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