IBM Machine Learning Certificate Course

This workbook relates to all of the various labs and some personal workbooks whichs covers all the material from the IBM Machine Learning Course.

Exploratory Data Analysis

Labs cover the below topics:

• Reading Data using Pandas and SQL
• EDA concepts
• Hypothesis Testing
• Final Assignment: Performed EDA on Tech Layoffs Dataset from Kaggle

Supervised Machine Learning - Regression

Labs cover the below topics:

• Polynomial Regression and Creating Train and Testing splits of datasets
• Cross Validation
• Overfitting and Regularization

Supervised Machine Learning - Classification

Labs cover the below topics:

• Logistic Regression and Error Metrics
• K-Nearest Neighbours
• Support Vector Machines
• Decision Trees
• Ensemble Methods including Boosting, Stacking and Bagging
• Handling Imbalanced Datasets and Model Agnostic Explanations
• Final Assignment: Classifying Student Performance into GPA Buckets using Student Performance Dataset from Kaggle

Unsupervised Machine Learning

Labs include the below topics:

• K-Means and GMM Clustering
• Dimensionality and Distance Metrics
• DBSCAN and Evaluation of different Clustering Methods
• Dimensionality Reduction, PCA and SVD
• Kernel PCA and Multi Dimensional Scaling
• Non-Negative Matrix Factorization
• Final Assignment: Clustering California Housing Dataset using KMeans, Mean Shift and DBSCAN

Deep Learning and Reinforcement Learning

• Intro to Neural Networks and Keras
• Gradient Descent and Optimizers