During my tenure as a Machine Learning Bootcamp Intern at SDAIA (Saudi Data and Artificial Intelligence Authority), I embarked on an intensive journey into the realm of machine learning. The experience has been instrumental in shaping my understanding of machine learning concepts and preparing me for future challenges in the field.
Starting with the fundamentals of data preprocessing, I progressed through a range of topics, including:
- Supervised Learning: Implemented various supervised learning models such as linear and logistic regression, decision trees, and more.
- Unsupervised Learning: Gained experience in clustering and other unsupervised techniques.
- Model Evaluation: Conducted statistical evaluations of models to ensure their accuracy and effectiveness.
By the conclusion of the bootcamp, I emerged equipped with the knowledge and skills to:
- Design Machine Learning Models: From scratch, based on problem requirements.
- Train Algorithms: Build and train models using diverse data sets.
- Evaluate Models: Use appropriate metrics to assess the performance of algorithms and make informed decisions.
- Objective: Clean and preprocess hotel booking data for further analysis.
- Key Tasks:
- Handled missing and duplicate data.
- Performed feature selection to remove irrelevant or redundant features.
- Applied data transformation techniques for uniformity in data.
- Objective: Develop a machine learning model to predict whether a loan will be approved based on applicant information.
- Key Tasks:
- Built a logistic regression model to classify loan applications.
- Conducted feature engineering to create new variables that improved model performance.
- Evaluated the model using precision and recall to balance correct approvals and rejections.
- Objective: Predict the likelihood of diabetes onset in patients based on medical data.
- Key Tasks:
- Cleaned and preprocessed medical data.
- Applied linear regression to predict diabetes onset based on factors like BMI, glucose levels, and age.
- Evaluated the model using mean squared error and visualized accuracy through scatter plots.
- Objective: Create a decision tree model to detect swine flu cases based on patient symptoms.
- Key Tasks:
- Preprocessed patient symptom data and handled missing values.
- Built a decision tree to classify cases as swine flu positive or negative.
- Pruned the tree to reduce complexity and improve generalization.
- Objective: Predict whether a person is a smoker based on demographic and medical data.
- Key Tasks:
- Implemented logistic regression and K-Nearest Neighbors to classify individuals as smokers or non-smokers.
- Compared model performance using confusion matrices and AUC-ROC curves.
- Focused on improving recall to better identify smokers.
- Objective: Use multi-linear regression to predict continuous outcomes.
- Key Tasks:
- Worked on datasets requiring prediction of variables like housing prices or insurance premiums.
- Optimized models using regularization techniques like Ridge and Lasso regression.
- Evaluated model performance with R-squared and root mean square error (RMSE).
- Gained hands-on experience with both supervised and unsupervised machine learning models.
- Acquired proficiency in transforming raw data into actionable insights through preprocessing and feature engineering.
- Mastered model evaluation techniques to ensure high performance and generalization on unseen data.
- Learned how to optimize models through hyperparameter tuning and regularization techniques to improve accuracy and reduce overfitting.
The SDAIA Machine Learning Bootcamp has been a transformative experience, allowing me to deeply engage with machine learning concepts and real-world applications. Through practical projects, I honed my ability to design, train, and evaluate machine learning models, ensuring they are optimized for performance and accuracy. This experience has solidified my confidence in tackling complex data science challenges and leveraging machine learning to create impactful, data-driven solutions.