What you'll get
- 12+ Hours
- 4 Courses
- Course Completion Certificates
- Self-paced Courses
- Technical Support
- Case Studies
Synopsis
- Learn essential machine learning concepts and techniques using Python.
- Gain proficiency in data preprocessing, model training, and evaluation with Python libraries.
- Apply machine learning to real-world case studies and hands-on projects.
- Build data pipelines to analyze, visualize, and extract insights from datasets.
- Develop a portfolio of data science projects using real-world data.
- Analyze datasets independently and generate meaningful insights.
- Master critical data science skills and understand machine learning end-to-end.
- Work with NumPy for numerical processing and Pandas for data manipulation.
- Conduct feature engineering on real-world case studies.
- Create supervised learning algorithms to predict outcomes.
- Use Matplotlib to design fully customized visualizations.
- Replicate real-world scenarios and data reports to gain practical experience.
Content
| Courses | No. of Hours | Certificates | Details |
|---|---|---|---|
| Machine Learning with Python Course | 5h 17m | ✔ | View Curriculum |
| Project on Machine Learning - Covid19 Mask Detector | 2h 05m | ✔ | View Curriculum |
| Data Science with Python Project-Predict Diabetes on Diagnostic Measures | 1h 02m | ✔ | View Curriculum |
| Courses | No. of Hours | Certificates | Details |
|---|---|---|---|
| Machine Learning Case Studies | 4h 5m | ✔ | View Curriculum |
Description
This course offers a thorough introduction to machine learning with Python, combining theory with practical experience on real-world data.
Section 1: Machine Learning with Python 2024
Participants will master the essentials of machine learning, including key concepts, algorithms, and techniques for data preprocessing, model training, and evaluation. They will work with popular Python libraries such as Scikit-learn and TensorFlow to implement machine learning solutions.
Section 2: Projects and Case Studies on Machine Learning
This section focuses on applying machine learning concepts to hands-on projects using real-world datasets. Participants will practice feature engineering, model selection, optimization, and performance evaluation, reinforcing their understanding and building practical skills.
Participants will build Python-based machine learning skills through interactive exercises, hands-on demonstrations, and practical projects. By the end, they will have the knowledge and skills to confidently implement machine learning algorithms across diverse datasets and problem domains.
Requirements
- No prior knowledge of machine learning required.
- Basic familiarity with Python programming.
Target Audience
- Individuals interested in data and analytics.
- Data engineers, analysts, and architects.
- Software engineers and IT operations professionals.
- Technical managers seeking to understand data-driven decision making.
