Machine Learning Specialist
R3 720.00
Machine learning is one of the most sought after skills in the data science world. This Aspire Journey will teach you how machine learning algorithms work and give you hands-on experience in building, tuning and evaluating them. Along the way, you will create real-world projects to practice and demonstrate your machine learning skills.
 Digital learning |
 Start anytime |
 Months course access |
 Skillsoft LMS access |
 Online Skillsoft mentor |
 Certified by College SA |
- Course fee
- About the course
- Admission requirements
- Accreditation status
- Assessment details
- What to expect
- Enquire now
Course fee
Cost:R3 720.00
Deposit: R930.00
Monthly instalments: R930.00 x 3
Duration: You will have Skillsoft access to this course for 12 months. The average time required to work through the syllabus is:
- 11 courses (5h 34m) / 15 labs (15h) / 19 others (17h 45m)
- Optional additional resources are available to enhance your learning in your own time.
About the course
Course code: C01381
Course overview:
- Track 1: Supervised Learning I: Linear and Logistic Regression
- Track 2: Supervised Learning II: Naive Bayes, SVM, KNN and Decision Trees
- Track 3: Feature Engineering
- Track 4: Unsupervised Learning
- Track 5: Improving Machine Learning Models
Modules and topics covered:
Track 1: Supervised Learning I: Linear and Logistic Regression
- Linear Regression
- Multiple Linear Regression with Python
- Logistic Regression
- Evaluation Metrics for Classification Tasks
- Logistic Regression II
Track 2: Supervised Learning II: Naive Bayes, SVM, KNN and Decision Trees
- Bayes Theorem
- Naive Bayes Classifier
- Support Vector Machines
- K-Nearest Neighbors
- K-Nearest Neighbors Regression
- Decision Trees
Track 3: Feature Engineering
- What is Feature Engineering?
- Introduction To Feature Selection Methods
- Filter Methods
- Feature Importance
Track 4: Unsupervised Learning
- Unsupervised Learning
- Clustering Techniques
- ML & Dimensionality Reduction: Performing Principal Component Analysis
Track 5: Improving Machine Learning Models
- Introduction To Ensembling Methods
- Stacking Machine Learning Models
- Introduction To Recommender Systems
- Recommender Systems: Under the Hood of Recommendation Systems
Admission requirements
Academic grade: We recommend the following prerequisite skills:
- Data Science Foundations
- Fundamental Math for Data Science
Language: Proficiency in English (course material and support only available in English).
Expertise level: Beginner
Equipment: Access to a PC or laptop with a reliable internet connection.
Effort: Self-paced learning online.
Accreditation status
Course type: Short course
Industry partner: Skillsoft
Certification: Certificate confirming course completion.
Certification issued by: College SA
Assessment details
Each track concludes with a final internal exam that will test your knowledge and application of the topics presented throughout that specific track.
There are no external certification exams for this course.
What to expect
Dedicated support team
We understand that students may require guidance and support to navigate the learning journey, and our Client Services team is always ready to assist them in every possible way. Our team is readily available during office hours and can be contacted via email, phone, WhatsApp and social media.
Skillsoft Learner Management System (LMS) access
Skillsoft is an online learning management system that offers all students enrolled for any of our IT Academy courses compelling content, interactive videos, quizzes, mentoring and practical simulations/virtual labs. The platform allows students to learn at their own pace.
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