What you will learn in IBM Machine Learning Professional Certificate Course
- Gain a solid foundation in machine learning (ML) and its real-world applications.
- Learn how to use Python, Scikit-Learn, TensorFlow, and IBM Watson for ML tasks.
- Master supervised, unsupervised, and reinforcement learning techniques.
- Understand the principles of data preprocessing, feature engineering, and model evaluation.
- Develop skills in deep learning, neural networks, and AI deployment.
- Apply your knowledge through hands-on projects and labs using real datasets.
Program Overview
Introduction to Machine Learning
⏱️2-4 weeks
- Understand the fundamentals of machine learning algorithms and AI concepts.
- Learn about supervised vs. unsupervised learning.
- Explore real-world applications of ML in various industries.
Data Science & Feature Engineering
⏱️ 4-6 weeks
- Learn how to clean, preprocess, and transform datasets for ML models.
- Understand the importance of feature selection and feature scaling.
- Use Python libraries like Pandas, NumPy, and Scikit-Learn for data analysis.
Supervised & Unsupervised Learning Techniques
⏱️ 6-8 weeks
- Implement algorithms like linear regression, decision trees, and clustering.
- Learn how to evaluate model performance using metrics like accuracy and RMSE.
- Understand bias-variance tradeoff and overfitting prevention techniques.
Deep Learning & Neural Networks
⏱️ 8-10 weeks
- Learn the fundamentals of deep learning and artificial neural networks (ANNs).
- Use TensorFlow and Keras to build and train deep learning models.
- Explore convolutional neural networks (CNNs) and recurrent neural networks (RNNs).
Capstone Project – End-to-End ML Model Deployment
⏱️ 10-12 weeks
- Apply all learned skills to develop and deploy a machine learning model.
- Work with real-world datasets to solve an industry problem.
- Showcase your project to enhance your portfolio and job prospects.
Get certificate
Job Outlook
- Machine Learning Engineer roles are growing rapidly, with a projected 22% job growth by 2030.
- The average salary for ML engineers ranges from $90K – $150K+, depending on experience.
- ML skills are in high demand across industries like finance, healthcare, e-commerce, and AI research.
- Employers seek professionals with expertise in Python, Scikit-Learn, TensorFlow, and AI frameworks.
- ML knowledge provides pathways into AI research, data science, and deep learning specialization.
Specification: IBM Machine Learning Professional Certificate
|
