Skills Required:
- ● Basic Knowledge of python coding
- ● Basic Knowledge of C/C++ coding standard
- ● Should have knowledge of basic libraries for machine learning such as scikit-learn and pandas, NumPy
- ● Should have knowledge of basic data visualization, data preprocessing, data-cleaning
- ● Basic knowledge of Computer vision required
- ● Familiarity with Linux
- ● Should have knowledge of basics of Deep learning
- ● Should have knowledge of how to build basic Convolutional neural net
- ● Should have basic knowledge of NLP
- ● Ability to think out of the box and passion in learning computer vision and machine learning is expected.
- ● Good debugging skills
- ● Basic knowledge of PCA and T-SNE
- ● Handling online code assistance, code mentoring, and education-based solutions
- ● Basic of OpenCV
- ● Should have knowledge of how to run code on an online tool like google collab
- ● Basic knowledge of Working with pre-trained models and deploying them on edge devices
Responsibilities:
- ● To study and convert data science prototypes.
- ● Understanding the problem statement
- ● Managing available resources such as hardware, data, and personnel so that deadlines are met
- ● Exploring and visualizing data to gain an understanding of it
- ● Exploring and visualizing data to gain an understanding of it
- ● Supervising the data acquisition process if more data is needed
- ● To perform statistical analysis and fine-tune models using test results.
- ● Defining validation strategies
- ● Defining the preprocessing or feature engineering to be done on a given dataset
- ● Defining data augmentation to increase the existing data set
- ● Training models and tuning their hyperparameters
- ● Analyzing the errors of the model and designing strategies to overcome them
- ● Deploying models to production
- ● Implementing and validating solution/s
- ● Solving object detection, recognition, image classification problems using machine learning and deep learning algorithms & transfer learning methods and techniques Using innovative & automated approaches for data annotation, labeling, and data augmentation & use active learning