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Key features of the Machine Learning and Deep Learning Certification

    • Recognised qualification upon successful completion of the course
    • Study from anywhere, anytime, whenever it is convenient for you.
    • Get 24/7 support from our Customer Success Team
    • Affordable and engaging e-learning study materials
    • Study at your own pace from a tablet, PC or smartphone
    • Online tutor support when you are in need.

Who is this course for?

There is no experience or previous qualifications required for enrolment on this course. It is available to all students, of all academic backgrounds.

Requirements

Our Machine Learning and Deep Learning Certification is fully compatible with any kind of device. Whether you are using Windows computer, Mac, smartphones or tablets, you will get the same experience while learning. Besides that, you will be able to access the course with any kind of internet connection from anywhere at any time without any kind of limitation.

Career Path

After completing this course you will be able to build up accurate knowledge and skills with proper confidence to enrich yourself and brighten up your career in the relevant job market.

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 00:04:00
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 00:03:00
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 00:09:00
Module 29: Regression 00:03:00
Module 30: Types of Regression 00:07:00
Module 31: Lasso Regration 00:08:00
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 00:03:00
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 00:08:00
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
Certificate and Transcript
Order Your Certificates or Transcripts 00:00:00

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    The course offered is excellent. I am glad to have taken it.
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