Machine Learning Programmer to Machine Learning Architect


This course is ideal for candidates looking to take the journey towards a completed. This course covers four modules until completion: Deep Learning Programmer, Deep Learning Engineer, Machine Learning/Deep Learning Architect and Machine Learning/Deep Learning Architect Master.

Deposit : R5,253.00

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 Mode of delivery: Distance learning
ML Track 1: ML Programmer
ML Track 2: DL Programmer
ML Track 3: ML Engineer
ML Track 4: ML Architect
Subject breakdown
Machine Learning 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 ProgrammerML Programming with PythonMachine Learning 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
DL Programming with Python
Final Exam: DL ProgrammerMachine Learning 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
Architecting ML/DL Apps with Python
Final Exam: ML EngineerMachine Learning 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
Architecting Advanced ML/DL Apps with Python
Final Exam: ML Architect



Course starting dates: You can register and start studying this course at any time as registration is open all year round.

Admission requirements:

Grade 10 The ability to read and write in English Learners must be at least 16 years of age





 Course type: Short Course

  Certification: Certificate confirming course completion

  Certification issued by: Optimi College




Assessment information

Online Assessments


Course fee: R12,180.00

Course Deposit: R5,253.00

Monthly instalment option: R577.25

Payment duration: 12 Months

Study duration: 12 Months


 Dedicated Client Engagement Support
 Recorded Lecture Sessions
 Online Mentor (Skillsoft)
 Skillsoft Learner Management System Access
 Skillsoft App Access
 Skillsoft Practice Labs (Online)





Machine Learning Programmer to Machine Learning Architect

Deposit : R5,253.00