Step into the future of technology with our Data Mining, Machine Learning and Deep Learning course. In an age where data is the new oil, mastering these skills opens up a world of opportunities. As industries increasingly rely on data-driven insights, the demand for professionals skilled in supervised and unsupervised learning, as well as deep learning, is skyrocketing. This course equips you with the ability to extract meaningful patterns from vast datasets, drive innovation and create smarter solutions. Delivered online, it offers you the flexibility to learn at your own pace, ensuring you stay ahead in a competitive job market. Whether you’re looking to enhance your current role or switch to a new career, this course is your gateway to becoming a highly sought-after expert in a constantly evolving field. Join us to turn data into insights and shape the future.
Data mining, machine learning and deep learning (English)
AI Development
40 hours
Introduction
Objectives
- Understand the fundamental concepts of supervised learning techniques.
- Learn how to implement regression and classification models effectively.
- Explore unsupervised learning methods for data clustering.
- Understand the basics of neural networks in the context of deep learning.
- Develop skills to evaluate model performance accurately.
- Gain an understanding of advanced deep learning architectures.
- Apply machine learning solutions to real-world data problems.
Table of Contents
UNIT 1. SUPERVISED LEARNING (I)
1. Introduction
2. Simple, multiple and logistic linear regression (I)
3. Simple, multiple and logistic linear regression (II)
4. Support vector machines (SVM)
5. Decision trees
UNIT 2. SUPERVISED LEARNING (II)
1. KNN (k-nearest neighbours)
2. Naive Bayes
3. Evaluation of supervised models
4. Example exercise
5. Proposed exercise
UNIT 3. UNSUPERVISED LEARNING
1. Introduction to clustering: purconsider and metrics
2. K-means clustering
3. Hierarchical clustering, other techniques and examples
4. Principal component analysis (PCA)
5. PCA example exercise
UNIT 4. DEEP LEARNING
1. Artificial Neural Networks (ANN) (I)
2. Artificial Neural Networks (ANN) (II)
3. Artificial Neural Networks (ANN) (III)
4. Example exercise
5. Proposed exercise
More related courses
Your questions answered
Why choose Educa.Pro?
Educa.Pro enables organisations to centralise training management, personalise learning and gain clear insights into the impact of training programmes. Thanks to its AI-powered technology, organisations can optimise their learning strategies, enhance talent development and make data-driven decisions.
Can the courses be taken at your own pace?
Yes. Most of the courses on Educa.Pro are self-paced and on-demand, allowing users to progress at their own pace without missing out on progress tracking or assessments, which makes it easier to fit them around work or study commitments.
What levels of security and compliance does the platform offer?
Educa.Pro complies with security standards designed to protect user and organisational data. Furthermore, it supports the traceability of certifications, digital signatures and the management of assessments, thereby ensuring regulatory and legal compliance in both educational and corporate environments.
What support and guidance does Educa.Pro offer?
The Educa.Pro team offers comprehensive support throughout implementation, roll-out and adoption, ensuring that organisations get the most out of the platform and achieve measurable results from day one.