What will you learn in this IBM Introduction to Machine Learning Specialization Course
Understand the fundamentals of machine learning and its applications in various industries.
Perform exploratory data analysis, including data retrieval, cleaning, and feature engineering.
Implement supervised learning techniques such as regression and classification.
Apply unsupervised learning methods, including clustering and dimensionality reduction.
Develop practical skills through hands-on projects using real-world datasets.
Program Overview
1. Exploratory Data Analysis for Machine Learning
⏳ 14 hours
Learn to retrieve data from various sources, clean and preprocess it, and perform feature engineering to prepare for machine learning models.
2. Supervised Learning: Regression
⏳ 14 hours
Delve into regression techniques, including linear regression, ridge regression, and LASSO, to predict continuous outcomes.
3. Supervised Learning: Classification
⏳ 14 hours
Explore classification algorithms such as logistic regression, decision trees, and support vector machines to categorize data.
4. Unsupervised Learning
⏳ 14 hours
Understand clustering methods like K-means and hierarchical clustering, as well as dimensionality reduction techniques like PCA
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Job Outlook
Equips learners for roles such as Machine Learning Engineer, Data Scientist, and AI Analyst.
Applicable in industries like technology, healthcare, finance, and e-commerce.
Enhances employability by providing practical skills in machine learning and data analysis.
Supports career advancement in fields requiring expertise in predictive modeling and data-driven decision-making.
Specification: IBM Introduction to Machine Learning Specialization
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