeline and 3. inference pipeline.
  • Deploy data and machine learning pipelines: Deploy your pipelines using Google Cloud Functions to automate their execution.
  • Implement modern job orchestration: Orchestrate the sequential and conditional execution of the data and machine learning pipelines using a modern, serverless tool like Cloud Workflows.
  • Automate data workflows: Leverage Cloud Scheduler to automate the hourly execution of the machine learning pipeline.
  • Create interactive visualizations: Use Looker Studio to create dynamic dashboards that visually represent your machine learning models' predictions.
  • Prepare for interviews: Gain valuable insights on presenting this project to potential employers effectively and handling common interview questions 

Prerequisites:

๐Ÿคฉ Even if you lack experience in any of the areas below, this project provides the necessary tools and resources to learn and fill the gap.

  • Python and Pandas programming skills: Basic to intermediate knowledge of Python and familiarity with the Pandas library for data manipulation.

  • Machine Learning concepts: Basic understanding of machine learning principles and fundamental experience building machine learning models. 

  • Google Cloud Platform account: Set up a free trial on Google Cloud Platform (GCP) to access various services used in the project.

  • Ready to learn: No matter your current skill level, this project is designed to help you succeed. Come eager to learn and ready to tackle new challenges!

โฐ Estimated Completion Time:

  • For Beginners (1+ year of programming experience with Python and basic understanding Machine Learning concepts): 3-4 weeks.         

  • For Experienced Data/Software Professionals (1+ years of professional experience with Python and Machine Learning models or a Computer Science degree): 2 weeks.

๐ŸŽ“ Certification Included ๐ŸŽ“ 

Upon completion of project material

๐Ÿ’ญ Who is this project for?

This project is perfect for many tech professionals, from beginners in Machine Learning to Experienced (Software, Data, AI) Engineers looking to enhance their skillset with production-grade Machine Learning solutions. Whether you're looking to add impressive projects to your portfolio or upscale to become more competitive in the job market, this project is designed just for you:

#1 Aspiring 
Data Scientists & Machine Learning Engineers

If you're starting your career in AI or enhancing your Machine Learning, this project offers a standout opportunity. Most beginners don't know how to build and deploy end-to-end machine learning pipelines in a production environment. By building and managing a production-grade machine learning pipeline on Google Cloud, you'll upscale and distinguish yourself from other candidates. This practical, scalable project experience can help you land your first full-time job.

#2 Python Developers, Data and Software Engineers

As an experienced professional working as a Python Developer, Data or Software Engineer, this project is an excellent chance to expand your expertise into production-grade machine learning. It's perfect for professionals eager to learn how to implement machine learning solutions following industry standards, such as splitting each machine learning pipeline into three main components: the feature pipeline, the training pipeline, and the inference pipeline.

#3 Experienced Cloud and BI Engineers

If you're proficient in cloud technologies or business intelligence, this project will expand your capabilities by introducing advanced machine learning and automation in Google Cloud. You'll learn how to deploy and manage machine learning pipelines in a cloud environment, leveraging tools like Cloud Functions, BigQuery, and Cloud Storage. This knowledge will enhance your ability to implement scalable machine learning solutions and improve your career prospects.

#4 Career Switchers to AI

If you're transitioning into AI from fields like finance, marketing, or science, this project is your gateway. It offers hands-on experience with coding, data handling, and deploying machine learning models in the cloud. This project will equip you with the skills needed for AI and machine learning roles, ensuring you gain the confidence to tackle technical tasks and discussions, positioning you well for roles in the rapidly growing field of AI.

๐Ÿ’ญ Who is this project for?

This project is perfect for many tech professionals, from beginners in Machine Learning to Experienced (Software, Data, AI) Engineers looking to enhance their skillset with production-grade Machine Learning solutions. Whether you're looking to add impressive projects to your portfolio or upscale to become more competitive in the job market, this project is designed just for you:

#1 Aspiring  Data Scientists & Machine Learning Engineers

If you're starting your career in AI or enhancing your Machine Learning, this project offers a standout opportunity. Most beginners don't know how to build and deploy end-to-end machine learning pipelines in a production environment. By building and managing a production-grade machine learning pipeline on Google Cloud, you'll upscale and distinguish yourself from other candidates. This practical, scalable project experience can help you land your first full-time job.

#2 Python Developers, Data and Software Engineers

As an experienced professional working as a Python Developer, Data or Software Engineer, this project is an excellent chance to expand your expertise into production-grade machine learning. It's perfect for professionals eager to learn how to implement machine learning solutions following industry standards, such as splitting each machine learning pipeline into three main components: the feature pipeline, the training pipeline, and the inference pipeline.

#3 Experienced Cloud and BI Engineers

If you're proficient in cloud technologies or business intelligence, this project will expand your capabilities by introducing advanced machine learning and automation in Google Cloud. You'll learn how to deploy and manage machine learning pipelines in a cloud environment, leveraging tools like Cloud Functions, BigQuery, and Cloud Storage. This knowledge will enhance your ability to implement scalable machine learning solutions and improve your career prospects.

#4 Career Switchers to AI

If you're transitioning into AI from fields like finance, marketing, or science, this project is your gateway. It offers hands-on experience with coding, data handling, and deploying machine learning models in the cloud. This project will equip you with the skills needed for AI and machine learning roles, ensuring you gain the confidence to tackle technical tasks and discussions, positioning you well for roles in the rapidly growing field of AI.

 What's included? 

This is not just a project; it's a complete package that contains everything you need to build the project successfully and master the given skills and technologies. Specifically, it offers:

๐Ÿ’Ž Real-World Portfolio Project:

"Build a Production-Grade Machine Learning Pipeline on Google Cloud Platform"

We provide detailed, step-by-step instructions on how to start, build, and deploy your project from scratch. You don't need to worry about a thing. Every step of the process is clearly documented in an easy-to-read format. We provide both code and instructions because we want you to concentrate on the project's core value: "Learning how to combine cloud technologies to build a high-value, production-ready system that can gather data from any data source and leverge them to build robust and scalable machine learning models"!

By implementing this project, you'll gain experience in high-demand cloud technologies and skills while learning how to build a production-ready project. You'll be able to add a unique portfolio project to your resume, setting you apart from other candidates and boosting your confidence for your interviews to secure your next high-paying job in AI!

๐ŸŒŸ By the end of the project, you will have developed and deployed an automated Batch Machine Learning Pipeline on Google Cloud Platform to gather cryptocurrency data and use them to build models that predict future cryptocurrency prices using modern cloud-based tools and services.


๐ŸŽ Bonuses Included with Every Enrollment:

We offer much more than just a project. You'll engage with various technologies and tools, and to help you navigate them, we provide tailored mini-courses. These courses cover a range of essential topics, from Machine Learning and Google Cloud fundamentals to setting up a free Google Cloud Account and building every crucial component of a Machine Learning Pipeline: Feature Engineering Pipeline, Model Training Pipeline, and Inference (Predicitons) Pipeline. You will also learn about Job Orchestration with modern tools like Cloud Workflows. Everything you need to know is included.

Furthermore, we offer mini-courses on Git and GitHub, complete with detailed instructions on creating a GitHub repository and uploading your code. This visibility can attract recruiters and companies.

We also guide you in sharing your project on LinkedIn to secure your next interviews. Our support extends to offering advice on presenting the project in interviews and responding to common interview questions.Want even more? We want you to be interview-ready! That's why we have prepared a special chapter with the most common interview questions and their answers you will likely face while presenting this project in an interview. We care about everything!

Finally, you'll receive access to our active community of 40+ individuals working on similar projects. Here, you can ask questions, get answers, and receive support from other members and the instructor. We pride ourselves on our excellent community support, as noted by our users.      

๐Ÿ˜ Detailed Description of Your Bonuses:

1. ๐Ÿ‘จโ€๐Ÿ’ป Best free resources to learn Python and Pandas if youโ€™re not familiar already
2. ๐Ÿค– Best free resources to master Machine Learning if you want to enhance your skills.
2. โš™๏ธ Local Environment Setup: How to install Python, VSCode and Anaconda (if you havenโ€™t done already).
3. ๐Ÿ“šMini-Courses & extra lessons to cover the following:
         - API Fundamentals
         - Introduction Google Cloud Platform
         - How to Set Up Your Google Cloud Platform Free Trial Account
         - Security and Access Permissions On Google Cloud Platform       
         - Cloud Storage and Data Lake Fundamentals
         - BigQuery and Data Warehousing Fundamentals
         - Cloud Functions Fundamentals
         - Introduction to Git & GitHub
         - Introduction to Machine Learning
         - Introduction to Time Series Analysis
         - Serverless Job Orchestration with Cloud Workflows

4. ๐Ÿ“‚ Instructions and well-structured readme file to deploy your project on Github.
5. ๐ŸŒ Networking Guidelines to share your Project on LinkedIn and attract recruiters.
6. ๐Ÿ‘” Interview Preparation with common interview questions and answer related to your project.
7. ๐Ÿ“ Final Assesment to test your acquired knowledge.
8. ๐Ÿ…Certificate of completion to share with your network.
9. ๐ŸŒ Access to our Slack Community ๐ŸŒ where we communicate daily to help each other on the projects and job searching.
๐Ÿ˜Š Don't miss this offer!

 What's included? 

This is not just a project; it's a complete package that contains everything you need to build the project successfully and master the given skills and technologies. Specifically, it offers:

๐Ÿ’Ž Real-World Portfolio Project:

"Build a Production-Grade Machine Learning Pipeline on Google Cloud Platform"

We provide detailed, step-by-step instructions on how to start, build, and deploy your project from scratch. You don't need to worry about a thing. Every step of the process is clearly documented in an easy-to-read format. We provide both code and instructions because we want you to concentrate on the project's core value: "Learning how to combine cloud technologies to build a high-value, production-ready system that can gather data from any data source and leverge them to build robust and scalable machine learning models"!

By implementing this project, you'll gain experience in high-demand cloud technologies and skills while learning how to build a production-ready project. You'll be able to add a unique portfolio project to your resume, setting you apart from other candidates and boosting your confidence for your interviews to secure your next high-paying job in AI!

๐ŸŒŸ By the end of the project, you will have developed and deployed an automated Batch Machine Learning Pipeline on Google Cloud Platform to gather cryptocurrency data and use them to build models that predict future cryptocurrency prices using modern cloud-based tools and services.


๐ŸŽ Bonuses Included with Every Enrollment:

We offer much more than just a project. You'll engage with various technologies and tools, and to help you navigate them, we provide tailored mini-courses. These courses cover a range of essential topics, from Machine Learning and Google Cloud fundamentals to setting up a free Google Cloud Account and building every crucial component of a Machine Learning Pipeline: Feature Engineering Pipeline, Model Training Pipeline, and Inference (Predicitons) Pipeline. You will also learn about Job Orchestration with modern tools like Cloud Workflows. Everything you need to know is included.

Furthermore, we offer mini-courses on Git and GitHub, complete with detailed instructions on creating a GitHub repository and uploading your code. This visibility can attract recruiters and companies.

We also guide you in sharing your project on LinkedIn to secure your next interviews. Our support extends to offering advice on presenting the project in interviews and responding to common interview questions.Want even more? We want you to be interview-ready! That's why we have prepared a special chapter with the most common interview questions and their answers you will likely face while presenting this project in an interview. We care about everything!

Finally, you'll receive access to our active community of 40+ individuals working on similar projects. Here, you can ask questions, get answers, and receive support from other members and the instructor. We pride ourselves on our excellent community support, as noted by our users.

๐Ÿ˜ Detailed Description of Your Bonuses:

1. ๐Ÿ‘จโ€๐Ÿ’ป Best free resources to learn Python and Pandas if youโ€™re not familiar already
2. ๐Ÿค– Best free resources to master Machine Learning if you want to enhance your skills.
2. โš™๏ธ Local Environment Setup: How to install Python, VSCode and Anaconda (if you havenโ€™t done already).
3. ๐Ÿ“šMini-Courses & extra lessons to cover the following:
- API Fundamentals
- Introduction Google Cloud Platform
- How to Set Up Your Google Cloud Platform Free Trial Account
- Security and Access Permissions On Google Cloud Platform       
- Cloud Storage and Data Lake Fundamentals
- BigQuery and Data Warehousing Fundamentals
- Cloud Functions Fundamentals
- Introduction to Git & GitHub
- Introduction to Machine Learning
- Introduction to Time Series Analysis
- Serverless Job Orchestration with Cloud Workflows

4. ๐Ÿ“‚ Instructions and well-structured readme file to deploy your project on Github.
5. ๐ŸŒ Networking Guidelines to share your Project on LinkedIn and attract recruiters.
6. ๐Ÿ‘” Interview Preparation with common interview questions and answer related to your project.
7. ๐Ÿ“ Final Assesment to test your acquired knowledge.
8. ๐Ÿ…Certificate of completion to share with your network.
9. ๐ŸŒ Access to our Slack Community ๐ŸŒ where we communicate daily to help each other on the projects and job searching.
๐Ÿ˜Š Don't miss this offer!

What Our Students Say โค๏ธ

What Our Students Say โค๏ธ

WHAT YOU ARE GOING TO BUILD

Project architecture diagram:

Project Workflow   

Here is the outline of the process you will follow to complete the project:
๐Ÿ”น PART 1: Introduction
1. Getting Started:

Define the project, establish requirements, and devise an implementation strategy. Learn the essential steps for setting up and starting the project.

2. Mini-Course: API Fundamentals
Learn API fundamentals, what is an API, how data professionals uses APIs, how to get access and make API calls to extract data using Python and Postman.
3. Account Setup: Create a CoinAPI Account
Create a Free CoinAPI Account to get an API key and use it extract historical and real-time cryptocurrency data.


๐Ÿ”น PART 2: Data Fetching Pipeline
4. Data Extraction
Write Python code to extract weather historical and real-time data from CoinAPI.

5. Data Transformation
Cleanse and manipulate the extracted data using Pandas functionalities.

6. Google Cloud Platform Introduction:
Introduce Google Cloud Platform, focusing on setting up a free trial account and understanding its core functionalities.

7. Load Raw Data in Data Lake
Write Python code to Load raw, unprocessed data into Cloud Storage.

8. Store Processed Data in Data Warehouse
Write Python code to Load Clean Data into BigQuery (Data Warehouse).


๐Ÿ”น PART 3: Machine Learning Pipeline
9. Mini-Course: Introduction to Machine Learning
Learn the basics of machine learning, including key concepts and techniques used in the field.

10. Mini-Course: Introduction to Time Series Analysis
Understand the fundamentals of time series analysis, crucial for predicting future cryptocurrency prices based on historical data.
11.Introduction to Production-Ready Machine Learning Pipelines
Learn how to design and implement machine learning pipelines that are scalable, reliable, and suitable for production environments.

12 Build & Deploy the Feature Pipeline
Develop and deploy a pipeline to perform feature engineering on raw data and store the generated features in BigQuery.

13. Build & Deploy the Training Pipeline
Create and deploy a pipeline to train machine learning models using the engineered features and store the trained models in Cloud Storage.

14. Build & Deploy the Inference Pipeline
Implement and deploy a pipeline to use the trained models to make hourly predictions of cryptocurrency prices and store the predictions in BigQuery.


๐Ÿ”น PART 4: Automation
15. Automate the Machine Learning Pipeline with Cloud Workflows and Cloud Scheduler

Orchestrate the sequential execution of the pipelines and set up Cloud Scheduler to automate their hourly execution.


16. Apply Your Knowledge: Set Up the ML Pipeline for Another Cryptocurrency in 10 Minutes

Use the knowledge and skills acquired to quickly set up the machine learning pipeline for another cryptocurrency, demonstrating the flexibility and scalability of your solution.


๐Ÿ”น PART 5: Visualization
17. Looker Studio Challenge: 
Build an interactive dashboard with Looker Studio to visualize the predictions of cryptocurrency prices, providing insights and trends based on the data stored in BigQuery.


๐Ÿ”น PART 6: Next Steps
18. Project Wrap-Up:
Review final steps and instructions for winding down the Google Cloud Platform Free Trial Account properly.

19. Portfolio Enhancement & Interview Prep: 
Learn how to upload your project to GitHub to showcase your technical skills to potential employers and prepare for common interview questions related to your project

Take your career to the next level
with better job opportunities and skills!

๐Ÿ˜ Limited time offer!

          Project Material         

We provide different types of content to serve learning purposes in the most efficient manner.

๐Ÿ“ Educational Text Material

We provide comprehensive step-by-step tutorials designed to guide you through building and deploying your project on the cloud. You'll receive detailed instructions for uploading your project to GitHub and gain access to common interview questions and answers related to the project. These resources are tailored to enhance your understanding and boost your job preparation effectively.

โ–ถ๏ธ Comprehensive Video Lectures

For a more immersive learning experience, we offer video tutorials whenever needed. For instance, you can learn to build an interactive dashboard by closely following our step-by-step video lessons, making complex concepts easier to grasp and apply.

๐Ÿ’ฏ Assessments & Quizes

Validate your grasp of the technologies and their strategic application with carefully designed assessments and quizzes integrated within the course structure.

๐ŸŽ“ Certificate of Completion

Upon successful completion of the course, you will be awarded a professional Certificate of Completion. Showcase your accomplishment on platforms like LinkedIn and other social networks.

       Project Material       

We provide different types of content to serve learning purposes in the most efficient manner.

๐Ÿ“ Educational Text Material

We provide comprehensive step-by-step tutorials designed to guide you through building and deploying your project on the cloud. You'll receive detailed instructions for uploading your project to GitHub and gain access to common interview questions and answers related to the project. These resources are tailored to enhance your understanding and boost your job preparation effectively.

โ–ถ๏ธ Comprehensive Video Lectures

For a more immersive learning experience, we offer video tutorials whenever needed. For instance, you can learn to build an interactive dashboard by closely following our step-by-step video lessons, making complex concepts easier to grasp and apply.

๐Ÿ’ฏ Assessments & Quizes

Validate your grasp of the technologies and their strategic application with carefully designed assessments and quizzes integrated within the course structure.

๐ŸŽ“ Certificate of Completion

Upon successful completion of the course, you will be awarded a professional Certificate of Completion. Showcase your accomplishment on platforms like LinkedIn and other social networks.

YOUR INSTRUCTOR

Mike Chionidis

Freelance Data & AI Engineer, specialized in Leading Teams and Building Products
About me
๐Ÿ‘‹ I'm Mike, a Freelance Data & AI Engineer based in Greece. 

Throughout my career, I've worked with prominent clients like Publicis Groupe and delivered data products utilized by industry giants such as as Samsung, Three Mobile, and Western Union, and others. Within just two years of professional experience, I advanced to a Team Lead role, guiding a team of data professionals.

Previously, I've taught programming to university students to help them excel in their exams and assisted junior developers in kickstarting their careers.

My passion for sharing knowledge led to the establishment of DataProjects, with a clear purpose to help data enthusiasts secure their dream roles in the field. 

Let's embark on this learning adventure together, as we delve into the exciting world of data! ๐Ÿ’ซ 
YOUR INSTRUCTOR

Mike Chionidis

Freelance Data & AI Engineer, specialized in Leading Teams and Building Products
About me

๐Ÿ‘‹ I'm Mike, a Freelance Data & AI Engineer based in Greece. 


Throughout my career, I've worked with prominent clients like Publicis Groupe and delivered data products utilized by industry giants such as as Samsung, Three Mobile, and Western Union, and others. Within just two years of professional experience, I advanced to a Team Lead role, guiding a team of data professionals.

Previously, I've taught programming to university students to help them excel in their exams and assisted junior developers in kickstarting their careers.

My passion for sharing knowledge led to the establishment of DataProjects, with a clear purpose to help data enthusiasts secure their dream roles in the field. 

Let's embark on this learning adventure together, as we delve into the exciting world of data! ๐Ÿ’ซ 

Frequently asked questions

1. How much Python and SQL knowledge is required?

A fundamental proficiency in Python, specifically with the Pandas library, is necessary. You should have experience working with Pandas DataFrames. In terms of SQL, a solid understanding of fundamental statements such as Select, From, Where, and Join, as well as familiarity with SQL tables, is recommended.

2. Will I need to subscribe to any Cloud services?

No, you won't need to pay for any Cloud subscriptions. Google Cloud offers all new users a $300 credit to use within their first 90 days. Simply create an account with your credit card, activate the trial, and follow the provided instructions to deactivate automated renewal, as demonstrated in the project videos.

3. How long will I have access to the project?

You will have access to the project and its materials for the duration of your subscription. This includes all future updates related to the project during your subscription period.

4. Do you offer trainings for professionals/businesses?

Yes, based on request, we offer professional training on Data/Cloud technologies. For inquiries regarding professional trainings, please reach out to us at info@dataprojects.io with your specific details, or using our Contact Form.

5. Have more questions?

For any additional inquiries or clarifications, feel free to contact us at info@dataprojects.io or using our Contact Form.

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