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Study duration (Months)
Skillsoft LMS access
Online Skillsoft mentor
Certified by Optimi CollegeCourse code: C01103
Course overview:
- Track 1: The focus is on linear regression, computational theory, and training sets.
- Track 2: The focus is on neural networks, CNNs, RNNs, and ML algorithms.
- Track 3: The focus is on predictive modelling and analytics, ml modelling, and ml architecting.
- Track 4: The focus is on applied predictive modelling, CNNs and RNNs, and ML algorithms.
What are Aspire Journeys?
Aspire Journeys are guided learning paths designed and published by Skillsoft. These courses provide:
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- 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.
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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:
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 Programmer
Machine 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 Programmer
Machine 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 Engineer
Machine 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
