What will you learn in this Machine Learning with Python Course
Understand core machine learning principles and how to apply them using Python.
Learn supervised and unsupervised learning techniques.
Perform model evaluation, validation, and refinement.
Build real-world machine learning models with Scikit-learn.
Apply learned techniques in a capstone project.
Program Overview
1. Introduction to Machine Learning
⏳ 1 week
Gain an understanding of what machine learning is, types of learning algorithms, and their applications in real-world scenarios.
2. Supervised Learning
⏳ 1 week
Learn regression and classification models, including linear regression, decision trees, and support vector machines.
3. Unsupervised Learning
⏳ 1 week
Explore clustering algorithms like k-means and hierarchical clustering, and study dimensionality reduction techniques.
4. Model Evaluation and Refinement
⏳ 1 week
Understand overfitting and underfitting, learn how to use metrics and validation strategies to improve model performance.
5. Building ML Models with Scikit-learn
⏳ 1 week
Hands-on training to implement and tune models using the Scikit-learn library.
6. Final Project
⏳ 1 week
Demonstrate mastery by completing a practical project using real data and machine learning techniques.
Get certificate
Job Outlook
High demand for professionals with machine learning expertise across industries.
Roles include Machine Learning Engineer, Data Scientist, and AI Specialist.
Competitive salaries and freelance opportunities in data-driven sectors.
Strong foundation for more advanced AI and deep learning studies.
Specification: Machine Learning with Python
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