Site Map - skillsoft.digitalbadges.skillsoft.com
- User Authentication
- Kumar Dahal's Credentials
- Kumar Dahal's Wallet
- ML Programmer to ML Architect
- ML Architect
- Deep Learning with Keras
- Final Exam: ML Architect
- Research Topics in ML & DL
- ML/DL Best Practices: Building Pipelines with Applied Rules
- Advanced Reinforcement Learning: Implementation
- ML/DL Best Practices: Machine Learning Workflow Best Practices
- Advanced Reinforcement Learning: Principles
- Applied Deep Learning: Unsupervised Data
- Applied Deep Learning: Generative Adversarial Networks and Q-Learning
- Applied Predictive Modeling
- Implementing Deep Learning: Practical Deep Learning Using Frameworks & Tools
- Implementing Deep Learning: Optimized Deep Learning Applications
- ML Engineer
- Final Exam: ML Engineer
- Refactoring ML/DL Algorithms: Refactor Machine Learning Algorithms
- Refactoring ML/DL Algorithms: Techniques & Principles
- Architecting Balance: Designing Hybrid Cloud Solutions
- Enterprise Architecture: Design Architecture for Machine Learning Applications
- Architecting Balance: Hybrid Cloud Implementation with AWS & Azure
- Enterprise Architecture: Architectural Principles & Patterns
- Enterprise Services: Machine Learning Implementation on Google Cloud Platform
- Enterprise Services: Machine Learning Implementation on Microsoft Azure
- Enterprise Services: Enterprise Machine Learning with AWS
- ML/DL in the Enterprise: Pipelines & Infrastructure
- ML/DL in the Enterprise: Machine Learning Modeling, Development, & Deployment
- Automation Design & Robotics
- Planning AI Implementation
- Predictive Modeling: Implementing Predictive Models Using Visualizations
- Applying Predictive Analytics
- DL Programmer
- Predictive Modeling: Predictive Analytics & Exploratory Data Analysis
- Final Exam: DL Programmer
- Build & Train RNNs: Implementing Recurrent Neural Networks
- ML Algorithms: Machine Learning Implementation Using Calculus & Probability
- ML Algorithms: Multivariate Calculation & Algorithms
- 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
- Convo Nets for Visual Recognition: Filters and Feature Mapping in CNN
- Build & Train RNNs: Neural Network Components
- Convolutional Neural Networks: Fundamentals
- Convolutional Neural Networks: Implementing & Training
- ConvNets: Working with Convolutional Neural Networks
- ConvNets: Introduction to Convolutional Neural Networks
- Improving Neural Networks: Data Scaling & Regularization
- Improving Neural Networks: Loss Function & Optimization
- Improving Neural Networks: Neural Network Performance Management
- Training Neural Networks: Advanced Learning Algorithms
- Building Neural Networks: Artificial Neural Networks Using Frameworks
- Training Neural Networks: Implementing the Learning Process
- Getting Started with Neural Networks: Perceptrons & Neural Network Algorithms
- Building Neural Networks: Development Principles
- Getting Started with Neural Networks: Biological & Artificial Neural Networks
- ML Programmer
- Final Exam: ML Programmer
- Linear Models & Gradient Descent: Gradient Descent and Regularization
- Linear Models & Gradient Descent: Managing Linear Models
- Building ML Training Sets: Preprocessing Datasets for Classification
- Building ML Training Sets: Preprocessing Datasets for Linear Regression
- Math for Data Science & Machine Learning
- Reinforcement Learning: Essentials
- Building ML Training Sets: Introduction
- Reinforcement Learning: Tools & Frameworks
- Implementing Bayesian Model and Computation with PyMC
- Bayesian Methods: Advanced Bayesian Computation Model
- Model Management: Building & Deploying Machine Learning Models in Production
- Bayesian Methods: Bayesian Concepts & Core Components
- Model Management: Building Machine Learning Models & Pipelines
- Simplifying Regression and Classification with Estimators
- Computational Theory: Language Principle & Finite Automata Theory
- Computational Theory: Using Turing, Transducers, & Complexity Classes
- Linear Regression Models: Introduction to Logistic Regression
- Linear Regression Models: Multiple & Parsimonious
- Linear Regression Models: Building Models with Scikit Learn & Keras
- Linear Regression Models: Introduction
- Linear Algebra & Probability: Advanced Linear Algebra
- Linear Algebra and Probability: Fundamentals of Linear Algebra
- NLP for ML with Python: Advanced NLP Using spaCy & Scikit-learn
- NLP for ML with Python: NLP Using Python & NLTK
- AI Apprentice to AI Architect
- AI Architect
- Explainable AI
- Final Exam: AI Architect
- AI in Industry
- Evaluating Current and Future AI Technologies and Frameworks
- Leveraging Reusable AI Architecture Patterns
- AI Enterprise Planning
- AI Practitioner
- Elements of an Artificial Intelligence Architect
- Final Exam: AI Practitioner
- Using Intelligent Information Systems in AI
- AI Practitioner: BERT Best Practices & Design Considerations
- AI Practitioner: Practical BERT Examples
- Working With the Keras Framework
- Using Apache Spark for AI Development
- Extending Amazon Machine Learning
- Advanced Functionality of Microsoft Cognitive Toolkit (CNTK)
- The AI Practitioner: Tuning AI Solutions
- AI Developer
- The AI Practitioner: Optimizing AI Solutions
- The AI Practitioner: Role & Responsibilities
- Final Exam: AI Developer
- Working with Google BERT: Elements of BERT
- Implementing AI Using Cognitive Modeling
- Applying AI to Robotics
- Introducing Apache Spark for AI Development
- Keras - a Neural Network Framework
- Implementing AI With Amazon ML
- Working With Microsoft Cognitive Toolkit (CNTK)
- AI Framework Overview: Development Frameworks
- AI Framework Overview: AI Developer Role
- AI Apprentice
- Final Exam: AI Apprentice
- Cognitive Models: Approaches to Cognitive Learning
- Cognitive Models: Overview of Cognitive Models
- Computer Vision: AI & Computer Vision
- Computer Vision: Introduction
- Python AI Development: Practice
- Python AI Development: Introduction
- Artificial Intelligence: Human-computer Interaction Overview
- Artificial Intelligence: Human-computer Interaction Methodologies
- Artificial Intelligence: Types of Artificial Intelligence
- Artificial Intelligence: Basic AI Theory
- Kubernetes Administrator: Kubernetes Fundamentals for Administrators
- Multi-cloud Load Balancing: Load Balancing Design Strategies
- Multi-cloud Load Balancing: Principles of Load Balancing
- Google Cloud Architect: CLI Cloud Resource Management
- Google Cloud Architect: Troubleshooting
- Google Cloud Architect: Solution Management & Testing
- Google Cloud Architect: Identity Management
- Google Cloud Architect: Programmatic Access
- Google Cloud Architect: Monitoring & Logging
- Google Cloud Architect: Virtual Machine Configuration
- Google Cloud Architect: Security
- Google Cloud Architect: Virtual Machine Deployment
- Google Cloud Architect: Data Storage
- Google Cloud Architect: Web Applications & Name Resolution
- Google Cloud Architect: Network Components
- Google Cloud Architect: Cloud Design
- Google Cloud Architect: Cloud Basics
- AWS Cloud Practitioner Bootcamp: Session 1 Replay
- Jenkins for DevOps: Automated Testing & Advanced Jobs Using Jenkins
- Jenkins for DevOps: Jenkins Configuration for DevOps
- Cloud Architect
- DevOps Engineer to Cloud Architect
- Cloud Future: Adapting Cloud Innovation
- Final Exam: Cloud Architect
- Applying the Explainability Approach to Guide Cloud Implementation
- CloudOps Solutioning Strategies
- Cloud Transition: Adopting & Moving to Cloud & Multi-cloud Environments
- CloudOps Engineer
- Final Exam: CloudOps Engineer
- Role of a Cloud Architect
- CloudOps: Implementing SD-WAN to Optimize Environments
- Securing CloudOps Deployments: Security Standards for Multi-cloud
- Securing CloudOps Deployments: Implementing Multi-cloud Security
- OpenStack in CloudOps: Automation
- OpenStack in CloudOps: Managing Multi-cloud with OpenStack
- Managing Multi-cloud Containers Using Kubernetes
- Docker & Multi-cloud: Multi-host, Multi-cloud Management with Docker Enterprise
- CloudOps Interoperability: Inter-cloud Integration & Implementation
- Docker & Multi-cloud: Managing Multi-cloud with Docker
- CloudOps Interoperability: Modeling Cloud Computing for Integration
- Implementing IaaS & Orchestration for Multi-cloud Environments
- DevOps to CloudOps for Multi-cloud
- Cloud Engineer
- DevOps Automation Across Platforms: Working with Multi-cloud Tools
- Final Exam: Cloud Engineer
- DevOps Automation Across Platforms: CloudOps for Multi-cloud Deployments
- GCP DevOps: CloudOps with Google Cloud Platform
- Using AWS to Set Up DevOps and CloudOps Automation Frameworks
- Applying Automation Using AWS Tools
- Azure DevOps: Repository & Pipeline Management
- Azure DevOps: Managing Agile Lifecycle
- Adopting IT Automation
- Applying Design Patterns in DevOps & CloudOps Automation
- DevOps Engineer
- CloudOps with Azure DevOps Tools
- CloudOps with Google Cloud Platform Tools
- CloudOps: Infrastructure as Code
- Final Exam: Cloud DevOps Engineer
- Adopting the DevOps CI/CD Paradigm
- DevOps & AWS: CloudOps Implementation
- Kanban for Operations: Managing Projects Using Kanban
- Agile and DevOps: Adopting Agile Methodology
- Adopting the DevOps Mindset
- DevOps Practices for the Enterprise
- Cloud Security Fundamentals: Basics of Cloud Operations
- Kumar Dahal's Transcript
- Kumar Dahal's Wallet
- About Accredible