INTRODUCTION

Machine learning is a type of artificial intelligence (AI) that allows software applications to become more accurate in predicting outcomes without being explicitly programmed to do so. Machine learning algorithms use historical data as input to predict new output values.

Machine learning is used in a wide variety of applications, including:

1. Fraud detection - Machine learning algorithms can be used to identify fraudulent transactions by analysing historical data. For example, a machine learning algorithm could be trained on a dataset of fraudulent transactions and learn to identify patterns that are common in fraudulent transactions. This information could then be used to flag potential fraudulent transactions in real time.

2. Spam filtering - Machine learning algorithms can be used to identify spam emails by analysing their content. For example, a machine learning algorithm could be trained on a dataset of spam emails and learn to identify words and phrases that are commonly used in spam emails. This information could then be used to filter out spam emails before they reach the user's inbox.

3. Image recognition - Machine learning algorithms can be used to identify objects in images. For example, a machine learning algorithm could be trained on a dataset of images of cats and learn to identify cats in new images. This information could then be used to build a search engine for images of cats or to develop a self-driving car that can identify cats on the road.

4. Natural language processing - Machine learning algorithms can be used to understand and interpret human language. For example, a machine learning algorithm could be trained on a dataset of text and learn to identify the sentiment of the text. This information could then be used to build a sentiment analysis tool that can help businesses understand how customers feel about their products or services.

5. Recommendation engines - Machine learning algorithms can be used to recommend products or services to users based on their past behavior. For example, a machine learning algorithm could be trained on a dataset of user purchase history and learn to recommend products that the user is likely to be interested in. This information could then be used to build a recommendation engine for e-commerce websites or streaming services.

Machine learning is a rapidly growing field with many potential applications. As the amount of data available continues to grow, machine learning algorithms will become increasingly accurate and powerful.

Some of the key concepts in machine learning:

1. 1. Algorithms - Machine learning algorithms are the mathematical models that are used to learn from data. There are many different types of machine learning algorithms, each with its own strengths and weaknesses.

   2. Data - Machine learning algorithms need data to learn from. The quality and quantity of data can have a significant impact on the accuracy of the algorithms.

   3. Training - Machine learning algorithms are trained on data. This process involves feeding the algorithms with data and allowing them to learn from it.

   4. Inference - Once a machine learning algorithm is trained, it can be used to make predictions. This process is called inference.

WHAT YOU WILL LEARN

In this bootcamp, you will learn:

1. Machine Learning Foundations - Learn the fundamental concepts, techniques, and methodologies of machine learning.

2. Data Science Libraries and Frameworks - Explore popular Python-based libraries and frameworks such as Scikit-learn, Anaconda, Spark, Keras, R Studio, Jupyter, XGBoost, LightGBM, TensorFlow, and PyTorch for data analysis and modeling.

3. Data Manipulation and Wrangling - Acquire skills in data manipulation and cleaning using tools like Excel, Python, SQL Server, and R to prepare data for analysis and modeling.

4. Data Visualisation - Master the art of visualising and presenting data effectively using tools like Excel, Power BI, and Qlik.

5. Advanced Machine Learning Techniques - Dive into advanced topics including deep learning, ensemble methods, and reinforcement learning.

6. Real-world Applications and Projects - Apply your knowledge through hands-on projects, solving real-world machine learning problems and gaining practical experience.

7. Model Deployment and Optimisation - Learn techniques for deploying and optimising machine learning models in production environments.

WHO THIS BOOTCAMP IS FOR

This bootcamp focuses towards engineers from various fields and are:

1. Aspiring Data Scientists - If you are interested in pursuing a career in data science and machine learning, this bootcamp will provide you with the foundational knowledge and practical skills to kickstart your journey.

2. Data Analysts and Data Engineers - If you are working with data and want to expand your skill set to include machine learning techniques, this bootcamp will help you enhance your data analysis and modeling capabilities.

3. Software Engineers and Developers - If you are a programmer or software engineer looking to explore the field of machine learning and leverage its techniques in your projects, this bootcamp will provide you with the necessary knowledge and tools.

4. Business Professionals and Decision Makers - If you work in a business or managerial role and want to gain a deeper understanding of machine learning to make data-driven decisions and leverage its potential for your organisation, this bootcamp will equip you with the necessary skills.

5. Anyone Interested in Machine Learning - If you have a keen interest in machine learning and want to expand your knowledge and skills in this field, this bootcamp will provide you with a comprehensive understanding and hands-on experience.

WHAT YOU WILL NEED

To take this course, you will need a computer with a working internet connection... and commitment to invest your time in up skilling your self in this new and exciting technologies.

HOW THIS COURSE WILL BENEFIT YOU

1. Comprehensive Knowledge and Skills - By completing the bootcamp, you will gain a deep understanding of machine learning concepts, techniques, and methodologies. You will develop practical skills in data analysis, modeling, and visualisation, as well as proficiency in using popular libraries and frameworks.

2. Hands-on Experience - The bootcamp emphasises hands-on learning through practical projects and exercises. You will have the opportunity to apply your knowledge to real-world datasets and develop a portfolio of projects that showcase your skills to potential employers.

3. Career Advancement Opportunities - Machine learning is in high demand across various industries, and acquiring skills in this field can open doors to exciting career opportunities. Whether you are looking to transition into a data science role or enhance your existing job prospects, this bootcamp equips you with the necessary skills to excel in the industry.

4. Industry-Relevant Tools and Techniques - The bootcamp focuses on using industry-standard tools and frameworks, ensuring that you are familiar with the tools commonly used in the field of machine learning. This knowledge will make you more competitive in the job market and enable you to contribute effectively to machine learning projects.

5. Practical Application - The bootcamp emphasises the practical application of machine learning techniques. You will learn how to approach real-world problems, analyse data, build models, and deploy them in production environments. This practical experience enhances your problem-solving abilities and prepares you for real-world challenges.

6. Lifelong Learning - Machine learning is a rapidly evolving field, and the bootcamp instills in you a mindset of continuous learning. You will be equipped with the necessary resources and skills to stay updated with the latest advancements in machine learning throughout your career.

JOB OPPORTUNITIES 

1. Machine learning engineer - Machine learning engineers are responsible for developing and deploying machine learning models. They typically have a background in computer science or engineering and have experience with machine learning algorithms.

2. Data scientist - Data scientists are responsible for collecting, cleaning, and analysing data. They typically have a background in statistics or mathematics and have experience with machine learning algorithms.

3. Software engineer - Software engineers who work on machine learning projects typically have a background in computer science and have experience with machine learning frameworks and libraries.

GENERAL COURSE GUIDE

The bootcamp will be taught by experienced engineers who will help students learn the skills they need to be successful in the technology/engineering industry. It is divided into 5 sections which are outlined below. Breakdown schedule of each section:

SPRINT 1: Fundamentals of Machine Learning

Course Objective:

By the end of the course, students will be able to:

1. Understand the fundamental concepts and components of machine learning frameworks.

2. Gain practical knowledge and skills in supervised and unsupervised learning techniques.

3. Explore various ML tasks such as classification, regression, object detection, and reinforcement learning.

4. Learn about performance optimisation in machine learning, considering people, process, and technology aspects.

5. Acquire programming proficiency in JavaScript, Python, Go, C, and C++ for machine learning applications.

6. Understand the hardware and infrastructure requirements for machine learning, including cloud computing, database management, and key-value stores.

7. Learn about the process and best practices for model serving, monitoring, explainable AI, and experiment management.

8. Gain hands-on experience in model development, validation, training, and data preprocessing using tools like Apache Airflow or Kubeflow.

Learning Outcomes:

By the end of this course, students will be able to:

1. Develop a solid understanding of machine learning frameworks and their components.

2. Apply supervised and unsupervised learning techniques to solve real-world problems.

3. Implement classification, regression, object detection, and reinforcement learning algorithms.

4. Optimise machine learning performance through effective people, process, and technology strategies.

5. Utilise JavaScript, Python, Go, C, and C++ programming languages for machine learning tasks.

6. Set up and manage hardware and infrastructure for machine learning, including cloud computing and database systems.

7. Deploy and serve machine learning models using appropriate technologies and frameworks.

8. Monitor and evaluate machine learning models for performance and explainability.

9. Manage experiments, validate models, preprocess data, and ensure data quality in machine learning workflows.

Prerequisites:

1. Basic understanding of programming concepts and syntax.

2. Familiarity with at least one programming language (preferably JavaScript, Python, or C++).

3. Knowledge of data structures and algorithms.

4. Understanding of statistics and mathematical concepts relevant to machine learning.

5. Familiarity with database management systems, including SQL and NoSQL databases.

6. Basic understanding of cloud computing concepts and technologies.

7. Some exposure to machine learning concepts and algorithms is beneficial but not mandatory.

Course Outline:

The course will be divided into the following modules:

1. Definition of ML Framework 

   1. 1. Supervised 

         1. 1. Labelling Tool Active Learning

            2. Classification

            3. Regression

            4. Object Detection

      2. Unsupervised 

         1. 1. Reinforcement Learning

2. Performance Optimisation 

   1. 1. People

      2. Process 

      3. Technology setup

3. Programming language

   1. 1. Javascript 

      2. Python

      3. Go

      4. C

      5. C++  

      6. Cube

4. Hardware / Infrastructure 

   1. 1. Cloud Computing 

      2. Database Management SQL (ORM or DAL) 

      3. NoSQL (Document CouchBase or MongoDB) 

      4. Key-Value (Redis or Memcached)

      5. RDBMS

5. Process 

   1. 1. Model Serving 

      2. TFX 

      3. Monitoring

      4. Explainable Al 

      5. Experiment Management 

      6. Model Development 

      7. Model Validation 

      8. Model Training 

      9. Data Validation 

      10. Data Pre-processing (Apache Airflow or Kubeflow) 

      11. Real-time or Batched (TensorRT or ONNX or TF Serving)

SPRINT 2: Implementing Machine Learning 

Course Objective:

By the end of the course, students will be able to:

1. Understand multithreading and parallel programming concepts to leverage concurrent processing capabilities.

2. Learn synchronisation techniques to ensure thread safety and prevent data race conditions.

3. Explore parallel programming frameworks like Ray for efficient and scalable distributed computing.

4. Gain knowledge of task-based programming models such as Thread Building Block (C++), async-await (C#), asyncio (Python), and goroutine (Go).

5. Understand serialisation techniques for efficient data storage and transfer, including model weight serialisation using Protobuf and JSON formats.

6. Master programming techniques, including design patterns, test-driven development, dependency injection, dump analysis, and functional programming paradigms

Learning Outcomes:

Upon completion of this course, students will be able to:

1. Develop proficiency in implementing multithreading and parallel programming solutions.

2. Apply synchronisation techniques to ensure thread safety and prevent data inconsistencies.

3. Utilise parallel programming frameworks like Ray to distribute workloads efficiently across multiple processors or machines.

4. Implement task-based programming models in various languages, such as C++, C#, Python, and Go.

5. Serialise and deserialise data efficiently using formats like Protobuf and JSON.

6. Apply various programming techniques, including design patterns, test-driven development, dependency injection, dump analysis, and functional programming principles.

Prerequisites:

Have completed the previous sprint.

Course Outline:

The course will be divided into the following modules:

1. Multithreading / processing

   1. 1. Synchronisation

      2. Parallel Programming (Ray)

      3. Task-based (Thread Building Block C++ / async-await C# / asyncio Python / goroutine Go

2. Serialisation

   1. 1. Model Weight

      2. Model

      3. Protobuf

      4. JSON

3. Programming techniques

   1. 1. Design Pattern

      2. Test driven development

      3. Dependency Injection

      4. Dump analysis

      5. Functional Programming

Assessment:

The course will be assessed through a combination of quizzes and exercises.

SPRINT 3: Advanced Machine Learning

Course Objective:

By the end of the course, students will be able to:

1. Maximise learner skills by providing advanced-level knowledge and skills in machine learning.

2. Develop a solid understanding of mathematical foundations essential for machine learning, including linear algebra, probability and statistics, and optimisation theory.

3. Explore Bayesian statistics and its application in machine learning algorithms.

4. Build skills in various types of machine learning techniques, including supervised learning, unsupervised learning (clustering, dimension reduction, generative adversarial network), semi-supervised learning, and reinforcement learning.

5. Understand the practical applications of reinforcement learning in bandit recommendation systems, reinforcement learning in production environments, and the development of scalable reinforcement learning agents.

Learning Outcomes:

By the end of this course, you will be able to:

1. Acquire in-depth knowledge and skills in the mathematical foundations of machine learning, including linear algebra, probability and statistics, and optimisation theory.

2. Apply Bayesian statistics to model uncertainty and make informed decisions in machine learning.

3. Implement and apply supervised learning algorithms to solve classification and regression problems.

4. Utilise various unsupervised learning techniques such as clustering, dimension reduction, and generative adversarial networks for data exploration and pattern discovery.

5. Develop a comprehensive understanding of semi-supervised learning and its applications in leveraging both labeled and unlabeled data.

6. Explore reinforcement learning algorithms, including bandit recommendation systems and scalable reinforcement learning agents, and understand their practical implementation in real-world scenarios.

Prerequisites:

Have completed the previous sprints.

Course Outline:

The course will be divided into the following modules:

1. Maximise Learner Skills

   1. 1. Mathematics - Linear Algebra / Probability & Stats / Optimisation Theory

      2. Bayesian Statistics

2. Type of Machine Learning - Skills Building (Advanced Level):

   1. 1. Supervised Learning

      2. Unsupervised Learning (Clustering / Dimension Reduction / Generative Adversarial Network)

      3. Semi-Supervised Learning (Mix the two above types and let the data develop its own understanding. The data is labeled under training data and the algorithm is free to explore.

      4. Reinforcement Learning (Bandits Recommendation / RL in Production / Scalable RL Agents)

Assessment:

The course will be assessed through a combination of quizzes and exercises.

SPRINT 4: Machine Processes And Scenarios

Course Objective:

By the end of the course, students will be able to:

1. Develop skills in data preparation techniques for machine learning, including data usage, processing, generation, and reporting.

2. Understand the structure and processing skills required for different types of data, including text, numbers, video, images, and sound.

3. Learn data visualisation techniques for effective presentation and analysis of data, considering different visual types and categories.

4. Gain knowledge of DataOps principles and practices for efficient data management and processing in machine learning projects.

5. Understand the importance of governance and MLOps (Machine Learning Operations) in ensuring ethical and responsible use of data and models.

6. Explore application systems and structures relevant to data processing and machine learning.

7. Learn risk management and control measures to mitigate potential risks associated with data management and machine learning projects.

Learning Outcomes:

By the end of this course, students will be able to:

1. Acquire practical skills in preparing data for machine learning projects, including data processing, cleaning, and transformation.

2. Apply appropriate techniques for handling different types of data, such as text, numerical data, images, videos, and sound.

3. Develop proficiency in data visualisation techniques and effectively communicate insights through visual presentations.

4. Understand DataOps principles and implement efficient data management processes and procedures.

5. Comprehend the significance of governance and MLOps in ensuring compliance, ethical use of data, and effective deployment of machine learning models.

6. Gain knowledge of application systems and structures relevant to data processing and machine learning, enabling effective integration of models into production systems.

7. Implement risk management and control practices to ensure data integrity, security, and compliance with regulatory requirements.

Prerequisites:

Have completed the previous sprints

Course Outline:

The course will be divided into the following modules:

1. Data preparation for ML

   1. 1. Different types of data usage / process / generating / reporting

      2. Structure of processing skills - Data types: text, numbers, video, image, sound.

2. Data Visualisation:

   1. 1. Type of visual usage / presentation

      2. Categories of item usage

3. DataOps

   1. 1. Process / Procedure

         1. 1. Governance & MLOps

            2. Application System and Structure

      2. Risk Management & control

         1. 1. Data Management

            2. Machine Learning

            3. Model Development

Assessment:

The course will be assessed through a combination of quizzes and exercises.

SPRINT 5: Programming Language Learning

Course Objective:

By the end of the course, students will be able to:

1. Develop proficiency in machine learning libraries and frameworks for data analysis and modeling, with a focus on Python-based tools.

2. Gain practical knowledge and skills in popular data science libraries and frameworks, including Scikit-learn, Anaconda, Spark, Keras, R Studio, Jupyter, XGBoost, LightGBM, TensorFlow, and PyTorch.

3. Learn data visualisation techniques using Excel, Power BI, and Qlik for effective presentation and analysis of data.

4. Acquire data wrangling skills to manipulate and clean data using tools such as Excel, Python, SQL Server, and R.

Learning Outcomes:

By the end of this course, you will be able to:

1. Apply machine learning libraries and frameworks in Python to perform data analysis and develop predictive models.

2. Utilise popular data science tools and frameworks, such as Scikit-learn, Anaconda, Spark, Keras, R Studio, Jupyter, XGBoost, LightGBM, TensorFlow, and PyTorch, for various data science tasks.

3. Visualise and present data using Excel, Power BI, and Qlik to effectively communicate insights and patterns.

4. Manipulate and clean data using Excel, Python, SQL Server, and R to prepare it for analysis and modeling.

Prerequisites:

Have completed the previous sprints

Course Outline:

The course will be divided into the following modules:

1. Machine Learning (Python Libraries & Framework):

2. Data Science (You will learn two of these libraries/frameworks)

   1. 1. Scikit-learn

      2. Anaconda

      3. Spark

      4. Keras

      5. R Studio

      6. Jupyter

      7. XGBoost

      8. LightGBM

      9. TensorFlow

      10. PyTorch

3. Visualisation

   1. 1. Excel / Power BI

      2. Output Qlik. Q

4. Data Wrangling - Data Manipulation / Clean Data

   1. 1. Excel

      2. Python / SQL Server / R

Assessment:

The course will be assessed through a combination of quizzes and exercises.

Successful completion of this bootcamp will equip participants with the knowledge, skills, and practical experience needed to confidently tackle machine learning projects, contribute to data-driven decision-making, and pursue a rewarding career in the field of machine learning and data science:

1. Possess a comprehensive understanding of machine learning concepts, techniques, and methodologies.

2. Have practical skills in data analysis, modeling, and visualisation using popular libraries and frameworks.

3. Be proficient in programming languages commonly used in machine learning, such as Python and R.

4. Understand the different types of machine learning algorithms and when to apply them.

5. Be able to preprocess and manipulate data for machine learning tasks.

6. Have experience with real-world machine learning projects and a portfolio of projects to showcase your skills.

7. Understand best practices for model evaluation, optimisation, and deployment.

8. Be familiar with industry-standard tools and technologies used in machine learning.

9. Have the ability to interpret and communicate machine learning results effectively.

10. Be prepared for various roles in the field of machine learning, such as data scientist, machine learning engineer, or data analyst.

11. Have a strong foundation for further learning and specialisation in specific areas of machine learning.

YOUR TRAINERS

Dr Harjinthar Singh -  is a Principal Trainer at Marc & Zed. He has over 25 years of experience in the IT industry, and has worked as a trainer, lecturer, and consultant for software development, product design, user interface, user experience, data analysis, RDBMS, video and image editing, and mobile development.

Dr. Singh has taught in Singapore, the United Kingdom, Malaysia, and Australia. He started his teaching career in 2001 as a lecturer for software engineering at London South Bank University. In 2012, he joined a Malaysian government agency, MIMOS Berhad. From 2016 to 2017, he taught Software & Mobile Development for undergraduates, staff re-training programmes, and post-graduates intending to pursue a career as programmers and developers.

Since 2017, Dr. Singh has conducted training and workshops in UI/UX, Interaction Design, Design Thinking, DevOps, MERN FullStack, Agile, JIRA, Git/GitLab, MySQL, MS SQL Server 2016, Infographics, Graphics/Video, and mobile/web development. He is also a certified Scrum Master and Product Owner.

Dr. Singh is a highly experienced and qualified trainer, and has a wealth of knowledge and experience in the IT industry. He is passionate about teaching and helping others to learn, and is committed to providing high-quality training that meets the needs of his clients.

Dr Khairul Anuar Abd Wahid -  is a Senior Trainer at Marc & Zed. He has over 15 years of experience in the IT industry, and has worked as a trainer, lecturer, and consultant for software development, data science, machine learning, artificial intelligence, and cloud computing.

He has taught in Singapore, Malaysia, and the United States. He started his teaching career in 2007 as a lecturer for software engineering at the National University of Malaysia. In 2012, he joined a Silicon Valley startup, where he worked on developing machine learning algorithms for fraud detection.

Since 2017, Dr. Khairul has conducted training and workshops in Python, R, Machine Learning, Artificial Intelligence, Cloud Computing, and Data Science. He is also a certified Data Scientist and Machine Learning Engineer. He is a highly experienced and qualified trainer, and has a wealth of knowledge and experience in the IT industry. He is passionate about teaching and helping others to learn, and is committed to providing high-quality training that meets the needs of his clients.

Dr. Khairul is a valuable asset to the Marc & Zed Training team, and his expertise in data science and machine learning is highly sought after by businesses in Singapore and Malaysia. He is a passionate educator who is committed to helping others learn and grow.

Djoshkun Diko - has been working as a developer, trainer, coach, and consultant in software engineering since 2008. His expertise includes FullStack, DevOps, Cloud Computing (Amazon Web Services & Google Cloud Platform), PHP, JavaScript, C++, Laravel, Docker, Kubernetes, Golang, VueJS, Python, Shell scripting, HTML5/CSS, MySQL, MariaDB, PostgreSQL, MSSQL Server, Cassandra, and MongoDB.

Throughout his career as a Software Architect/developer/trainer, he has been involved in designing and executing distributed system architecture principles and patterns for applied machine learning products. He has contributed to various projects involving technologies such as Laravel, Symfony, Prestashop, NodeJS, ExpressJS, VueJS, MySQL, MongoDB, PostgreSQL, Camunda Microservices architecture with gRP, GoLang/Python & Echo (Go framework), Flask & Panda libraries (Python Framework), Angular, Docker & Kubernetes, and JIRA & Confluence (Atlassian products).

During his freelance career, he has collaborated with several companies, developing web pages, web shops, and forums using platforms such as Joomla, Wordpress, vBulletin, MyBB, and HTML.

In 2017, he joined Marc & Zed SPACES in Kuala Lumpur as an Assistant Trainer. Although he left Marc & Zed in 2019, his interest in the training field brought him back in February 2020 as a Principal Trainer and Coach. In this role, he conducts hybrid trainings in Singapore, Germany, and Malaysia. He has also taken on web development projects for Marc & Zed, including developing their own website and creating a CMS website for propertysifu.com.my, and providing training for their staff. Currently, he is working on developing a website and providing training for another client of Marc & Zed, Cameron Adams UK Ltd., a real-estate agency.