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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FAQs
- Basic Python programming knowledge is recommended.
- Prior machine learning experience is helpful but not required.
- The course introduces ML concepts from scratch.
- Focuses on practical implementation using Python libraries.
- Suitable for beginners aiming to build a career in data science.
- Supervised learning: regression and classification algorithms.
- Unsupervised learning: clustering and dimensionality reduction.
- Model evaluation and selection techniques.
- Feature engineering and preprocessing with Python.
- Basics of neural networks and model optimization.
- Includes Python coding exercises for each ML model.
- Real-world datasets are used for practice.
- Projects cover end-to-end workflows from data preprocessing to evaluation.
- Encourages experimentation with hyperparameters and model tuning.
- Provides portfolio-ready examples for career use.
- scikit-learn for machine learning algorithms.
- pandas and NumPy for data handling.
- Matplotlib and Seaborn for data visualization.
- Jupyter Notebook for coding and experimentation.
- Optional introduction to TensorFlow or Keras for neural networks.
- Prepares for roles like data analyst, data scientist, or ML engineer.
- Builds practical experience with Python-based ML workflows.
- Supports portfolio development with real-world projects.
- Enhances problem-solving and predictive modeling skills.
- Provides a foundation for advanced AI and deep learning courses.
