Site Map - skillsoft.digitalbadges.skillsoft.com
- User Authentication
- Harsh Apoorva's Credentials
- Harsh Apoorva's Wallet
- Exploring Business Process Automation
- Data Access & Governance Policies: Data Classification, Encryption, & Monitoring
- Using Robots and RPA in the Workplace
- CCSP 2019: Identity & Access Management
- CCSP 2022: Cloud Security Concepts & Design Principles
- Leading Security Teams for GenAI Solutions: Use of Generative AI
- Responsible Use of AI
- Hybrid Environment Pipelines: Hybrid Cloud Transformation
- Exploring Project Management, Then and Now (2021 Update)
- Validate and Control Scope (PMBOK® Guide Sixth Edition)
- Effective Team Communication
- Software Design and Development: Design Patterns & SOLID Principles
- CAPM®: Performance Measurement Metrics
- Advanced Agile: Leadership Techniques
- ML Programmer to ML Architect
- ML Engineer
- Enterprise Architecture: Architectural Principles & Patterns
- Architecting Balance: Designing Hybrid Cloud Solutions
- Enterprise Services: Machine Learning Implementation on Microsoft Azure
- Refactoring ML/DL Algorithms: Techniques & Principles
- Enterprise Services: Machine Learning Implementation on Google Cloud Platform
- Enterprise Architecture: Design Architecture for Machine Learning Applications
- Architecting Balance: Hybrid Cloud Implementation with AWS & Azure
- ML Architect
- ML/DL in the Enterprise: Machine Learning Modeling, Development, & Deployment
- Predictive Modeling: Implementing Predictive Models Using Visualizations
- Applied Predictive Modeling
- Deep Learning with Keras
- ML/DL Best Practices: Building Pipelines with Applied Rules
- Automation Design & Robotics
- Applying Predictive Analytics
- Advanced Reinforcement Learning: Principles
- Planning AI Implementation
- Predictive Modeling: Predictive Analytics & Exploratory Data Analysis
- Implementing Deep Learning: Practical Deep Learning Using Frameworks & Tools
- Applied Deep Learning: Unsupervised Data
- Implementing Deep Learning: Optimized Deep Learning Applications
- Refactoring ML/DL Algorithms: Refactor Machine Learning Algorithms
- Advanced Reinforcement Learning: Implementation
- Applied Deep Learning: Generative Adversarial Networks and Q-Learning
- ML/DL Best Practices: Machine Learning Workflow Best Practices
- Enterprise Services: Enterprise Machine Learning with AWS
- Research Topics in ML & DL
- Final Exam: ML Architect
- Final Exam: ML Engineer
- ML Programmer
- DL Programmer
- Improving Neural Networks: Data Scaling & Regularization
- Improving Neural Networks: Loss Function & Optimization
- Getting Started with Neural Networks: Biological & Artificial Neural Networks
- Getting Started with Neural Networks: Perceptrons & Neural Network Algorithms
- Building Neural Networks: Artificial Neural Networks Using Frameworks
- Training Neural Networks: Implementing the Learning Process
- Improving Neural Networks: Neural Network Performance Management
- Convolutional Neural Networks: Fundamentals
- 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: Implementing Recurrent Neural Networks
- ML Algorithms: Machine Learning Implementation Using Calculus & Probability
- Building Neural Networks: Development Principles
- ConvNets: Introduction to Convolutional Neural Networks
- Convo Nets for Visual Recognition: Filters and Feature Mapping in CNN
- ML Algorithms: Multivariate Calculation & Algorithms
- Training Neural Networks: Advanced Learning Algorithms
- Final Exam: ML Programmer
- Build & Train RNNs: Neural Network Components
- Convolutional Neural Networks: Implementing & Training
- ConvNets: Working with Convolutional Neural Networks
- Final Exam: DL Programmer
- AWS Certified Machine Learning: Data Engineering, Machine Learning, & AWS
- Math for Data Science & Machine Learning
- ML/DL in the Enterprise: Pipelines & Infrastructure
- Linear Algebra & Probability: Advanced Linear Algebra
- Linear Algebra and Probability: Fundamentals of Linear Algebra
- Reinforcement Learning: Essentials
- NLP for ML with Python: NLP Using Python & NLTK
- Linear Regression Models: Introduction to Logistic Regression
- Linear Models & Gradient Descent: Managing Linear Models
- Linear Models & Gradient Descent: Gradient Descent and Regularization
- Model Management: Building Machine Learning Models & Pipelines
- NLP for ML with Python: Advanced NLP Using spaCy & Scikit-learn
- Building ML Training Sets: Introduction
- Simplifying Regression and Classification with Estimators
- Building ML Training Sets: Preprocessing Datasets for Classification
- Building ML Training Sets: Preprocessing Datasets for Linear Regression
- Computational Theory: Language Principle & Finite Automata Theory
- Computational Theory: Using Turing, Transducers, & Complexity Classes
- Linear Regression Models: Multiple & Parsimonious
- Model Management: Building & Deploying Machine Learning Models in Production
- Bayesian Methods: Bayesian Concepts & Core Components
- Implementing Bayesian Model and Computation with PyMC
- Reinforcement Learning: Tools & Frameworks
- Bayesian Methods: Advanced Bayesian Computation Model
- Big Data Fundamentals
- Machine & Deep Learning Algorithms: Introduction
- Linear Regression Models: Introduction
- Linear Regression Models: Building Models with Scikit Learn & Keras
- SOLID & GRASP
- Harsh Apoorva's Transcript
- Harsh Apoorva's Wallet
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