Details
Machine learning is a subset of artificial intelligence (AI) that focuses on the development of algorithms and statistical models that enable computers to learn from and make predictions or decisions based on data, without being explicitly programmed for specific tasks.
In essence, machine learning algorithms use patterns and insights discovered in data to improve their performance over time, without the need for human intervention. This course covers the essential components to machine learning using Python, over 10 days.
This 10-day course covers an overview of machine learning concepts and types, including supervised and unsupervised learning, with hands-on implementation using Python libraries like Scikit-learn. It includes techniques for evaluating and validating models, interpreting metrics, and improving performance, followed by an exploration of unsupervised learning algorithms and model selection strategies.
If there is time, advanced topics such as ensemble learning and optional deep learning concepts will be introduced. However, we do aim for practical application and will cover deployment techniques for deploying machine learning models in production environments.
Delivery Methods
| Delivery Method | Duration | ||
|---|---|---|---|
| 10 Days | Get a Quote | ||
| 10 Days | Get a Quote |
Discounts Available
Save up to 10% by booking and paying 10 business days before the course.
Brochure:
Audience
- Data scientists
- Data analysts
- Business Intelligence professionals
- Software Engineers
- Business Professionals
- Researchers
- Start-up owners
- Anyone interested in machine learning
Pre-Requisites
- Python programming proficiency
- Core Mathematics to Grade 12
- Data analysis skills
- Critical thinking and problem-solving skills
Course Outline / Curriculum
Introduction to Machine Learning:
- Overview of machine learning concepts, types of machine learning (supervised, unsupervised, semi-supervised, reinforcement learning), and applications.
Supervised Learning:
- Introduction to supervised learning algorithms, including linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), and k-nearest neighbours (k-NN).
- Hands-on implementation of supervised learning algorithms using Python libraries such as Scikit-learn.
Model Evaluation and Validation:
- Techniques for evaluating and validating machine learning models, including cross-validation, train-test split, and performance metrics (accuracy, precision, recall, F1-score, ROC AUC).
- Interpretation of evaluation metrics and techniques for improving model performance.
Unsupervised Learning:
- Introduction to unsupervised learning algorithms, including clustering (k-means, hierarchical clustering), dimensionality reduction (PCA, t-SNE), and association rule learning.
- Hands-on implementation of unsupervised learning algorithms using Python libraries.
Model Selection and Tuning:
- Strategies for model selection, hyperparameter tuning, and optimization techniques such as grid search and random search.
- Techniques for avoiding overfitting and underfitting, including regularization and model complexity control.
Ensemble Learning:
- Explanation of ensemble learning methods, including bagging, boosting, and stacking.
- Implementation of ensemble learning algorithms such as Random Forest and Gradient Boosting.
Deep Learning (Optional):
- Introduction to deep learning concepts, including artificial neural networks (ANN), convolutional neural networks (CNN), recurrent neural networks (RNN), and deep learning frameworks (TensorFlow, Keras, PyTorch).
- Hands-on implementation of deep learning models for tasks such as image classification, natural language processing (NLP), and sequence prediction.
Deployment and productionization:
- Techniques for deploying machine learning models in production environments, including containerization (Docker), model serving (Flask, FastAPI), and monitoring.
Schedule Dates and Booking
There are currently no scheduled dates.