Lvl.1 -> Machine Learning Apprentice
Pre-requisite: N/A (this path is suitable for complete beginners)
Approx. Time Required: 1 month
✅ NEW! Join our Mentorship Program for this Career Path here. 👈
Embark on a transformative journey into the world of machine learning with our Machine Learning Apprentice path, tailored for complete beginners and designed to be completed in just one month. Start with the Executive Briefing: Machine Learning to demystify the essentials of ML, understanding its impact and strategic value in business. Dive deeper into the technical realm with Python A-Z, where you’ll gain hands-on experience in data manipulation, visualization, and model building using Python, a key language in data science.
Progress to Machine Learning Level 1, an introductory course that lays the groundwork in machine learning concepts—from regression and classification to clustering—equipping you with a foundational understanding and practical skills to tackle more complex ML challenges. Continue building your ML expertise with Introduction to Feature Engineering, where you’ll learn how to transform raw data into high-quality inputs that boost model performance and reliability.
Finally, round out your skill set with Intro to Git & GitHub, a practical guide to version control and collaboration that ensures you’re ready to work confidently in any professional or open-source machine learning environment.
This path is your first step towards mastering machine learning, setting you on course to unlock the potential of AI and data-driven decision-making in your future endeavors.
1.Executive Briefing: Machine Learning
This concise, executive-level course demystifies Machine Learning (ML) for business leaders, providing a clear understanding of what ML is, why it’s important, and how it can be applied within your organization to drive strategic advantage.
- Grasp the fundamental concepts of Machine Learning and its significance in today’s data-driven world.
- Learn how to align ML strategies with business objectives to solve real-world problems, improve operational efficiencies, and innovate products and services.
- Understand the essentials of data management and the role of high-quality data in successful ML projects.
- Get acquainted with the tools and technologies that power ML, and the process of building and managing an effective ML team.
- Gain insights through technical case studies on regression, classification, clustering, association rule learning, and deep learning, providing a comprehensive view of ML applications.
Armed with knowledge from this course, you’ll be equipped to make informed decisions about implementing ML in your business. Whether it’s identifying potential use cases, strategizing ML integration, or leading your team towards a data-centric approach, you’ll have the confidence and understanding needed to guide your organization into the future of intelligent technology and data-driven decision-making.
Master Python, the versatile language at the heart of data analysis, machine learning, and AI.
- Write Python code for data manipulation and prepare datasets for analysis.
- Visualize data with libraries like Matplotlib and Seaborn.
Gain a robust set of programming skills to analyze data, build models, and automate data processes.
Embark on a comprehensive journey into the world of machine learning with this introductory course. Designed for beginners, it covers the basics of regression and classification, as well as the complexities of clustering.
- Understand the machine learning process, including the preparation of datasets and the use of tools like Google Colab and ChatGPT to enhance your ML projects.
- Master regression techniques, including Simple and Multiple Linear Regression, and learn how to evaluate models using R-squared and Adjusted R-squared metrics.
- Dive into classification through Logistic Regression, exploring concepts like maximum likelihood, feature scaling, and model evaluation with confusion matrices.
- Explore clustering with K-Means, understand the elbow method for optimal cluster selection, and get hands-on experience with K-Means++ for improved clustering initialization.
By the end of this course, you will have a solid foundation in machine learning, equipped with the knowledge to build, evaluate, and improve your own machine learning models. This course sets the stage for further exploration and specialization in the vast field of machine learning.
4.Introduction to Feature Engineering
Unlock the true potential of your data with feature engineering, one of the most critical steps in building effective machine learning models. This course introduces you to the techniques that transform raw data into meaningful inputs, allowing algorithms to learn more efficiently and perform better. Perfect for beginners, this course provides both foundational and intermediate strategies to prepare real-world datasets for success.
- Understand the core principles of feature engineering and why it matters in machine learning.
- Learn how to handle missing data, apply feature scaling techniques, and encode categorical variables for model readiness.
- Dive into more advanced topics like engineering temporal features, managing imbalanced datasets, and working with high-cardinality variables.
- Explore practical feature selection techniques to identify the most impactful variables for your model.
By the end of this course, you’ll have a solid grasp of feature engineering best practices and the hands-on skills to transform messy datasets into high-quality inputs for machine learning models.
Master the essential tools for version control and collaboration in modern software and machine learning projects. This hands-on course introduces you to Git and GitHub, empowering you to track changes in your code, collaborate seamlessly with others, and manage your projects like a professional. Whether you’re working solo or as part of a team, understanding Git and GitHub is a foundational skill for any machine learning journey.
Set up Git and GitHub on your machine and understand the basics of version control.
Learn how to create repositories, track changes, and manage your project history using Git via the terminal.
Explore how to use Git and GitHub directly from your code editor for a more streamlined workflow.
Collaborate effectively with others using GitHub—create pull requests, review code, and resolve merge conflicts.
Dive into advanced Git concepts such as branching and merging to manage multiple versions of your project with ease.
By the end of this course, you’ll be confident using Git and GitHub to support your machine learning projects and work efficiently within a team or open-source environment.
Plus, check out this Live Lab Recording:
Lab #11: Machine Learning for Regression and Classification – Level 1
Unlocks: Lvl.2 Machine Learning Engineer, Lvl.2 Generative AI Engineer, Lvl.2 AI Engineer
👉 Remember to join the Mentorship Program [here] to grow even faster!