https://github.com/bursasha/deeplearningai-machine-learning-tensorflow-course

Projects and certificates from a machine learning course focused on TensorFlow and Keras, covering neural networks, CNNs, NLP, and time series analysis 🧠
https://github.com/bursasha/deeplearningai-machine-learning-tensorflow-course

convolutional-neural-networks deeplearning-ai keras machine-learning mlcourse natural-language-processing neural-networks numpy pandas python-machine-learning recurrent-neural-networks tensorflow

Last synced: 28 days ago
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Projects and certificates from a machine learning course focused on TensorFlow and Keras, covering neural networks, CNNs, NLP, and time series analysis 🧠

• Host: GitHub
• URL: https://github.com/bursasha/deeplearningai-machine-learning-tensorflow-course
• Owner: bursasha
• Created: 2024-06-28T09:38:28.000Z (10 months ago)
• Default Branch: main
• Last Pushed: 2024-06-28T10:09:30.000Z (10 months ago)
• Last Synced: 2025-02-09T01:17:28.066Z (3 months ago)
• Topics: convolutional-neural-networks, deeplearning-ai, keras, machine-learning, mlcourse, natural-language-processing, neural-networks, numpy, pandas, python-machine-learning, recurrent-neural-networks, tensorflow
• Language: Jupyter Notebook
• Homepage:
• Size: 1.37 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# **Machine Learning with `TensorFlow` and `Keras`** 💠

## **Repository Overview** 📄

This repository contains comprehensive materials from a course on **Machine Learning** using `TensorFlow` and `Keras`, 

offering both theoretical concepts and practical implementations. 

It demonstrates deep learning techniques across various domains such as _image recognition_, _natural language processing_, and _time series analysis_.

## **Repository Structure** 📂

- **`intro/`**: Modules covering basic concepts and introductory techniques in neural networks.

- **`convnetwork/`**: Projects and examples demonstrating the use of _Convolutional Neural Networks_.

- **`nlp/`**: Exercises and projects focused on _Natural Language Processing_.

- **`timeseries/`**: Techniques and models for _time series forecasting and analysis_.

- **`README.md`**: The main README file providing an overview of the repository.

## **Course Description** 📚

The **"Machine Learning with `TensorFlow` and `Keras`"** course provides a deep dive into the methods and applications of machine learning. 

The course is structured into several key areas:

- **Introduction to Neural Networks**:

  - Fundamentals of using TensorFlow for building simple to complex neural network architectures.

- **Convolutional Neural Networks**:

  - In-depth exploration of CNNs for handling image data.

- **Natural Language Processing**:

  - Techniques for text data processing, sentiment analysis, and language modeling.

- **Time Series Analysis**:

  - Application of neural networks to predict and analyze time-dependent data sets.

## **Key Projects and Their Purpose** 📌

### 1. **`intro`**:

- Focuses on the basics of _neural networks_, implementing foundational models to understand layer functions and network architecture.

### 2. **`convnetwork`**:

- Demonstrates advanced _image recognition and classification_ tasks using _CNNs_, enhancing feature extraction capabilities.

### 3. **`nlp`**:

- Applies _recurrent neural networks_ and other advanced techniques to process and generate language-based data.

### 4. **`timeseries`**:

- Explores models like _LSTM_ to handle time-dependent patterns and predict future values based on historical data.

## **Tools and Techniques Used** 🛠️

- **`TensorFlow`** and **`Keras`**:

    - Utilized for building and training neural network models.

    - Key functionalities include creating layers, adjusting hyperparameters, and implementing loss functions.

- **`Python`**:

    - Programming language used for all scripting and development.

    - Extensive use of data handling libraries like `NumPy` and `pandas`.

- **`Jupyter` Notebooks**:

    - For interactive code execution, visualization, and real-time data analysis.

## **Concepts Applied** 📚

- **Neural Network Training and Validation**:

    - Techniques for training models efficiently and validating their accuracy and generalization capabilities.

- **Feature Extraction and Image Processing**:

    - Utilizing CNNs to extract features from images and improve model accuracy.

- **Text Analysis and Sentiment Detection**:

    - Analyzing text data to understand sentiments and contextual meanings.

- **Forecasting and Trend Analysis**:

    - Using historical data to forecast future trends with recurrent neural networks.

## **Conclusion** 📝

This repository encapsulates my journey through the **"Machine Learning with `TensorFlow` and `Keras`"** course, 

demonstrating a structured engagement with foundational and advanced machine learning techniques. 

Throughout this course, I've familiarized myself with pivotal frameworks such as `TensorFlow` and `Keras`, 

exploring their functionalities across a range of applications from image and text processing to time series analysis.

Each module of the course was paired with practical assignments and projects, reinforcing my understanding of key concepts and mechanics in _neural networks_, 

_convolutional networks_, _natural language processing_, and more. 

The completion of these modules is evidenced by certificates.

This educational endeavor has not only bolstered my theoretical knowledge but also enhanced my practical skills, 

preparing me to tackle real-world data-driven challenges using advanced machine learning tools and methodologies.