In the Machine Learning specialization, we will cover Supervised Learning, Unsupervised Learning, and the basics of Deep Learning. You will apply ML algorithms to real-world data, learn when to use which model and why, and improve the performance of your models.
Starting with supervised learning, we will cover linear and logistic regression, KNN, Decision trees, ensembling methods such as Random Forest and Boosting, and kernel methods such as SVM. Then we turn our attention to unsupervised methods, including dimensionality reduction techniques (e.g., PCA), clustering, and recommender systems. We finish with an introduction to deep learning basics, including choosing model architectures, building/training neural networks with libraries like Keras, and hands-on examples of CNNs and RNNs.
- Introduction to Machine Learning: Supervised Learning.
- Unsupervised Algorithms in Machine Learning.
- Introduction to Deep Learning.
What will you learn
- Explore several classic Supervised and Unsupervised Learning algorithms and introductory Deep Learning topics.
- Explain which Machine Learning models would be best to apply to a Machine Learning task based on the data’s properties.
- Build and evaluate Machine Learning models utilizing popular Python libraries and compare each algorithm’s strengths and weaknesses.
- Improve model performance by tuning hyperparameters and applying various techniques such as sampling and regularization.