This course provides a comprehensive introduction to Machine Learning; a branch of Artificial Intelligence (AI) that enables computers to learn from data and make predictions or decisions without being explicitly programmed.
Students will learn the fundamentals of data preprocessing, exploratory data analysis, supervised and unsupervised learning, model evaluation, and optimization techniques. The course covers popular ML algorithms such as Linear Regression, Logistic Regression, Decision Trees, Random Forests, Support Vector Machines (SVM), K-Means Clustering, and Neural Networks.
Participants will gain hands-on experience using Python and industry-standard libraries such as NumPy, Pandas, Matplotlib, Scikit-learn, and TensorFlow. Through practical projects and real-world datasets, learners will develop the skills needed to build, train, evaluate, and deploy machine learning models.
Learning Outcomes:
- Understand core machine learning concepts and workflows.
- Prepare and clean datasets for analysis.
- Build and evaluate predictive models.
- Apply supervised and unsupervised learning techniques.
- Use Python tools and libraries for ML development.
- Solve real-world problems using data-driven approaches.
Prerequisites:
- Basic knowledge of Python programming.
- Familiarity with mathematics and statistics is helpful but not mandatory.
Target Audience:
Students, software developers, data analysts, aspiring data scientists, and professionals interested in AI and machine learning.
About the instructor
An expert in his industry, Arthur Wells is accomplished. He’s built this learning platform to help others in his industry learn and grow.
