Ershveer Singh Saini

A Programmer with driving force and enthusiasm to work in Software/AI field. Seeks higher and execution roles in the field of Artificial Intelligence/Robotics and use this knowledge cum experience acquired to develop futuristic new resources and advancements required in these fields. Offers strong interpersonal skills, commitment to work, including sincerity, integrity and loyalty.

Deep Learning for Images with PyTorch (May 2024)

TRAINING & PROJECTS CHANDIGARH UNIVERSITY

PROJECTS

COLOUR RECOGNIZATION SYSTEM USING OPEN CV (ML PROJECT) (Aug-Sept 2021)

• Its whole purpose was to extend the intellect of the synthetic mechanism available and detect the color.
• Used Jypter Notebook, Python, Open CV, Panda's libraries.
• Used Naïve Bayes classifiers, made functions to calculate RGB values and mouse call back for creating window for input calculations.

IMAGE RECOGNITION SYSTEM (ML PROJECT) (Jan-Apr 2021)

• The goal is to discover parameter values that produce accurate model output as often as possible.
• The model which is build, then fed the image dataset with its known and correct labels.
• Performed using technologies Jupyter Notebook, Python, Google Colab, Keras and TensorFlow.
• Used neural networks to train, test and predict the images.
• Trained and tested thousands of images, made a Softmax classifier and maxpool and convocational layer.

CUSTOMER CHURN PREDICTION (ML PROJECT) (May-June 2020)

• Designed a churn prediction for customer to check retention using: Pandas, Numpy, Scikit Learn.
• Used Logistic Regression, Random Forest Trees and Gradient Boosting Algorithm.
• By using PyTorch, learnt about Generative Adversarial Networks(GANs) as well as how to assess the quality and diversity of generated images.
• Worked on both binary and multi-class image classification models, utilize pre-trained models for deep learning tasks, and master object detection with bounding boxes.

COURSES/TRAINING

Data Aanalyst in Python (Feb-Present 2025)

• Gain the data analyst skills to manipulate, analyze, and visualize data.
• Learning seaborn, pandas, exploratory data analysis and hypothesis testng

DEEP LEARNING SPECAILIZATION (COURSE) (Sep-Nov 2020)

• Completed 5 months of specialization course ( total 5) and learnt Neural Networks & Deep Learning.
• Hyperparameter Tuning, Regularization and Optimization.
• Structuring Machine Learning Projects.
• Convolutional Neural Network, Computer Vision.
• Sequence Models.

MACHINE LEARNING (TRAINING) (May-June 2020)

• Completed 6 weeks of training and learnt Evaluation.
• Metrics, KNN, Selecting the Right Model, Linear.
• Regression, Logistic Regression, Decision Trees.
• Feature Engineering, Basics of Ensemble Models.
• Random Forest and Cluster modules.

FUNDAMENTALS OF ARTIFICIAL INTELLIGENCE (COURSE)

(July-Oct 2019)

• Completed 12 weeks of course and learnt principles of AI, practices of AI to address complex real world problems. Developed the basic understanding of problem solving, knowledge representation, reasoning and learning methods of AI.

1. Deep Learning for Images with PyTorch

2. Intermediate Deep Learning with PyTorch

3. Natural Language Processing in Python

4. Fundamentals Of Artificial Intelligence

5. Machine Learning Training

6. FilpKart Grid 2.0 Robotics Competition

7. Deep Learning Specialization