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The Certification in Machine Learning and Deep Learning is designed for learners who are ready to explore the full potential of intelligent systems and modern algorithmic design. Whether you’re aiming to build a foundation in data science or keen to sharpen your algorithmic intuition, this course dives into the heart of automation, neural networks, and predictive analytics in an engaging and structured way.
From regression to reinforcement learning, data wrangling to neural network tuning, this course offers a deep dive into the theory and application of machine learning and deep learning techniques. With over 120 modules, it covers a vast landscape — including model deployment, ethical considerations, visualisation tools, and cutting-edge frameworks such as TensorFlow, Keras, and Docker — without the need for physical attendance. It’s all designed for curious minds and analytical thinkers with an eye on the future.
- Accredited by CPD
- Instant e-certificate and hard copy dispatch by next working day
- Fully online, interactive course with audio voiceover
- Self-paced learning and laptop, tablet, smartphone-friendly
- 24/7 Learning Assistance
- Discounts on bulk purchases
Sneak Peek
Learning Outcomes
- Understand core principles of machine learning and deep learning models
- Explore classification, clustering, and dimensionality reduction techniques
- Evaluate models using advanced metrics and tuning strategies
- Clean, process, and visualise data using Python libraries
- Deploy AI models with Docker, Flask, and monitoring tools
- Analyse ethical issues around AI, bias, and security risks
Who is this Course For
- Beginners interested in machine learning and deep learning techniques
- Data analysts aiming to enhance predictive modelling capabilities
- Python developers wanting to apply AI in software projects
- Professionals exploring automation and decision-making algorithms
- Engineers keen on model deployment and system integration
- Students seeking structured, flexible machine learning learning paths
- Researchers applying neural networks in data-rich environments
- Tech enthusiasts fascinated by algorithmic intelligence and AI ethics
Certification
Once you’ve successfully completed your course, you will immediately be sent a digital certificate. Also, you can have your printed certificate delivered by post (shipping cost £3.99). All of our courses are fully accredited, providing you with up-to-date skills and knowledge and helping you to become more competent and effective in your chosen field. Our certifications have no expiry dates, although we do recommend that you renew them every 12 months.
Accreditation
All of our courses, including this Certification in Machine Learning and Deep Learning, are fully accredited, providing you with up-to-date skills and knowledge and helping you to become more competent and effective in your chosen field.
Career Path
- Machine Learning Engineer – £60,000 average salary per year
- Data Scientist – £55,000 average salary per year
- AI Research Scientist – £70,000 average salary per year
- Deep Learning Specialist – £65,000 average salary per year
- Data Engineer (AI Focus) – £58,000 average salary per year
- ML Ops Engineer – £62,000 average salary per year
Course Curriculum
The detailed curriculum outline of our Certification in Machine Learning and Deep Learning is as follows:
- Module 1: Introduction & study plan
- Module 2: Overview of Mechine Learning
- Module 3: Types of Mechine Learning
- Module 4: continuation of types of machine learning
- Module 5: Steps in a typical machine learning workflow
- Module 6: Application of Mechine Learning
- Module 7: Data types & structure
- Module 8: Control Flow & Structure
- Module 9: 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
- Module 121: Continuation of security Consideration
- Module 122: Education & Awareness
- Module 123: Capstone Project
- Module 124: Project Task
- Module 125: Evaluation and performance
- Module 126: Privacy-Preservin g Deployment
- Module 127: Learning Outcome
- Module 128: Additional Resources and Practices
- Module 129: Assignment
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- Original price was: £125.00.£20.00Current price is: £20.00. ex Vat
- 1 year
- Intermediate
- Course Certificate
- 12 hours, 6 minutes Gift this course
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