Data Science Boot Camp

What I learned

How to pre-process data

I learned how to analyze an initial dataset using Pandas and NumPy. The goal being to locate inconsistencies in data using statistical techniques and analysis. Through the exploratory analysis practice I learned valuables skills on how to visualize data to spot anomalies.

Understand the mathematics behind Machine Learning

The course also had sections dedicated to understand the statistical methodology used in the ML algorithms. Through this I learned how linear and logistic regressions work. Understood the mechanics of a neural network from how the bias and weights affect each other.

Applied Machine Learning Models

Some models I applied were linear, logistic regression, k-means clustering, and built neural networks. These implementation of these algorithms can be found on my GitHub. Through this I was able to practice my Python and the various machine learning libraries such as NumPy, statsmodels, TensorFlow and scikit-learn. I also learned how to improve models by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance.

Case studies

The course also walked me through various real-life business cases from which I learned how to translate my technical skills to answer business questions.