Advanced Computer Vision - Object Detection and recognition
Course Computer Vision
The project involves 3 sub segments
CNN, NN, Computer Vision, Tensor Flow, Siamese Networks, Triplet Loss, Object Detection
Course Computer Vision
The project involves 3 sub segments
- Part 1 Implement an object detection model for highlighting human faces to automate the process of providing information of cast and crew while streaming.
- Part 2 Curate a training dataset to be used for highlighting human faces
- Part 3 Implement a face identification model for a company, which intends to recognize human faces from images.
CNN, NN, Computer Vision, Tensor Flow, Siamese Networks, Triplet Loss, Object Detection
CNN Architecture and Transfer Learning
Course Computer Vision
This project involves 2 subprojects to solve the problem of a botanical research group. Part 1 Image classifier capable of determining a plant's species and detailed analysis on how CNN is a better image classifier over traditional methods. Part 2 Image classifier capable of determining a flower’s species using CNN and curating an image dataset. Dataset - The dataset comprises images from 12 plant species. The dataset comprises images from 17 flower species “import tflearn.datasets.oxflower1"
Skills and Tools
Computer Vision, CNN, Transfer Learning, TensorFlow, GUI
Course Computer Vision
This project involves 2 subprojects to solve the problem of a botanical research group. Part 1 Image classifier capable of determining a plant's species and detailed analysis on how CNN is a better image classifier over traditional methods. Part 2 Image classifier capable of determining a flower’s species using CNN and curating an image dataset. Dataset - The dataset comprises images from 12 plant species. The dataset comprises images from 17 flower species “import tflearn.datasets.oxflower1"
Skills and Tools
Computer Vision, CNN, Transfer Learning, TensorFlow, GUI
Neural Networks & Deep Learning
Course Introduction to Neural Network and Deep Learning
The project was accomplished by delivering 2 sub-projects. Part 1 deploys a neural network to build a regressor & classifier respectively for a communications equipment manufacturer. The model predicts the equipment’s signal quality using various parameters from its products, which is responsible for emitting informative signals. Part 2 delivers an image classifier, which can classify numbers from the photographs captured at street level using a Neural Network”
Skills and Tools
Electronics and Telecommunication, Neural Networks, Deep Learning, TensorFlow, Image Recognition
Course Introduction to Neural Network and Deep Learning
The project was accomplished by delivering 2 sub-projects. Part 1 deploys a neural network to build a regressor & classifier respectively for a communications equipment manufacturer. The model predicts the equipment’s signal quality using various parameters from its products, which is responsible for emitting informative signals. Part 2 delivers an image classifier, which can classify numbers from the photographs captured at street level using a Neural Network”
Skills and Tools
Electronics and Telecommunication, Neural Networks, Deep Learning, TensorFlow, Image Recognition
Recommendation Systems
Course Recommendation Systems
This project replicates a real time use case of an e-commerce company, which can recommend mobile phones to a user, which are most popular and personalized respectively. The project was accomplished by employing recommendation techniques such as popularity-based recommendation and collaborative filtering methods to recommend a mobile handset to its users based on the individual consumer’s behavior/choice.
Skills and Tools
Collaborative Filtering, Popularity-based, Recommender Systems, Python
Course Recommendation Systems
This project replicates a real time use case of an e-commerce company, which can recommend mobile phones to a user, which are most popular and personalized respectively. The project was accomplished by employing recommendation techniques such as popularity-based recommendation and collaborative filtering methods to recommend a mobile handset to its users based on the individual consumer’s behavior/choice.
Skills and Tools
Collaborative Filtering, Popularity-based, Recommender Systems, Python
Feature Engineering & Model Tuning
Course Featurization, Model Selection & Tuning
The project was accomplished by employing supervised learning, ensemble modeling, and unsupervised learning techniques to build and train a prediction model to identify Pass/Fail yield of a particular process entity for a semiconductor manufacturing company. This project helps to determine key factors contributing to yield excursions downstream in the process and will enable an increase in process throughput, decreased time to learn and reduce per-unit production costs. Dataset – The dataset pr
Skills and Tools
Supervised learning, PCA, Feature engineering, Model tuning, Grid Search, python
Course Featurization, Model Selection & Tuning
The project was accomplished by employing supervised learning, ensemble modeling, and unsupervised learning techniques to build and train a prediction model to identify Pass/Fail yield of a particular process entity for a semiconductor manufacturing company. This project helps to determine key factors contributing to yield excursions downstream in the process and will enable an increase in process throughput, decreased time to learn and reduce per-unit production costs. Dataset – The dataset pr
Skills and Tools
Supervised learning, PCA, Feature engineering, Model tuning, Grid Search, python
Unsupervised Learning Project
Course Unsupervised Learning
Clustering, Support Vector Machines, Principal Component Analysis, Classification, Python
Course Unsupervised Learning
- Part 1 - To segment cars into various categories by fuel consumption and other attributes
- Part 2 - To classify a given silhouette as one of three types of vehicle, using a set of features extracted from the silhouette. The vehicle may be viewed from one of many different angles.
Clustering, Support Vector Machines, Principal Component Analysis, Classification, Python
Ensemble Techniques Project
Course Ensemble Techniques
This project is based on the case study of a telecommunication company, which is facing a customer churn issue. The project aims at understanding the pattern of the data and predicting customers who are going to churn based on multiple variables to help the company in retaining their existing customers. The project was accomplished by building a machine learning workflow that will run autonomously with the CSV file and return the best-performing model.
Skills and Tools
EDA, Logistic regression, Decision Trees, Random forest, XGboost, Adaboost, python, ML workflow
Course Ensemble Techniques
This project is based on the case study of a telecommunication company, which is facing a customer churn issue. The project aims at understanding the pattern of the data and predicting customers who are going to churn based on multiple variables to help the company in retaining their existing customers. The project was accomplished by building a machine learning workflow that will run autonomously with the CSV file and return the best-performing model.
Skills and Tools
EDA, Logistic regression, Decision Trees, Random forest, XGboost, Adaboost, python, ML workflow
Supervised Learning
Course Supervised Learning
This project uses the most popular classification techniques to predict the outcomes after an extensive EDA and work missing values, and imbalance in data. This project has two parts.
Logistic Regression,Naive Bayes, KNN, Classification, Python
Course Supervised Learning
This project uses the most popular classification techniques to predict the outcomes after an extensive EDA and work missing values, and imbalance in data. This project has two parts.
- Part 1 - Predicting the condition of the patient depending on the received test results on biomechanics features of the patients according to their current conditions.
- Part 2 - Build an AIML model to perform focused marketing by predicting the potential customers who will convert using the historical database.
Logistic Regression,Naive Bayes, KNN, Classification, Python
Applied Statistics Project
Course - Applied Statistics
This project uses Plotting distribution, Visualization and Hypothesis Testing to validate statistical evidence and leverage information to make effective decisions
Course - Applied Statistics
This project uses Plotting distribution, Visualization and Hypothesis Testing to validate statistical evidence and leverage information to make effective decisions
- Part 1 - Answering Industry Problems through Statistical inferences
- Part 2 - Analyze past tournament information to make informative investment decisions.
- Part 3 - Analyzing the status of various startups that participated in the Startup Battlefield which is the world’s pre-eminent startup competition.