ML-Specialization-Notebooks

This repository contains Jupyter Notebooks built whilst taking some introductory courses of DataCamp's Machine Learning Specialization.

Note: These notebooks might not follow the exact order or content of the exercises, as the purpose of creating these was to do complete / extended analysis on the practice exercises in the specialization, and try to improve knowledge beyond the sample Q&As. These are also about 1.5 years old, so if the courses have updated now, then you might not find them as useful today.

0. Data Manipulation with Pandas

An Introduction to the Pandas Python API for cleaning data (imputation, analysis, reporting, etc.)

1. Introduction to PySpark

Apache PySpark Basics (Context, DataFrames and SparkML) with Flights Dataset (and reference airports and planes):

year	month	day	dep_time	dep_delay	arr_time	arr_delay	carrier	tailnum	flight	origin	dest	air_time	distance	hour	minute
2014	12	8	658	-7	935	-5	VX	N846VA	1780	SEA	LAX	132	954	6	58
2014	1	22	1040	5	1505	5	AS	N559AS	851	SEA	HNL	360	2677	10	40
2014	3	9	1443	-2	1652	2	VX	N847VA	755	SEA	SFO	111	679	14	43
2014	4	9	1705	45	1839	34	WN	N360SW	344	PDX	SJC	83	569	17	5
2014	3	9	754	-1	1015	1	AS	N612AS	522	SEA	BUR	127	937	7	54

2. Supervised Learning with Scikit-Learn

Scikit-Learn Basics for the following models:

• KNeighborsClassifier
• LinearRegression
• LogisticRegression
• DecisionTreeClassifier
• ElasticNet

alongwith Pipeline Components like:

• Hyper-Parameter Tuning and Cross Validation: RandomizedSearchCV, GridSearchCV
• Data PreProcessors: Imputer, StandardScaler
• Loss and Metrics: LogLoss, MSE, F1-Score, etc.

3. Unsupervised Learning in Python

Scikit-Learn Models for Unsupervised Learning:

• K-Means Clustering
• Heirarchial Clustering with Linkage and Dendograms
• tSNE Scatter Plots
• Pearson's Correlation and PCA
• TruncatedSVD and Non-Negative Matrix Factorization (NMF)

4. Linear Classifiers in Python

Based on the Large Movie Reviews Dataset (http://ai.stanford.edu/~amaas/data/sentiment/), it shows a comparison between the Scikit-Learn's Linear Classifiers: KNeighborsClassifier, LogisticRegression, Support Vector Machine (svm.SVC and LinearSVC) and SGDClassifier, as well as an exploration into Regularization Types, Strengths and Losses.

5. Machine Learning with Tree-Based Models in Python

Scikit-Learn Tree-Based Model Implementations (Intro):

• Voting Classifier with different base-estimators
• Bagging Classifier with Decision Trees
• Random Forest Regressor (RF Algorithm)
• AdaBoost Classifier and Gradient Boosting Regressor
• Stochastic Gradient Boosting Classifier

6. Extreme Gradient Boosting in Python

An introduction to XGB Dataset Format (DMatrix), XGBClassifier and XGBRegressor, with detail into Hyper-Parameter Tuning and Cross Validation for:

• No. of Estimators
• Max. Depth
• Learning Rate
• Column Sample by Tree (Max. Features)

7. Statistical Thinking in Python

A Practice run of Statistical Analysis Techniques in Python using:

• Histograms and Binning with SwarmPlots
• Empirical Distribution Functions and Cummulative Distribution Functions
• Probability Mass Functions and Probability Distribution Functions
• Quantiles and Percentiles with BoxPlots
• Variance, Covariance and Correlations
• Probability Theory and Bernoulli Trials
• Binomial and Poisson Distributions

All Source Code Copyrights to DataCamp and its contributors. All Dataset Copyrights to the official citings inside the Jupyter Notebooks.