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Certification in Machine Learning and Deep Learning

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4 STUDENTS

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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

Data Processing Exploratory Data Analysis Regression Continuation

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 

Course Curriculum

Certification in Machine Learning and Deep Learning
Module 1: Introduction & study plan 00:08:00
Module 2: Overview of Mechine Learning 00:02:00
Module 3: Types of Mechine Learning 00:04:00
Module 4: continuation of types of machine learning 00:05:00
Module 5: Steps in a typical machine learning workflow 00:04:00
Module 6: Application of Mechine Learning 00:04:00
Module 7: Data types & structure 00:02:00
Module 8: Control Flow & Structure 00:02:00
Module 9: Libraries for Machine Learning 00:04:00
Module 10: Loading & preparing data final 00:04:00
Module 11: Loading and preparing data 00:02:00
Module 12: Tools and Platforms 00:05:00
Module 13: Model Deployment 00:05:00
Module 14: Numpy 00:04:00
Module 15: Indexing and slicing 00:07:00
Module 16: Pundas 00:05:00
Module 17: Indexing and selection 00:04:00
Module 18: Handling missing data 00:05:00
Module 19: Data Cleaning and Preprocessing 00:05:00
Module 20: Handling Duplicates 00:04:00
Module 21: Data Processing 00:04:00
Module 22: Data Splitting 00:05:00
Module 23: Data Transformation 00:06:00
Module 24: Iterative Process 00:04:00
Module 25: Exploratory Data Analysis 00:04:00
Module 26: Visualization Libraries 00:05:00
Module 27: Advanced Visualization Techniques 00:15:00
Module 28: Interactive Visualization 00:07:00
Module 29: Regression 00:03:00
Module 30: Types of Regression 00:07:00
Module 31: Lasso Regration 00:09:00
Module 32: Steps in Regration Analysis 00:14:00
Module 33: Continuation 00:03:00
Module 34: Best Practices 00:08:00
Module 35: Regression Analysis is a Fundamental 00:03:00
Module 36: Classification 00:04:00
Module 37: Types of classification 00:06:00
Module 38: Steps in Classification Analysis 00:05:00
Module 39: Steps in Classification analysis Continuou. 00:10:00
Module 40: Best Practices 00:07:00
Module 41: Classification Analysis 00:03:00
Module 42: Model Evolution and Hyperparameter tuning 00:05:00
Module 43: Evaluation Metrics 00:04:00
Module 44: Continuations of Hyperparameter tuning 00:08:00
Module 45: Best Practices 00:06:00
Module 46: Clustering 00:04:00
Module 47: Types of Clustering Algorithm 00:06:00
Module 48: Continuations Types of Clustering 00:04:00
Module 49: Steps in Clustering Analysis 00:06:00
Module 50: Continuations Steps in Clustering Analysis 00:05:00
Module 51: Evalution of Clustering 00:08:00
Module 52: Application of Clustering 00:07:00
Module 53: Clustering Analysis 00:03:00
Module 54: Dimensionality Reduction 00:10:00
Module 55: Continuation of Dimensionally Reduction 00:03:00
Module 56: Principal Component Analysis (PCA) 00:07:00
Module 57: Distributed Stochastic Neighbor Embedding 00:03:00
Module 58: Application of Dimensionality Reduction 00:04:00
Module 59: Continuation of Application of Dimensionality 00:06:00
Module 60: Introduction to Deep Learning 00:08:00
Module 61: Feedforward Propagation 00:03:00
Module 62: Backpropagation 00:07:00
Module 63: Recurrent Neural Networks (RNN) 00:07:00
Module 64: Training Techniques 00:05:00
Module 65: Model Evaluation 00:08:00
Module 66: Introduction to Tensorflow and Keras 00:08:00
Module 67: Continuation of Introduction to Tensorflow and Keras. 00:11:00
Module 68: Workflow 00:07:00
Module 69: Keras 00:05:00
Module 70: Continuation of Keras 00:02:00
Module 71: Integration 00:07:00
Module 72: Deep learning Techniques 00:03:00
Module 73: Continuation of Deep learning techniques 00:07:00
Module 74: Key Components 00:05:00
Module 75: Training 00:08:00
Module 76: Application 00:04:00
Module 77: Continuation of Application 00:05:00
Module 78: Recurrent Neural Networks 00:06:00
Module 79: Continuation of Recurrent Neural Networks. 00:03:00
Module 80: Training 00:03:00
Module 81: Varients 00:04:00
Module 82: Application 00:05:00
Module 83: RNN 00:05:00
Module 84: Transfer Learning and Fine Tuning 00:05:00
Module 85: Continuation Transfer Learning and Fine Tuning 00:07:00
Module 86: Fine Tuning 00:05:00
Module 87: Continuation Fine Tuning 00:04:00
Module 88: Best Practices 00:05:00
Module 89: Transfer Learning and Fine Tuning are powerful techniques 00:04:00
Module 90: Advance Deep Learning 00:05:00
Module 91: Architecture 00:07:00
Module 92: Training 00:04:00
Module 93: Training Process 00:04:00
Module 94: Application 00:06:00
Module 95: Generative Adversarial Network have 00:03:00
Module 96: Rainforcement Learning 00:05:00
Module 97: Reward Signal and Deep Reinforcement 00:04:00
Module 98: Techniques in Deep Reinforcement Learning 00:05:00
Module 99: Application of Deep Reinforcement 00:06:00
Module 100: Deep Reinforcement Learning has demonstrated 00:04:00
Module 101: Deployment & Model Management 00:04:00
Module 102: Flask for Web APIs 00:05:00
Module 103: Example 00:08:00
Module 104: Dockerization 00:08:00
Module 105: Example Dockerfile 00:10:00
Module 106: Flask and Docker provide a powerful combination 00:04:00
Module 107: Model Management & Monitoring 00:15:00
Module 108: Continuation of Model Management & Mentoring 00:04:00
Module 109: Model Monitoring 00:08:00
Module 110: Continuation of Model Monitoring 00:06:00
Module 111: Tools and Platforms 00:05:00
Module 112: By implementing effecting model management 00:04:00
Module 113: Ethical and Responsible AI 00:04:00
Module 114: Understanding Bias 00:10:00
Module 115: Promotion Fairness 00:07:00
Module 116: Module Ethical Considerations 00:07:00
Module 117: Tools & Resources 00:06:00
Module 118: Privacy and Security in ML 00:06:00
Module 119: Privacy Consideration 00:07:00
Module 120: Security Consideration 00:10:00
Module 121: Continuation of security Consideration 00:07:00
Module 122: Education & Awareness 00:07:00
Module 123: Capstone Project 00:09:00
Module 124: Project Task 00:04:00
Module 125: Evaluation and performance 00:07:00
Module 126: Privacy-Preservin g Deployment 00:08:00
Module 127: Learning Outcome 00:06:00
Module 128: Additional Resources and Practices 00:04:00
Module 129: Assignment 00:01:00
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