A comprehensive course guide covering Machine Learning, Data Science and Deep Learning with Python. This guide spans data preprocessing, supervised learning (regression and classification), evaluation and tuning, unsupervised learning, and advanced topics including natural language processing (NLP), deep learning, data mining, reinforcement learning, big data, and experimental design.

Topics Covered

• Data Preprocessing: Importing data, missing values, categorical data, feature scaling, splitting datasets
• Regression: Simple/Multiple/Polynomial Linear Regression, SVR, Decision Tree, Random Forest Regression
• Classification: Logistic Regression, KNN, SVM, Kernel SVM, Naive Bayes, Decision Tree, Random Forest Classification
• Model Selection & Tuning: K-Fold Cross Validation, Grid Search, Boosting (XGBoost)
• Metrics: Confusion Matrix, Accuracy, Precision, Recall
• Clustering: K-Means Clustering, Hierarchical Clustering
• Association Rule Learning: Apriori, Eclat
• Natural Language Processing (NLP): Text processing with Artificial Neural Networks
• Deep Learning: ANNs, CNNs, Reinforcement Learning, TensorFlow, Keras, CNN, RNN, Transfer Learning, Case Studies (Politics, Mammal Masses)
• Data Mining: Similar Movies, Item-Based CF
• Reinforcement Learning: Upper Confidence Bound, Thompson Sampling
• Dimensionality Reduction: PCA, LDA, Kernel PCA
• Big Data: Apache Spark, Spark MLLib
• Experimental Design: T-Test, Python 101, Remixability

GitHub Repository

View full code and examples on GitHub