Data cleaning through the EDA, Model Building
- Clear understanding of Machine Learning, Deep Learning, NLP, Algorithm
- Supervise Machine learning algorithms such as- linear and logistic regressions,
Decision tree, Random Forest, Naïve Bayes, k-NN, SVM and Neural Networks.
- Unsupervised machine learning- Hierarchical clustering, k-means clustering and principal component analysis (PCA)
- Involved in converting unstructured data into structured and workable datasets.
- Ability to use a language like Python to work at scale with large data sets and tools like SQL
- Involved in publishing the python code in the Git - Hub
- Good command on statistical techniques like Regression, Classification & clustering.
- Experience in data visualization techniques for final report communication to clients.
- Expertise in data exploration techniques using various statistical tools.
- Experience using NLP toolkits like Spacy, NLTK, TextBlob
- Experience using data science libraries Pandas, Numpy, Scipy, Seaborn
- Build predictive models and machine-learning algorithms
- Combine models through ensemble modelling
- Experience with exploratory data analysis, statistical analysis,
- Knowledge and skills to turn raw data into information and insight, which can be used to make business decisions
