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Python & TensorFlow: Deep Dive into Machine Learning

Overview Python & TensorFlow: Deep Dive into Machine Learning empowers learners with essential skills in machine and deep learning using …

Python & TensorFlow: Deep Dive into Machine Learning

Python & TensorFlow: Deep Dive into Machine Learning

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Python & TensorFlow: Deep Dive into Machine Learning Overview

Python & TensorFlow: Deep Dive into Machine Learning empowers learners with essential skills in machine and deep learning using Python and TensorFlow. Commencing with foundational concepts, the course progresses seamlessly through practical TensorFlow applications, encompassing supervised and unsupervised learning techniques. Participants delve deeply into key aspects of deep learning, such as neural networks, model evaluation, and optimization strategies. By exploring TensorFlow applications for production environments and culminating in an image classification project, this course effectively prepares learners to actively apply machine learning in real-world scenarios.

Python & TensorFlow Machine Learning Learning Outcomes

  • Understand the fundamentals of machine learning and deep learning.
  • Install and configure TensorFlow for various machine learning tasks.
  • Implement supervised learning algorithms using TensorFlow.
  • Apply unsupervised learning techniques for data exploration and clustering.
  • Build neural networks with TensorFlow for deep learning applications.
  • Evaluate machine learning models and optimize their performance.
  • Deploy TensorFlow models in production environments.
  • Complete a hands-on project on image classification using TensorFlow.
  • Gain insights into best practices for model development and deployment.
  • Summarize their learning and apply TensorFlow skills to real-world machine learning challenges.

Who Is This Course For

Participants should possess a solid understanding of Python programming and basic knowledge of machine learning concepts. While familiarity with linear algebra and statistics is recommended, it is not required.

Entry Requirements

  • Age Requirement: Applicants must be aged 16 or above, allowing both young learners and adults to engage in this educational pursuit.
  • Academic Background: There are no specific educational prerequisites, opening the door to individuals from diverse academic histories.
  • Language Proficiency: A good command of the English language is essential for comprehension and engagement with the course materials.
  • Numeracy Skills: Basic numeracy skills are required for understanding nutritional data and dietary planning.

Why Choose Us

  • Affordable, engaging & high-quality e-learning study materials;
  • Tutorial videos/materials from the industry-leading experts;
  • Study in a user-friendly, advanced online learning platform;
  • Efficient exam systems for the assessment and instant result;
  • The UK & internationally recognised accredited
  • Access to course content on mobile, tablet or desktop from anywhere, anytime;
  • The benefit of career advancement opportunities;
  • 24/7 student support via email.

Career Path

Upon completing this course, participants can actively pursue careers as machine learning engineers, data scientists, AI developers, research scientists, machine learning-specialized software engineers, and data analysts.

 

Course Curriculum

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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