About This Course
The Machine Learning Engineering Level 1 course provides a comprehensive foundation in machine learning, guiding students through core concepts and practical skills essential for real-world applications. Beginning with statistics and information theory, the course builds a strong theoretical base to help students understand data and probability. Students then dive into Python programming, exploring essential libraries for data manipulation and visualization. With data preprocessing and feature engineering, learners will gain hands-on experience in preparing datasets for model training. Finally, the course covers key machine learning algorithms, model evaluation techniques, and an introduction to model deployment, equipping students with the knowledge and skills to develop and implement effective machine learning models.
Learning Objectives
Material Includes
- Course lecture videos and slides
- Reading materials (eBooks, articles)
- Assignments and practical exercises
- Quizzes for knowledge check
- Capstone project instructions
- Discussion forum or community platform for support
Requirements
- Laptop/PC with Internet Access
- Python Programming Environment (Install Python, Jupyter, or Google Colab)
- Essential Python Libraries (NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn)
- Access to Course Materials (Videos, Slides, Readings)
- Text Editor or IDE (VS Code, PyCharm, or Sublime Text)
- Assignment and Project Submissions (Complete regular assignments and capstone project)
- Discussion Platform/Forum (For peer discussions and support)
- Practical Exercises (Apply concepts through coding exercises)
- Assessment and Quizzes (Complete quizzes after each module)
- Capstone Project (Solve a real-world problem using machine learning)
Target Audience
- Beginners in Machine Learning: Individuals with little to no prior experience in machine learning who are eager to learn the basics and build a strong foundation.
- Students in Data Science, AI, and Related Fields: University students or recent graduates looking to strengthen their machine learning skills for academic or career purposes.
- Aspiring Data Analysts and Scientists: Professionals in related fields who want to shift into data analysis or data science by building their machine learning skills.
- Software Engineers and Developers: Programmers with basic Python knowledge who want to expand their skill set into machine learning and explore data-driven development.
- Business Analysts and Managers: Professionals interested in leveraging machine learning concepts for business insights, data-driven decision-making, and automation.
- Career Changers: Individuals looking to transition to a tech-focused role in AI, machine learning, or data science.
- Tech Enthusiasts: Anyone with a keen interest in AI and technology, looking to understand the fundamentals of machine learning and build practical skills.
Curriculum
Foundational Concepts
Introduction to Machine Learning00:00:00
Supervised vs Unsupervised Learning00:00:00
Data Preprocessing
Basic Algorithms and Models
Model Evaluation and Tuning
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Material Includes
- Course lecture videos and slides
- Reading materials (eBooks, articles)
- Assignments and practical exercises
- Quizzes for knowledge check
- Capstone project instructions
- Discussion forum or community platform for support
