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Ever imagined a program that can learn from data, recognize patterns, and make intelligent predictions? That’s the magic of Machine Learning, and Python & TensorFlow: Deep Dive into Machine Learning is your key to unlocking its potential. This comprehensive course dives deep into the theoretical foundations of Machine Learning and Deep Learning, using Python as the programming language and TensorFlow as the powerful framework.
Get ready to explore the fascinating world of supervised and unsupervised learning, unravel the complexities of Neural Networks, and master the art of model evaluation and optimization. By the end, you’ll be equipped with a solid understanding of these cutting-edge technologies, ready to tackle your own Machine Learning projects.
This course is purely theoretical, focusing on building a strong conceptual foundation. So, prepare to embark on an intellectual journey that will transform your understanding of artificial intelligence and its applications.
Learning Outcomes
After studying the course materials of the Python & TensorFlow: Deep Dive into Machine Learning there will be a written assignment test which you can take either during or at the end of the course. After successfully passing the test you will be able to claim the pdf certificate for £4.99. Original Hard Copy certificates need to be ordered at an additional cost of £8.
This Python & TensorFlow: Deep Dive into Machine Learning does not require you to have any prior qualifications or experience. You can just enrol and start learning. This Python & TensorFlow: Deep Dive into Machine Learning was made by professionals and it is compatible with all PC’s, Mac’s, tablets and smartphones. You will be able to access the course from anywhere at any time as long as you have a good enough internet connection.
Introduction to Machine & Deep Learning | |||
What is Machine Learning? | 00:03:00 | ||
Types of Machine Learning | 00:03:00 | ||
Applications of Machine Learning | 00:03:00 | ||
What is Deep Learning? | 00:04:00 | ||
Basics of TensorFlow & Installation | |||
What is TensorFlow? | 00:05:00 | ||
Installing and Setting up TensorFlow | 00:03:00 | ||
TensorFlow Architecture | 00:04:00 | ||
A refresher on APIs | 00:08:00 | ||
TensorFlow APls | 00:04:00 | ||
Machine Learning Part 1: Supervised Learning | |||
What is Supervised Learning? | 00:03:00 | ||
Linear Regression | 00:10:00 | ||
Logistic Regression | 00:13:00 | ||
Decision Trees | 00:08:00 | ||
Random Forests | 00:08:00 | ||
Support Vector Machines (SVMs) | 00:05:00 | ||
Machine Learning Part 2: Unsupervised Learning | |||
What is Unsupervised Learning? | 00:09:00 | ||
K-Means Clustering | 00:06:00 | ||
Hierarchical Clustering | 00:06:00 | ||
Principal Component Analysis (PCA) | 00:04:00 | ||
Deep Learning Basics with Tensorflow: Neural Networks | |||
What are Neural Networks? | 00:04:00 | ||
Basic Neural Networks | 00:05:00 | ||
Convolutional Neural Networks (CNNs) | 00:06:00 | ||
Recurrent Neural Networks (RNNs) | 00:04:00 | ||
Building Deep Neural Networks | 00:05:00 | ||
Model Evaluation & Optimization | |||
Training and Testing Data | 00:04:00 | ||
Model Evaluation Metrics | 00:05:00 | ||
Overfitting and Underfitting | 00:07:00 | ||
Hyperparameter Tuning | 00:04:00 | ||
TensorFlow for Production | |||
Saving and restoring models | 00:04:00 | ||
Deploying TensorFlow models | 00:04:00 | ||
Distributed TensorFlow | 00:04:00 | ||
TensorBoard for visualization and debugging | 00:06:00 | ||
Project: Image Classification | |||
ML Project: Image Classification Model | 00:05:00 | ||
Conclusion | |||
Conclusion | 00:05:00 |
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