Applied Machine Learning with Python
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Next Start
October 12, 2026
(Offered monthly)Duration
5 Days
ClassroomProgram Fee
$1,500
Exclusive 16% VATOther Dates
November 9, 2026
December 14, 2026
December 14, 2026
Course Description
This comprehensive course provides a practical foundation in machine learning using Python. Participants will learn data preprocessing techniques, build supervised and unsupervised machine learning models, and apply them to real-world datasets using Python's scientific computing ecosystem.
The curriculum progresses from data preprocessing and machine learning basics to supervised learning (regression and classification), unsupervised learning (clustering), and dimensionality reduction techniques. Participants will work with NumPy, Pandas, Scikit-learn, SciPy, and Matplotlib to build, evaluate, and interpret machine learning models.
Prerequisite note: This course requires basic Python programming knowledge and familiarity with Pandas and NumPy. Participants should be comfortable with data manipulation and basic statistical concepts before enrolling.
Course Outline
This course is structured into 5 core modules, each carefully designed to build progressively on the previous one.
Learning Outcomes
Technical Skills
- Preprocess raw data for machine learning using Python.
- Handle missing data and encode categorical variables.
- Split datasets and apply feature scaling for ML models.
- Build and evaluate simple and multiple linear regression models.
- Implement polynomial regression and support vector regression (SVR).
- Build decision tree and random forest regression models.
- Evaluate regression models using R², adjusted R², and residuals.
- Build and evaluate logistic regression models for classification.
- Implement K-Nearest Neighbors (KNN) and Support Vector Machines (SVM).
- Build decision tree and random forest classification models.
- Perform K-Means and hierarchical clustering for unsupervised learning.
- Apply Principal Component Analysis (PCA) for dimensionality reduction.
- Implement Linear Discriminant Analysis (LDA) and Kernel PCA.
- Visualize machine learning models and decision boundaries using Matplotlib.
- Evaluate model performance using confusion matrices and accuracy metrics.
Career Benefits
- Build a strong foundation in machine learning using Python.
- Learn to preprocess and prepare data for ML models.
- Develop practical skills in supervised and unsupervised learning.
- Gain hands-on experience with Scikit-learn and Pandas.
- Learn to build, evaluate, and interpret various ML models.
- Strengthen quantitative and analytical skills for data science roles.
- Build a portfolio of ML projects for career advancement.
Who Should Attend
- Data Scientists — Professionals building and deploying ML models.
- Data Analysts — Analysts transitioning to predictive modeling.
- Software Engineers — Developers integrating ML into applications.
- Researchers — Academics applying ML to research problems.
- Business Analysts — Professionals using ML for data-driven decisions.
- Students — Learners building practical ML skills.
- Career Changers — Individuals entering the data science field.
Prerequisites & Requirements
Prerequisites
- Basic Python programming knowledge is required.
- Familiarity with Pandas and NumPy is required.
- Basic understanding of statistics and linear algebra is recommended.
- Comfort with data manipulation and exploratory data analysis.
- A willingness to work with quantitative data and ML concepts.
Requirements
- Laptop with at least 2GB RAM (GB recommended for smoother performance).
- All learning facilities are provided on-site, including high-speed internet, projectors, writing materials, and refreshments.
- Administrative rights on your laptop to install R, RStudio, and necessary packages.
- Sufficient free disk space (at least 2GB) for software installation and project files.
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