Machine learning specialization
In my free time, I completed a Machine Learning specialization course, where I gained expertise in building and applying advanced ML models to solve real-world problems.
Course Content
The course covered a wide range of Machine Learning techniques, structured into 10 key modules:
- Data Preprocessing
- Regression Models: Simple & Multiple Linear Regression, Polynomial Regression, SVR, Decision Trees, and Random Forest Regression.
- Classification Models: Logistic Regression, K-NN, SVM, Kernel SVM, Naïve Bayes, Decision Trees, and Random Forest Classification.
- Clustering: K-Means, Hierarchical Clustering.
- Association Rule Learning: Apriori, Eclat.
- Reinforcement Learning: Upper Confidence Bound (UCB), Thompson Sampling.
- Natural Language Processing (NLP): Bag-of-Words Model and NLP algorithms.
- Deep Learning: Artificial Neural Networks (ANNs) and Convolutional Neural Networks (CNNs).
- Dimensionality Reduction: PCA, LDA, Kernel PCA.
- Model Selection & Boosting: k-Fold Cross Validation, Hyperparameter Tuning, Grid Search, XGBoost.
The course was highly hands-on, with practical exercises based on real-world case studies, allowing me to apply ML techniques to real datasets.
Additional Resources & Benefits
- Complete Python for all models, ready for implementation in personal projects.
Machine learning specialist
Key Learning Outcomes
Upon completion of the course, I acquired the ability to:
✅ Master Machine Learning with Python.
✅ Develop a strong intuition for various ML models.
✅ Make accurate predictions and perform advanced data analysis.
✅ Build robust ML models for business applications.
✅ Apply Reinforcement Learning, NLP, and Deep Learning techniques.
✅ Utilize dimensionality reduction for complex datasets.
✅ Select the most suitable ML model for any given problem.
✅ Combine multiple ML models for optimized solutions.