Currently In Progress
DeepLearning.AI Deep Learning Specialization
Currently pursuing this advanced series on neural networks, which dives into core deep learning architectures and training strategies. It includes practical implementation of CNNs, RNNs, LSTMs, and Transformers using TensorFlow and Python.
By completing hands-on assignments across applications like image recognition, NLP, and neural style transfer, I’m deepening my understanding of model optimization, architecture design, and training dynamics.
Skills being developed: TensorFlow, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Sequence Modeling, Natural Language Processing (NLP), Neural Style Transfer, Transformers, HuggingFace, Dropout, Batch Normalization, Hyperparameter Tuning
Completed Certifications
DeepLearning.AI Machine Learning Specialization
Completed in July 2024, this specialization taught by Andrew Ng covers foundational and advanced machine learning techniques with a focus on real-world application. I gained hands-on experience developing and optimizing models using Python and industry-standard libraries.
Key learnings include: supervised learning algorithms (e.g., linear and logistic regression), tree-based models, ensemble techniques like XGBoost, recommender systems, anomaly detection, and core neural network concepts using TensorFlow.
To view my DeepLearning.AI Machine Learning Specialization certificate of completion awarded by Coursera and DeepLearning.AI, please click View Certificate.
Skills learned include: Supervised Learning, Unsupervised Learning, Reinforcement Learning, Gradient Descent, Logistic Regression, Linear Regression, Decision Trees, TensorFlow, XGBoost, Recommender Systems, Collaborative Filtering, Anomaly Detection, Artificial Neural Networks
IBM Data Science Professional Certificate
Earned in September 2023, this 10-course certificate program provided comprehensive training in the data science lifecycle—from data collection and cleaning to model development and deployment. It emphasized practical applications using real-world datasets, with tools like Pandas, Scikit-learn, SQL, and Matplotlib.
Projects covered exploratory data analysis, predictive modeling, dashboards, and more—enhancing my ability to draw actionable insights from complex data.
To view my IBM Data Science Professional Certificate of completion awarded by Coursera and IBM, please click View Certificate.
Skills learned include: Data Analysis, Data Wrangling, Data Visualization, Classification, Regression, Clustering, Recommender Systems, Python, SQL, Pandas, NumPy, Scikit-learn, Seaborn, Matplotlib, GitHub, Jupyter Notebooks, Machine Learning