Shailesh Kulal

• Programming Languages : Python, OOPS, Exception handling, Multi-Threading, Database Connectivity
• Libraries : Numpy, Pandas, matplotlib, sklearn, scipy, EDA
• Databases : Oracle DB
• Programming Languages : Python, SQL
• Environments : Windows
• Deep Learning : ANN,CNN,RNN,LSTM
• Deep Learning Frameworks : Keras, Tensorflow
• Machine Learning Algorithms : Linear Regression, SVM, KNN, Logistic Regression
• Decision Trees, Random Forest, XGBoosting, Gradient Boosting, Recommendation/ Apriori Algorithm , Cluster Analysis, PCA, Time Series Forecasting, Naive Bayes, Lasso and Ridge Regression.
• Cloud Technologies : AWS
• IDE : Jupyter Notebook, PyCharm, IDLE

• Developing and implementing advanced analytics approaches including statistical modeling, to answer business questions & drive actionable insights using Python
• Hands on experience in Analytics - deriving key business impact from data and in creating algorithms, implementing an analytical solution based on analysis with large, complex, structured and unstructured data sets to provide better insights.
• Analyzed business problems using data from different sources to provide strategic and actionable business insights.
• Knowledge in statistical and machine learning techniques: Linear/logistic regression, Decision trees, Clustering,
• Neural networks etc.
• Understanding of statistical modeling/data mining concepts.
• Able to leverage a heavy dose of mathematics and applied statistics with visualization and a healthy sense of exploration.
• Team player with a ‘can do’ attitude.
• High degree of flexibility, independent and proactive working style.
• Ability to work well under pressure and on multiple and conflicting priorities.
• Love to have passion to learn, think out of box and hard working

Python for Beginners-Live course

• Statistics
• At least one programming language – R/ Python
• Data Wrangaling
• Data exploration
• Data Extraction
• Data Transformation and loading
• Machine Learning Algorithms
• Deep Learning
• Quantitative Analysis
• Mathematics
• Probability
• Debugging