Certification in Machine Learning and Deep Learning
Training Express Ltd
Updated 2026 | 129 Modules Instructor Lead Video Classes | FREE PDF & Hard Copy Certificate | Lifetime Access
Summary
Price
£19 inc VAT
Duration
11.8 hours · Self-paced
Qualification
No formal qualification
CPD
10 CPD hours / points
CPD
What's this?
Certificates
- Digital certificate - Free
- Hard copy certificate - Free
- Reed Courses Certificate of Completion - Free
Additional info
- Tutor is available to students
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Overview
Certificates
Included
Reed Courses Certificate of Completion
Digital certificate
- Show recruiters you've mastered in-demand skills
- Boost your CV and job applications instantly
- Share on LinkedIn to stand out from other candidates
- Transcript of completion included
Included
Digital certificate
Digital certificate
Once you’ve successfully completed your course, you will immediately be sent a FREE digital certificate.
Included
Hard copy certificate
Hard copy certificate
Also, you can have your FREE printed certificate delivered by post (shipping cost £5.99 in the UK).
For all international addresses outside of the United Kingdom, the delivery fee for a hardcopy certificate will be only £10.
Our certifications have no expiry dates, although we do recommend that you renew them every 12 months.
CPD
10 CPD hours / points
Accredited by CPD Quality Standards
Curriculum
1
section
127
lectures
11h 46m
total
Course media
Description
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
and more....
Certification
Once you’ve successfully completed your Machine Learning and Deep Learning Course, you will immediately be sent a digital certificate. Also, you can have your printed certificate delivered by post (shipping cost £5.99). Our Machine Learning and Deep Learning Course certification has no expiry dates, although we do recommend that you renew them every 12 months.
Who is this course for?
- Beginners interested in machine learning concepts and workflows
- Data science learners exploring ML and deep learning
- Students aiming to understand Python ML libraries
- Tech enthusiasts seeking AI and model deployment knowledge
- Individuals interested in ethical and responsible AI
Career path
- Machine Learning Assistant
- Data Analyst Intern
- AI Research Assistant
- Junior Python Developer
- Deep Learning Support Analyst
- ML Model Monitoring Assistant
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This course is advertised on Reed.co.uk by the Course Provider, whose terms and conditions apply. Purchases are made directly from the Course Provider, and as such, content and materials are supplied by the Course Provider directly. Reed is acting as agent and not reseller in relation to this course. Reed's only responsibility is to facilitate your payment for the course. It is your responsibility to review and agree to the Course Provider's terms and conditions and satisfy yourself as to the suitability of the course you intend to purchase. Reed will not have any responsibility for the content of the course and/or associated materials.
FAQs
Study method describes the format in which the course will be delivered. At Reed Courses, courses are delivered in a number of ways, including online courses, where the course content can be accessed online remotely, and classroom courses, where courses are delivered in person at a classroom venue.
CPD stands for Continuing Professional Development. If you work in certain professions or for certain companies, your employer may require you to complete a number of CPD hours or points, per year. You can find a range of CPD courses on Reed Courses, many of which can be completed online.
A regulated qualification is delivered by a learning institution which is regulated by a government body. In England, the government body which regulates courses is Ofqual. Ofqual regulated qualifications sit on the Regulated Qualifications Framework (RQF), which can help students understand how different qualifications in different fields compare to each other. The framework also helps students to understand what qualifications they need to progress towards a higher learning goal, such as a university degree or equivalent higher education award.
An endorsed course is a skills based course which has been checked over and approved by an independent awarding body. Endorsed courses are not regulated so do not result in a qualification - however, the student can usually purchase a certificate showing the awarding body's logo if they wish. Certain awarding bodies - such as Quality Licence Scheme and TQUK - have developed endorsement schemes as a way to help students select the best skills based courses for them.
