Big Data and Machine Learning

Developed a multi-faceted Big Data and Machine Learning project, focusing on species prediction and image classification. Utilized advanced methods to analyze Iris flowers and wines, along with CIFAR10 image dataset classification. Implemented methods including: K-Means clustering, Gaussian Mixture Modeling, linear regression, PCA, LDA, Support Vector Machines, CNNs and Neural Networks.

What I Learned

1. Multi-Faceted Big Data and ML Project:

   • Developed a comprehensive project integrating Big Data and Machine Learning.
   • Focused on species prediction and image classification using diverse datasets.

2. Technical Skills Acquired:

   • Proficient in Python for data analysis and machine learning tasks.
   • Utilized essential libraries: NumPy, TensorFlow, and Matplotlib.

3. Diverse ML Techniques:

   • Implemented a suite of techniques such as K-Means clustering, Gaussian Mixture Modeling, linear regression, PCA, LDA, Support Vector Machines, and Neural Networks.
   • Emphasized Convolutional Neural Networks (CNNs) and Support Vector Classification (SVC) for image classification.

4. Dataset Analysis:

   • Analyzed Iris flowers and wines datasets alongside the CIFAR10 image dataset.

5. Proficiency in Machine Learning Libraries:

   • Gained proficiency in using TensorFlow for implementing and training neural networks.

6. Enhanced Understanding of:

   • Data analysis methodologies in the context of complex datasets.
   • Predictive modeling techniques for species prediction and image classification.
   • Algorithm optimization strategies for improved performance.

7. Hands-on Experience:

   • Acquired practical experience in applying machine learning techniques to real-world problems.
   • Demonstrated the ability to choose and implement appropriate algorithms based on the nature of the data and problem at hand.