hanifanta / recap-of-my-course-coursera

Coursera Specialization Recap

Coursera Specialization Recap

This repository contains a recap of my progress and learnings from the following Coursera specializations:

• Machine Learning Specialization
• DeepLearning.AI Specialization
• TensorFlow Data and Deployment Specialization

Machine Learning Specialization

This specialization provided a comprehensive overview of machine learning algorithms and techniques. It consisted of the following courses:

• Course 1: Machine Learning Foundations: A broad introduction to the field of machine learning, covering topics such as linear regression, classification, and model evaluation.

• Course 2: Machine Learning: Regression: Explored regression algorithms including linear regression, polynomial regression, and regularization methods.

• Course 3: Machine Learning: Classification: Focused on classification algorithms, such as logistic regression, decision trees, and random forests.

• Course 4: Machine Learning: Clustering & Retrieval: Covered unsupervised learning techniques like clustering, dimensionality reduction, and recommender systems.

• Course 5: Machine Learning: Recommender Systems & Dimensionality Reduction: Delved deeper into recommender systems and advanced dimensionality reduction methods.

• Course 6: Machine Learning Capstone: An opportunity to apply the knowledge gained throughout the specialization to a real-world project.

DeepLearning.AI Specialization

The DeepLearning.AI specialization provided a comprehensive understanding of deep learning algorithms and their applications. It consisted of the following courses:

• Course 1: Neural Networks and Deep Learning: Introduced the fundamental concepts of neural networks, deep learning, and their building blocks.

• Course 2: Improving Deep Neural Networks: Hyperparameter tuning, regularization, and optimization techniques were explored to improve model performance.

• Course 3: Structuring Machine Learning Projects: Discussed best practices for designing and structuring machine learning projects.

• Course 4: Convolutional Neural Networks: Focused on convolutional neural networks (CNNs) and their applications in image recognition and computer vision.

• Course 5: Sequence Models: Covered sequence models such as recurrent neural networks (RNNs) and long short-term memory (LSTM) networks.

• Course 6: DeepLearning.AI Capstone Project: Implemented a deep learning model on a real-world project to solve a specific problem.

TensorFlow Data and Deployment Specialization

The TensorFlow Data and Deployment specialization focused on practical aspects of using TensorFlow for data processing, model deployment, and scaling. It consisted of the following courses:

• Course 1: Browser-based Models with TensorFlow.js: Explored how to build and deploy machine learning models using TensorFlow.js, allowing for browser-based applications.

• Course 2: Device-based Models with TensorFlow Lite: Learned how to optimize and deploy machine learning models on resource-constrained devices using TensorFlow Lite.

• Course 3: Data Pipelines with TensorFlow Data Services: Covered techniques for building efficient data pipelines using TensorFlow Data Services (TFDS).

• Course 4: Advanced Deployment Scenarios with TensorFlow: Explored advanced deployment scenarios, including model serving with TensorFlow Serving, cloud-based deployment on Google Cloud Platform, and scaling models with TensorFlow on multiple machines.

Certificate