MACHINE LEARNING FUNDAMENTALS (3 DAYS)
TARGET AUDIENCE
Data scientists, analysts, and developers interested in learning Machine Learning techniques for data analysis and predictive modelling.
DESCRIPTION
3-DAY COURSE
This offers a comprehensive introduction to machine learning concepts, algorithms, and techniques, covering both theoretical foundations and practical applications.

LEARNING OUTCOMES
- Introduction to Machine Learning: Understand the fundamentals of machine learning, including Supervised Learning, Unsupervised Learning, and Reinforcement Learning.
- Data Pre-processing: Learn how to pre-process and clean datasets for Machine Learning tasks, including handling missing values, scaling features, and encoding categorical variables.
- Supervised Learning Algorithms: Explore supervised learning algorithms such as Linear Regression, Logistic Regression, Decision Trees, Random Forests, Support Vector Machines (SVM), and K-Nearest Neighbours (KNN).
- Unsupervised Learning Algorithms: Understand Unsupervised Learning algorithms such as K-Means Clustering, Hierarchical Clustering, Principal Component Analysis (PCA), and T-Distributed Stochastic Neighbour Embedding (t-SNE).
- Model Evaluation: Learn how to evaluate machine learning models using performance metrics such as accuracy, precision, recall, F1-score, and ROC curves.
- Cross-Validation: Explore techniques for model validation and selection, including K-Fold cross-validation and hyper-parameter tuning using Grid Search.
- Feature Selection and Engineering: Gain familiarity with feature selection techniques and feature engineering methods to improve model performance and interpretability.
- Ensemble Learning: Understand the concept of ensemble learning and its applications, including bagging, boosting, and stacking techniques.
- Deep Learning: Learn about deep learning concepts and architectures, including Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN).
- Natural Language Processing (NLP): Explore basic NLP techniques such as text preprocessing, sentiment analysis, Named Entity Recognition (NER), and text classification using Machine Learning Models.
- Time Series Analysis: Gain familiarity with time series analysis techniques, including trend analysis, seasonality decomposition, and forecasting using Machine Learning Models.
By completing this course, you will be able to apply Machine Learning techniques to real-world datasets, building predictive models, and making data-driven decisions. You will also be equipped with foundational knowledge to pursue more advanced topics in Machine Learning and data science. Upon successfully completing the course, you will be awarded a certificate and a digital badge.
PREREQUISITES
Basic understanding of mathematics, linear algebra, and calculus.
