IT Technician | Networking | Servers | Cloud | Coding | Database | Web Design | IT Management | Project Management | Microsoft Office | Data Scientist | Blockchain | Python | Machine Learning | AI
Meet your Instructor:

Charles Robinson
Instructor: Technology and Development
Meet your Mentor:

Carl Mullin
Mentor: Business/Technology and Developer
Machine Learning Programmer to Machine Learning Architect
There are 4 Tracks for the Machine Learning Programmer to Machine Learning Architect course (Machine Learning Programmer, Deep Learning Programmer, Machine Learning Engineer and Machine Learning Architect). Each stage of the journey delivers 40-50 hours of courses + multimodal content and an additional 10-12 Practice Labs, Certification Pre/Assessments.
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
ML Programming with Python
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
DL Programming with Python
Final Exam: DL Programmer
Getting to the Root of a Problem
Confronting Your Assumptions
Investigating Arguments
Managing a Project to Minimize Risk and Maximize Quality
Embracing an Agile Culture for Business Growth
Leading a Cross-functional Team
Cultivating Relationships with Your Peers
The Building Blocks of Building Trust
Trust Building through Effective Communication
Defining Alternative Solutions to a Problem
Six Sigma Performance Metrics
Thinking Strategically as a Manager
Cultivating Cross-functional Team Collaboration
Unleashing Personal and Team Creativity
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
Architecting ML/DL Apps with Python
Final Exam: ML Engineer
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
Architecting Advanced ML/DL Apps with Python
Final Exam: ML Architect
Confluence: Signing in & Navigating within Spaces
Confluence: Setting Up & Managing Spaces
Confluence: Working with Spaces
Confluence: Working with Team Members
Confluence: Configuring Spaces
Slack Web: Signing in and Setting Up
Slack Web: Using Channels
Slack Web: Private Messaging and Communication Tools
Slack Web: Creating, Finding, and Sharing Information
Slack Web: Configuring Slack
Slack iOS: Using the iOS App
R10 995.00 or R11 495.00 on terms with R4 955.00 deposit and 12 monthly instalments of R545.00.
Certification:
International exams are optional and can be written at any Pearson Vue Testing Centre in South Africa. Discounted international exam vouchers can be purchased through IT Academy.
Courses are accessible on a Computer, Laptop, Tablet or Smartphone. Courses are designed in video with audio, printable, includes One-on-One Mentoring, Free Virtual-LABS with feedback and exam preps. Learners are assessed after each lecture, all assessments can be retaken at no additional cost. Receive a .pdf and original Certificate within 1 week which can be added to your CV. International exams are optional and can be written at anytime, Click here to view Centres in your area. e-Mail info@it-academy.co.za to arrange your exam booking.
One-on-One Mentoring
Free Virtual-LABS
Mobile Ready (Tablet/Smartphone access)
100% Pass Rate / Course Mastery Certificate
Discounted International Exam Vouchers
Free Exam Preps
Printable
12 Month Subscription
Student Card
Payment Terms
Your Quick Course Guide
Certification: Machine Learning Architect
Study Time: 12 Months to complete 200 hours eg study 1 hour per day to complete your course in 7 months |study Full Time or Part Time
Vendor: IT Academy
Provider: IT Academy
Student support: One-on-One Mentoring
Pre-requisite: Data Analytics to Data Scientist
Assessment: Multimodal/Assessments
Resources: Laptop, Tablet or Smartphone & Internet Connection
Expertise Level: Intermediate
Register
Anytime – 24/7 Access
Salary Indicator
SALARY PROJECTION
Average
salary after
completing
High
R520k
You can earn an average
of R520,000.00 a year
Salary Indicator
SALARY PROJECTION
Average
salary after
completing
High
R520k
You can earn an average
of R520,000.00 a year
Salary Indicator
SALARY PROJECTION
Average
salary after
completing
High
R520k
You can earn an average
of R520,000.00 a year
What’s Included

- One-on-One Mentoring
- Free Virtual-LABS
- Mobile Ready (Tablet/Smartphone access)
- 100% Pass Rate / Course Mastery Certificate
- Discounted International Exam Vouchers
- Free Exam Preps
- Printable
- 12 Month Subscription
- Student Card
- Payment Terms
Testimonials



How Our Courses Work
Access your On-Demand, Cloud-Based Learning Anytime, Anywhere.

Courses are accesssible on a Laptop, Tablet or Smartphone.

Assessments after each lecture which can be retaken.

One-on-One Mentoring, Free Virtual-LABS with feedback.

IT Academy Course Mastery Certificate:
Receive a .pdf and original Certificate within 1 week which can be added to your CV.
Enquire about NQF Certification.

International Certificate:
Optional, can be written at anytime, click here to view Centres in your area. e-Mail info@it-academy.co.za to arrange your exam booking.
IT Academy Accreditation/Partners: CompTIA (512759) / Microsoft (1204038185) / MICT Seta (ACC2011/01/684)
Top Skills in demand by Companies in South Africa –
Software Development, Security, Data Analyst & Networking
What Sets Us Apart
- Official Partnerships
Partners with IT certification vendors Cisco, CompTIA, Microsoft & MICT Seta
- IT Experts
Courses are designed and led by Certified Professionals, Mobile Access
- Award Winning
e-Learning content delivered via Video, Quizzes, Virtual labs & Mentoring
- Virtual Practice Labs
Learn, Practice writing Code with instant feedback in a Live Environment
- Live Mentoring
Free One-on-One Live Mentoring with Certified IT Experts
- Practice Tests
Take Practice Tests designed to mimic the actual exam
Our Training Is Trusted By











