ML Programmer to ML Architect

Course fee

Cost: R5 400.00

Deposit: R1 350.00

Monthly instalment: R810.00 x 5

Duration: You will have Skillsoft access to this course for 12 months. The average time required to work through the syllabus is:

• 75 courses (71h 30m)
• Optional additional resources are available to enhance your learning in your own time.

About the course

Course code: C01103

Course overview:

• Track 1: ML Programmer
• Track 2: DL Programmer
• Track 3: ML Engineer
• Track 4: ML Architect

What are Aspire Journeys?

Aspire Journeys are guided learning paths designed and published by Skillsoft. These courses provide:

• A clear starting point across the roles and responsibilities of tomorrow.
• Exercises for on-the-job applications to put what you’ve learned into practice.
• Verifiable, shareable, and portable digital badges so you can celebrate accomplishments along the way.
• A diverse array of learning tools from the books to audiobooks to video courses, and more.

The learning path for each journey comprises tracks of content in a recommended order. Completing all content within a track completes the track. Completing all tracks within the journey completes the journey.

Modules and topics covered:

Track 1: ML Programmer

• NLP for ML with Python: NLP Using Python & NLTK
• NLP for ML with Python: Advanced NLP Using spaCy & Scikit-learn
• Linear Algebra and Probability: Fundamentals of Linear Algebra
• Linear Algebra & Probability: Advanced Linear Algebra
• Linear Regression Models: Introduction to Linear Regression
• Linear Regression Models: Building Simple Regression Models with Scikit Learn and Keras
• Linear Regression Models: Multiple and Parsimonious Linear Regression
• Linear Regression Models: An Introduction to Logistic Regression
• Linear Regression Models: Simplifying Regression and Classification with Estimators
• Computational Theory: Language Principle & Finite Automata Theory
• Computational Theory: Using Turing, Transducers, & Complexity Classes
• Model Management: Building Machine Learning Models & Pipelines
• Model Management: Building & Deploying Machine Learning Models in Production
• Bayesian Methods: Bayesian Concepts & Core Components
• Bayesian Methods: Implementing Bayesian Model and Computation with PyMC
• Bayesian Methods: Advanced Bayesian Computation Model
• Reinforcement Learning: Essentials
• Reinforcement Learning: Tools & Frameworks
• Math for Data Science & Machine Learning
• Building ML Training Sets: Introduction
• Building ML Training Sets: Preprocessing Datasets for Linear Regression
• Building ML Training Sets: Preprocessing Datasets for Classification
• Linear Models & Gradient Descent: Managing Linear Models
• Linear Models & Gradient Descent: Gradient Descent and Regularization
• Final Exam: ML Programmer

Track 2: DL Programmer

• Getting Started with Neural Networks: Biological & Artificial Neural Networks
• Getting Started with Neural Networks: Perceptrons & Neural Network Algorithms
• Building Neural Networks: Development Principles
• Building Neural Networks: Artificial Neural Networks Using Frameworks
• Training Neural Networks: Implementing the Learning Process
• Training Neural Networks: Advanced Learning Algorithms
• Improving Neural Networks: Neural Network Performance Management
• Improving Neural Networks: Loss Function & Optimization
• Improving Neural Networks: Data Scaling & Regularization
• ConvNets: Introduction to Convolutional Neural Networks
• ConvNets: Working with Convolutional Neural Networks
• Convolutional Neural Networks: Fundamentals
• Convolutional Neural Networks: Implementing & Training
• Convo Nets for Visual Recognition: Filters & Feature Mapping in CNN
• Convo Nets for Visual Recognition: Computer Vision & CNN Architectures
• Fundamentals of Sequence Model: Artificial Neural Network & Sequence Modeling
• Fundamentals of Sequence Model: Language Model & Modeling Algorithms
• Build & Train RNNs: Neural Network Components
• Build & Train RNNs: Implementing Recurrent Neural Networks
• ML Algorithms: Multivariate Calculation & Algorithms
• ML Algorithms: Machine Learning Implementation Using Calculus & Probability
• Final Exam: DL Programmer

Track 3: ML Engineer

• Predictive Modeling: Predictive Analytics & Exploratory Data Analysis
• Predictive Modeling: Implementing Predictive Models Using Visualizations
• Predictive Modelling Best Practices: Applying Predictive Analytics
• Planning AI Implementation
• Automation Design & Robotics
• ML/DL in the Enterprise: Machine Learning Modeling, Development, & Deployment
• ML/DL in the Enterprise: Machine Learning Infrastructure & Metamodel
• Enterprise Services: Enterprise Machine Learning with AWS
• Enterprise Services: Machine Learning Implementation on Microsoft Azure
• Enterprise Services: Machine Learning Implementation on Google Cloud Platform
• Architecting Balance: Designing Hybrid Cloud Solutions
• Enterprise Architecture: Architectural Principles & Patterns
• Enterprise Architecture: Design Architecture for Machine Learning Applications
• Architecting Balance: Hybrid Cloud Implementation with AWS & Azure
• Refactoring ML/DL Algorithms: Techniques & Principles
• Refactoring ML/DL Algorithms: Refactor Machine Learning Algorithms
• Final Exam: ML Engineer

Track 4: ML Architect

• Applied Predictive Modeling
• Implementing Deep Learning: Practical Deep Learning Using Frameworks & Tools
• Implementing Deep Learning: Optimized Deep Learning Applications
• Applied Deep Learning: Unsupervised Data
• Applied Deep Learning: Generative Adversarial Networks and Q-Learning
• Advanced Reinforcement Learning: Principles
• Advanced Reinforcement Learning: Implementation
• ML/DL Best Practices: Machine Learning Workflow Best Practices
• ML/DL Best Practices: Building Pipelines with Applied Rules
• Research Topics in ML and DL
• Deep Learning with Keras
• Final Exam: ML Architect

Admission requirements

Academic grade: No minimum school pass requirements or formal prerequisites, but it is recommended that candidates have some experience in the lab or field.

Language: Proficiency in English (course material and support only available in English).

Expertise level: Intermediate

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, and mentoring. The platform allows students to learn at their own pace.