IBM Machine Learning Professional Certificate

Prepare for a career in machine learning. Gain the in-demand skills and hands-on experience to get job-ready in less than 3 months.

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📖 What you'll learn

• Master the most up-to-date practical skills and knowledge machine learning experts use in their daily roles
• Learn how to compare and contrast different machine learning algorithms by creating recommender systems in Python
• Develop working knowledge of KNN, PCA, and non-negative matrix collaborative filtering
• Predict course ratings by training a neural network and constructing regression and classification models

📈 Skills you'll gain

🏆 Endorsements and recognition

• IBM Digital Badge: Receive a verified IBM Data Science Professional Certificate badge via Credly
• Career Support: Access IBM’s Talent Network with personalized job opportunities, skill-based recommendations, and interview tips
• Industry-Relevant Curriculum: Designed by IBM with a focus on real-world ML workflows—data prep, model building, evaluation, and deployment
• Career-Focused Learning: Prepares you for roles like Machine Learning Engineer, AI Specialist, and ML Operations Engineer

📚 Courses and lessons

1. Exploratory Data Analysis for Machine Learning

   • Retrieve data from multiple data sources: SQL, NoSQL databases, APIs, Cloud
   • Describe and use common feature selection and feature engineering techniques
   • Handle categorical and ordinal features, as well as missing values
   • Use a variety of techniques for detecting and dealing with outliers
   • Articulate why feature scaling is important and use a variety of scaling techniques

2. Supervised Machine Learning: Regression

   • Differentiate uses and applications of classification and regression in the context of supervised machine learning
   • Describe and use linear regression models
   • Use a variety of error metrics to compare and select a linear regression model that best suits your data
   • Articulate why regularization may help prevent overfitting
   • Use regularization regressions: Ridge, LASSO, and Elastic net

3. Supervised Machine Learning: Classification

   • Differentiate uses and applications of classification and classification ensembles
   • Describe and use logistic regression models
   • Describe and use decision tree and tree-ensemble models
   • Describe and use other ensemble methods for classification
   • Use a variety of error metrics to compare and select the classification model that best suits your data
   • Use oversampling and undersampling as techniques to handle unbalanced classes in a data set

4. Unsupervised Machine Learning

   • Explain the kinds of problems suitable for Unsupervised Learning approaches
   • Explain the curse of dimensionality, and how it makes clustering difficult with many features
   • Describe and use common clustering and dimensionality-reduction algorithms
   • Try clustering points where appropriate, compare the performance of per-cluster models
   • Understand metrics relevant for characterizing clusters

5. Deep Learning and Reinforcement Learning

   • Explain the kinds of problems suitable for Unsupervised Learning approaches
   • Explain the curse of dimensionality, and how it makes clustering difficult with many features
   • Describe and use common clustering and dimensionality-reduction algorithms
   • Try clustering points where appropriate, compare the performance of per-cluster models
   • Understand metrics relevant for characterizing clusters

6. Machine Learning Capstone

   • Compare and contrast different machine learning algorithms by creating recommender systems in Python
   • Predict course ratings by training a neural network and constructing regression and classification models
   • Create recommendation systems by applying your knowledge of KNN, PCA, and non-negative matrix collaborative filtering
   • Develop and deploy an application and evaluate your peers’ projects

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🚀 How to use this repo

This repo is open source! Feel free to:

• 👀 Browse the course readings, exercises, and case studies
• 💻 Fork/clone for your own self-study or review
• 🤝 Collaborate by submitting issues or improvements via pull requests
• 🌟 Get inspired if you’re preparing to be a data professional or want to level up your data skills

Disclaimer: All content is for educational purposes only and is shared to help aspiring data professionals. Please don’t submit this work as your own in graded assessments—let’s keep it ethical!

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✨ I’m always open to networking, collaboration, or sharing insights ✨
Don’t be shy — connect with me on LinkedIn! 👋