Machine Learning (ML) is reshaping industries and revolutionizing decision-making by enabling systems to learn from data and improve over time without explicit programming. As organizations increasingly collect vast amounts of data, ML provides the tools to uncover insights, forecast trends, automate operations, and personalize customer experiences.
This five-day hands-on training equips participants with a deep understanding of the core concepts, techniques, and real-world applications of machine learning. It blends theory with coding exercises and project-based learning using Python and widely-used ML libraries such as Scikit-learn, Pandas, and TensorFlow.
Participants who successfully complete the course will receive a Certificate of Participation.
Course Objectives
By the end of the course, participants will be able to:
- Understand the fundamentals and taxonomy of machine learning.
- Distinguish between supervised, unsupervised, and reinforcement learning.
- Apply key ML algorithms to real-world datasets.
- Preprocess and clean data effectively for ML models.
- Build, train, evaluate, and tune ML models using Python.
- Understand performance metrics and model evaluation techniques.
- Identify and avoid common pitfalls like overfitting and data leakage.
- Integrate ML models into business processes and systems.
- Explore ethical considerations and responsible AI practices.
- Use ML tools and frameworks to prototype predictive applications.
Who Should Attend
This course is suitable for:
- Data Analysts and Scientists
- Software Developers and Engineers
- Statisticians and Economists
- Business Intelligence Professionals
- IT and System Architects
- Research Officers and Academicians
- Product Managers and Tech Entrepreneurs
- Professionals in Finance, Health, Marketing, Telecom, Logistics, etc.
- Anyone with basic programming knowledge and interest in data science
Course Duration
10 Days
Course Outline
Module 1: Introduction to Machine Learning and the Data Science Workflow
- What is Machine Learning? History and Evolution
- ML vs Traditional Programming
- Types of Machine Learning:
- Supervised Learning
- Unsupervised Learning
- Semi-Supervised and Reinforcement Learning
- Applications and Use Cases across Industries
- Introduction to the Data Science Workflow
- Installing Python, Jupyter Notebook, and Anaconda
- Overview of Python for Data Science (NumPy, Pandas, Matplotlib)
- Exploratory Data Analysis (EDA) Basics
- Introduction to Scikit-learn and ML Pipelines
Module 2: Supervised Learning – Regression and Classification
- Understanding Supervised Learning
- Linear Regression: Theory, Implementation, and Evaluation
- Multiple and Polynomial Regression
- Classification Problems and Algorithms
- Logistic Regression
- Decision Trees
- k-Nearest Neighbors (k-NN)
- Support Vector Machines (SVM)
- Performance Evaluation Metrics
- MAE, MSE, RMSE (for regression)
- Accuracy, Precision, Recall, F1 Score, ROC-AUC (for classification)
- Cross-validation and Model Selection
- Hyperparameter Tuning with GridSearchCV
Module 3: Unsupervised Learning and Dimensionality Reduction
- Understanding Unsupervised Learning
- Clustering Algorithms
- K-Means Clustering
- Hierarchical Clustering
- DBSCAN
- Dimensionality Reduction
- Principal Component Analysis (PCA)
- t-SNE (brief overview)
- Feature Scaling and Normalization Techniques
- Visualizing High-Dimensional Data
- Real-World Use Cases: Customer Segmentation, Anomaly Detection
- Hands-on Case Study: Segmenting Bank or Retail Customers
Module 4: Advanced Topics – Ensemble Methods and Neural Networks
- Ensemble Learning Concepts
- Bagging and Boosting
- Random Forests
- Gradient Boosting Machines (GBM)
- XGBoost and LightGBM
- Introduction to Neural Networks
- Structure of a Neural Network
- Activation Functions
- Forward and Backpropagation Concepts
- Using Keras/TensorFlow for Deep Learning
- Image Classification and NLP Basics (optional overview)
- Model Interpretability: SHAP, LIME
- Avoiding Overfitting: Regularization, Dropout
- Hands-on: Building a Neural Network for Classification
Module 5: Model Deployment, Ethics, and Capstone Project
- From Notebook to Production: Model Deployment Basics
- Using Flask/FastAPI to Deploy ML Models
- ML in the Cloud (overview: AWS SageMaker, GCP AI, Azure ML)
- Responsible AI and Ethical Considerations
- Bias and Fairness
- Data Privacy and Security
- Explainability in AI Systems
- ML Workflow in Organizations – MLOps (Intro)
- Capstone Project:
- Participants work in groups or solo to build an end-to-end ML model
- Data Cleaning, EDA, Model Building, Evaluation, and Deployment
- Group Presentations and Peer Feedback
- Final Q&A and Resources for Further Learning
General Notes
- The instructor led trainings are delivered using a blended learning approach and comprises of presentations, guided sessions of practical exercise, web-based tutorials and group work. Our facilitators are seasoned industry experts with years of experience, working as professional and trainers in these fields.
- The participants should be reasonably proficient in English as all facilitation and course materials will be offered in English.
- Upon successful completion of this training, participants will be issued with a certificate.
- The training will be held at Kincaid Training Centre. The course fee covers the course tuition, training materials, two break refreshments and lunch.
- All participants will additionally cater for their, travel expenses, visa application, insurance, and other personal expenses.
- Accommodation and airport pickup are arranged upon request. For reservations contact the Training coordinator at Email: training@kincaiddevelopmentcenter.org or Tel: +254 724592901
- This training can also be customized to suit the needs of your institution upon request. You can have it delivered in our Kincaid Training Centre or at a convenient location.
For further inquiries, please contact us on Tel: +254 724592901 or send mail to training@kincaiddevelopmentcenter.org.
Payments are due upon registration. Payment should be sent to our Bank account before commencement of training and proof of payment sent to training@kincaiddevelopmentcenter.org.