Machine Learning and Deep Learning Certification

Training Express Ltd

Digital certificate

Digital certificate - Included

Hard copy certificate

Hard copy certificate - Included

Course Curriculum:

• Module 01: Introduction & study plan
• Module 02: Overview of Mechine Learning
• Module 03: Types of Mechine Learning
• Module 04: continuation of types of machine learning
• Module 05: Steps in a typical machine learning workflow
• Module 06: Application of Mechine Learning
• Module 07: Data types & structure
• Module 08: Control Flow & Structure
• Module 09: Libraries for Machine Learning
• Module 10: Loading & preparing data final
• Module 11: Loading and preparing data
• Module 12: Tools and Platforms
• Module 13: Model Deployment
• Module 14: Numpy
• Module 15: Indexing and slicing
• Module 16: Pundas
• Module 17: Indexing and selection
• Module 18: Handling missing data
• Module 19: Data Cleaning and Preprocessing
• Module 20: Handling Duplicates
• Module 21: Data Processing
• Module 22: Data Splitting
• Module 23: Data Transformation
• Module 24: Iterative Process
• Module 25: Exploratory Data Analysis
• Module 26: Visualization Libraries
• Module 27: Advanced Visualization Techniques
• Module 28: Interactive Visualization
• Module 29: Regression
• Module 30: Types of Regression
• Module 31: Lasso Regration
• Module 32: Steps in Regration Analysis
• Module 33: Continuation
• Module 34: Best Practices
• Module 35: Regression Analysis is a Fundamental
• Module 36: Classification
• Module 37: Types of classification
• Module 38: Steps in Classification Analysis
• Module 39: Steps in Classification analysis Continuou.
• Module 40: Best Practices
• Module 41: Classification Analysis
• Module 42: Model Evolution and Hyperparameter tuning
• Module 43: Evaluation Metrics
• Module 44: Continuations of Hyperparameter tuning
• Module 45: Best Practices
• Module 46: Clustering
• Module 47: Types of Clustering Algorithm
• Module 48: Continuations Types of Clustering
• Module 49: Steps in Clustering Analysis
• Module 50: Continuations Steps in Clustering Analysis
• Module 51: Evalution of Clustering
• Module 52: Application of Clustering
• Module 53: Clustering Analysis
• Module 54: Dimensionality Reduction
• Module 55: Continuation of Dimensionally Reduction
• Module 56: Principal Component Analysis (PCA)
• Module 57: Distributed Stochastic Neighbor Embedding
• Module 58: Application of Dimensionality Reduction
• Module 59: Continuation of Application of Dimensionality
• Module 60: Introduction to Deep Learning
• Module 61: Feedforward Propagation
• Module 62: Backpropagation
• Module 63: Recurrent Neural Networks (RNN)
• Module 64: Training Techniques
• Module 65: Model Evaluation
• Module 66: Introduction to Tensorflow and Keras
• Module 67: Continuation of Introduction to Tensorflow and Keras.
• Module 68: Workflow
• Module 69: Keras
• Module 70: Continuation of Keras
• Module 71: Integration
• Module 72: Deep learning Techniques
• Module 73: Continuation of Deep learning techniques
• Module 74: Key Components
• Module 75: Training
• Module 76: Application
• Module 77: Continuation of Application
• Module 78: Recurrent Neural Networks
• Module 79: Continuation of Recurrent Neural Networks.
• Module 80: Training
• Module 81: Varients
• Module 82: Application
• Module 83: RNN
• Module 84: Transfer Learning and Fine Tuning
• Module 85: Continuation Transfer Learning and Fine Tuning
• Module 86: Fine Tuning
• Module 87: Continuation Fine Tuning
• Module 88: Best Practices
• Module 89: Transfer Learning and Fine Tuning are powerful techniques
• Module 90: Advance Deep Learning
• Module 91: Architecture
• Module 92: Training
• Module 93: Training Process
• Module 94: Application
• Module 95: Generative Adversarial Network have
• Module 96: Rainforcement Learning
• Module 97: Reward Signal and Deep Reinforcement
• Module 98: Techniques in Deep Reinforcement Learning
• Module 99: Application of Deep Reinforcement
• Module 100: Deep Reinforcement Learning has demonstrated
• Module 101: Deployment & Model Management
• Module 102: Flask for Web APIs
• Module 103: Example
• Module 104: Dockerization
• Module 105: Example Dockerfile
• Module 106: Flask and Docker provide a powerful combination
• Module 107: Model Management & Monitoring
• Module 108: Continuation of Model Management & Mentoring
• Module 109: Model Monitoring
• Module 110: Continuation of Model Monitoring
• Module 111: Tools and Platforms
• Module 112: By implementing effecting model management
• Module 113: Ethical and Responsible AI
• Module 114: Understanding Bias
• Module 115: Promotion Fairness
• Module 116: Module Ethical Considerations
• Module 117: Tools & Resources
• Module 118: Privacy and Security in ML
• Module 119: Privacy Consideration
• Module 120: Security Consideration

Accreditation

All of our courses are fully accredited, including this Machine Learning and Deep Learning Certification Course, providing you with up-to-date skills and knowledge and helping you to become more competent and effective in Machine Learning and Deep Learning Certification.

Certification

Once you’ve successfully completed your Machine Learning and Deep Learning Certification Course, you will immediately be sent a digital certificate. Also, you can have your printed certificate delivered by post (shipping cost £3.99). Our Machine Learning and Deep Learning Certification Course certification has no expiry dates, although we do recommend that you renew them every 12 months.

• Beginners who want to start a career in artificial intelligence.
• Data enthusiasts aiming to master machine learning and deep learning.
• Software developers seeking to implement AI models in real projects.
• Professionals looking to upskill in machine learning and deep learning.
• Analysts interested in predictive analytics using machine learning techniques.
• Students aiming for a strong foundation in AI and neural networks.
• Researchers exploring applications of deep learning and reinforcement learning.
• Managers and decision-makers planning to adopt AI in business solutions.

Learners do not require any prior qualifications to enrol on this Machine Learning and Deep Learning Certification Course.

• Machine Learning Assistant
• Data Analyst Intern
• AI Research Assistant
• Junior Python Developer
• Deep Learning Support Analyst
• ML Model Monitoring Assistant

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