Data Foundations and Machine Learning Applications (Level 2)
Course Overview
SUMMARY:
Data Foundations and Machine Learning Applications (Level 2) is a comprehensive, hands-on course designed to equip participants with practical skills in data analysis, machine learning, and selected deep learning applications using Python.
The course begins with strong data foundations, enabling participants to understand different data types, perform data cleaning, visualization, and basic exploration. It then progressively introduces core machine learning concepts, covering supervised learning for classification and regression, as well as unsupervised learning through clustering techniques.
Participants are further exposed to deep learning fundamentals, with a focused introduction to Convolutional Neural Networks (CNNs) for image-based applications. The course also addresses one of the most in-demand real-world use cases—time series forecasting, introducing both traditional baseline methods and modern sequence models such as LSTM and GRU.
The program concludes with an end-to-end time series forecasting project, allowing participants to integrate data preparation, model development, evaluation, and result interpretation in a structured, real-world scenario. This ensures participants not only understand theoretical concepts but can confidently apply them to practical problems.
This course is specifically designed to provide participants with:
- A solid foundation in data handling, cleaning, and visualization using Python
- Practical experience with classification, regression, and clustering models for real-world datasets
- Exposure to deep learning concepts, including CNNs for image classification tasks
- Applied knowledge of time series forecasting, including feature engineering and sequence models (LSTM/GRU)
- Hands-on experience in building and evaluating end-to-end ML and DL pipelines
- The ability to interpret model performance and communicate insights effectively
Course Curriculum
- Course overview and learning outcomes
- Types of data: numerical, categorical, time series, images
- Basic data handling in Python (load, inspect, summarize)
- Data cleaning: missing values, duplicates, simple transformations
- Data visualization: distributions, relationships, correlations
- What is Machine Learning and Deep Learning
- ML workflow: problem, data, model, evaluation, iteration
- Supervised learning concepts: features, labels, train/test split
- Classification problem types and examples
- Basic classification models:
- - Logistic Regression (concept + simple example)
- - k-Nearest Neighbors (k-NN)
- - Decision Tree
- - Tree Ensemble
- Model evaluation for classification:
- - Accuracy, confusion matrix
- - Precision, recall, F1-score (concept)
- Hands-on end-to-end classification examples
- Regression problem definition and use cases
- Basic regression models:
- - Linear Regression
- - k-Nearest Neighbors (k-NN)
- - Tree Ensemble
- Regression evaluation metrics: MAE, MSE, RMSE, R²
- Introduction to unsupervised learning
- Clustering with k-Means: intuition, how it works
- Clustering example with 2D visualization and interpretation
- From traditional ML to Deep Learning: why neural networks
- Neuron, layers, activation functions
- CNN fundamentals:
- - Convolution, filters, feature maps
- - Pooling, flattening, fully connected layers
- Image data preparation: resizing, normalization, train/validation split
- Building a simple CNN for image classification
- Training and monitoring performance (loss, accuracy curves)
- Overfitting and basic regularization (dropout, simple augmentation concept)
- What is time series data and where it appears (energy, finance, sensors)
- Key concepts: trend, seasonality, noise, forecast horizon
- Train/validation/test split for time series (no shuffling)
- Feature construction for time series: lags, windows
- Baseline forecasting methods (naive last value, simple moving average)
- Introduction to sequence models for time series:
- - RNN concept (idea of memory)
- - LSTM and GRU (high-level gates and intuition)
- LSTM/GRU examples for one-step/multi-step forecasting
- Structured recap of the course: data → classification → regression → clustering → CNN → time series
- End-to-end ML/DL pipeline revisited using a time series case
- Time series project work:
- - Problem definition and dataset overview
- - Short EDA and preprocessing review
- - Model selection (baseline vs LSTM/GRU)
- - Training, evaluation, and plotting predictions vs actual
- Group or individual mini-presentations / discussion of project results
- Real-world considerations: data quality, domain knowledge, deployment concept
- Next steps for participants and Q&A
Learning Outcomes
Upon completion of this course, you will be able to:
- Handle, clean, and visualize different types of data using Python
- Build and evaluate classification models (Logistic Regression, k-NN, Decision Trees, Tree Ensembles)
- Develop regression models for continuous value prediction
- Apply clustering techniques using k-Means for unsupervised learning
- Understand deep learning fundamentals and build CNNs for image classification
- Implement time series forecasting using baseline methods and sequence models (LSTM/GRU)
- Build end-to-end ML and DL pipelines from data preparation to model evaluation
- Interpret model performance metrics and communicate insights effectively
- Apply learned concepts to real-world problems through hands-on projects
Technologies and Libraries You'll Learn
- Python - Core programming language
- NumPy - Numerical computing and array operations
- Pandas - Data manipulation and analysis
- Matplotlib/Seaborn - Data visualization
- Scikit-learn - Machine learning library (classification, regression, clustering)
- TensorFlow/Keras - Deep learning frameworks for CNNs and sequence models
- Jupyter Notebook - Interactive development environment
Who Should Take This Course?
- Data analysts and data scientists looking to strengthen their ML skills
- Python programmers wanting to enter the machine learning field
- Professionals seeking practical ML and DL application skills
- Graduates and working professionals interested in AI/ML careers
- Anyone wanting to understand how to apply ML to real-world problems
Key Benefits
- Comprehensive Coverage - From data foundations to advanced deep learning applications
- Hands-On Learning - Practical exercises and real-world projects throughout the course
- End-to-End Project - Complete time series forecasting project integrating all learned concepts
- Industry-Relevant Skills - Learn techniques directly applicable to real-world ML problems
- Deep Learning Exposure - Introduction to CNNs and sequence models for modern AI applications
- Practical Application - Build confidence in applying ML/DL to solve business problems
Prerequisites
This course is designed for participants with basic Python programming knowledge. Familiarity with Python basics (variables, data types, functions, basic data structures) is recommended. Prior experience with data analysis or machine learning is helpful but not required. The course starts with data foundations and progressively builds to advanced concepts, making it accessible to learners with varying backgrounds.
Certification
Upon successful completion of this course, you will receive a certificate from MASET (Malaysian Association of Skills Training). This certificate validates your practical skills in data analysis, machine learning, and deep learning applications, demonstrating your ability to build and evaluate ML/DL models, handle data effectively, and apply these skills to real-world problems.
The course includes hands-on exercises and an end-to-end time series forecasting project to ensure you have practical experience with ML/DL pipelines, preparing you for advanced AI and machine learning roles.
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