Welcome, Course Introduction & overview, and Environment setup 

Welcome & Course Overview 

00:07:00 

Setup the Environment for the Course (lecture 1) 

00:09:00 

Setup the Environment for the Course (lecture 2) 

00:25:00 

Two other options to setup environment 

00:04:00 
Python Essentials 

Python data types Part 1 

00:21:00 

Python Data Types Part 2 

00:15:00 

Loops, List Comprehension, Functions, Lambda Expression, Map and Filter (Part 1) 

00:16:00 

Loops, List Comprehension, Functions, Lambda Expression, Map and Filter (Part 2) 

00:20:00 

Python Essentials Exercises Overview 

00:02:00 

Python Essentials Exercises Solutions 

00:22:00 
Python for Data Analysis using NumPy 

What is Numpy? A brief introduction and installation instructions. 

00:03:00 

NumPy Essentials – NumPy arrays, builtin methods, array methods and attributes. 

00:28:00 

NumPy Essentials – Indexing, slicing, broadcasting & boolean masking 

00:26:00 

NumPy Essentials – Arithmetic Operations & Universal Functions 

00:07:00 

NumPy Essentials Exercises Overview 

00:02:00 

NumPy Essentials Exercises Solutions 

00:25:00 
Python for Data Analysis using Pandas 

What is pandas? A brief introduction and installation instructions. 

00:02:00 

Pandas Introduction 

00:02:00 

Pandas Essentials – Pandas Data Structures – Series 

00:20:00 

Pandas Essentials – Pandas Data Structures – DataFrame 

00:30:00 

Pandas Essentials – Handling Missing Data 

00:12:00 

Pandas Essentials – Data Wrangling – Combining, merging, joining 

00:20:00 

Pandas Essentials – Groupby 

00:10:00 

Pandas Essentials – Useful Methods and Operations 

00:26:00 

Pandas Essentials – Project 1 (Overview) Customer Purchases Data 

00:08:00 

Pandas Essentials – Project 1 (Solutions) Customer Purchases Data 

00:31:00 

Pandas Essentials – Project 2 (Overview) Chicago Payroll Data 

00:04:00 

Pandas Essentials – Project 2 (Solutions Part 1) Chicago Payroll Data 

00:18:00 
Python for Data Visualization using matplotlib 

Matplotlib Essentials (Part 1) – Basic Plotting & Object Oriented Approach 

00:13:00 

Matplotlib Essentials (Part 2) – Basic Plotting & Object Oriented Approach 

00:22:00 

Matplotlib Essentials (Part 3) – Basic Plotting & Object Oriented Approach 

00:22:00 

Matplotlib Essentials – Exercises Overview 

00:06:00 

Matplotlib Essentials – Exercises Solutions 

00:21:00 
Python for Data Visualization using Seaborn 

Seaborn – Introduction & Installation 

00:04:00 

Seaborn – Distribution Plots 

00:25:00 

Seaborn – Categorical Plots (Part 1) 

00:21:00 

Seaborn – Categorical Plots (Part 2) 

00:16:00 

SebornAxis Grids 

00:25:00 

Seaborn – Matrix Plots 

00:13:00 

Seaborn – Regression Plots 

00:11:00 

Seaborn – Controlling Figure Aesthetics 

00:10:00 

Seaborn – Exercises Overview 

00:04:00 

Seaborn – Exercise Solutions 

00:19:00 
Python for Data Visualization using pandas 

Pandas Builtin Data Visualization 

00:34:00 

Pandas Data Visualization Exercises Overview 

00:03:00 

Panda Data Visualization Exercises Solutions 

00:13:00 
Python for interactive & geographical plotting using Plotly and Cufflinks 

Plotly & Cufflinks – Interactive & Geographical Plotting (Part 1) 

00:19:00 

Plotly & Cufflinks – Interactive & Geographical Plotting (Part 2) 

00:14:00 

Plotly & Cufflinks – Interactive & Geographical Plotting Exercises (Overview) 

00:11:00 

Plotly & Cufflinks – Interactive & Geographical Plotting Exercises (Solutions) 

00:37:00 
Capstone Project  Python for Data Analysis & Visualization 

Project 1 – Oil vs Banks Stock Price during recession (Overview) 

00:15:00 

Project 1 – Oil vs Banks Stock Price during recession (Solutions Part 1) 

00:18:00 

Project 1 – Oil vs Banks Stock Price during recession (Solutions Part 2) 

00:18:00 

Project 1 – Oil vs Banks Stock Price during recession (Solutions Part 3) 

00:17:00 

Project 2 (Optional) – Emergency Calls from Montgomery County, PA (Overview) 

00:03:00 
Python for Machine Learning (ML)  scikitlearn  Linear Regression Model 

Introduction to ML – What, Why and Types….. 

00:15:00 

Theory Lecture on Linear Regression Model, No Free Lunch, Bias Variance Tradeoff 

00:15:00 

scikitlearn – Linear Regression Model – Handson (Part 1) 

00:17:00 

scikitlearn – Linear Regression Model Handson (Part 2) 

00:19:00 

Good to know! How to save and load your trained Machine Learning Model! 

00:01:00 

scikitlearn – Linear Regression Model (Insurance Data Project Overview) 

00:08:00 

scikitlearn – Linear Regression Model (Insurance Data Project Solutions) 

00:30:00 
Python for Machine Learning  scikitlearn  Logistic Regression Model 

Theory: Logistic Regression, conf. mat., TP, TN, Accuracy, Specificity…etc. 

00:10:00 

scikitlearn – Logistic Regression Model – Handson (Part 1) 

00:17:00 

scikitlearn – Logistic Regression Model – Handson (Part 2) 

00:20:00 

scikitlearn – Logistic Regression Model – Handson (Part 3) 

00:11:00 

scikitlearn – Logistic Regression Model – Handson (Project Overview) 

00:05:00 

scikitlearn – Logistic Regression Model – Handson (Project Solutions) 

00:15:00 
Python for Machine Learning  scikitlearn  K Nearest Neighbors 

Theory: K Nearest Neighbors, Curse of dimensionality …. 

00:08:00 

scikitlearn – K Nearest Neighbors – Handson 

00:25:00 

sciktlearn – K Nearest Neighbors (Project Overview) 

00:04:00 

scikitlearn – K Nearest Neighbors (Project Solutions) 

00:14:00 
Python for Machine Learning  scikitlearn  Decision Tree and Random Forests 

Theory: DTree & Random Forests, splitting, Entropy, IG, Bootstrap, Bagging…. 

00:18:00 

scikitlearn – Decision Tree and Random Forests – Handson (Part 1) 

00:19:00 

scikitlearn – Decision Tree and Random Forests (Project Overview) 

00:05:00 

scikitlearn – Decision Tree and Random Forests (Project Solutions) 

00:15:00 
Python for Machine Learning  scikitlearn Support Vector Machines (SVMs) 

Support Vector Machines (SVMs) – (Theory Lecture) 

00:07:00 

scikitlearn – Support Vector Machines – Handson (SVMs) 

00:30:00 

scikitlearn – Support Vector Machines (Project 1 Overview) 

00:07:00 

scikitlearn – Support Vector Machines (Project 1 Solutions) 

00:20:00 

scikitlearn – Support Vector Machines (Optional Project 2 – Overview) 

00:02:00 
Python for Machine Learning  scikitlearn  K Means Clustering 

Theory: K Means Clustering, Elbow method ….. 

00:11:00 

scikitlearn – K Means Clustering – Handson 

00:23:00 

scikitlearn – K Means Clustering (Project Overview) 

00:07:00 

scikitlearn – K Means Clustering (Project Solutions) 

00:22:00 
Python for Machine Learning  scikitlearn  Principal Component Analysis (PCA) 

Theory: Principal Component Analysis (PCA) 

00:09:00 

scikitlearn – Principal Component Analysis (PCA) – Handson 

00:22:00 

scikitlearn – Principal Component Analysis (PCA) – (Project Overview) 

00:02:00 

scikitlearn – Principal Component Analysis (PCA) – (Project Solutions) 

00:17:00 
Recommender Systems with Python  (Additional Topic) 

Theory: Recommender Systems their Types and Importance 

00:06:00 

Python for Recommender Systems – Handson (Part 1) 

00:18:00 

Python for Recommender Systems – – Handson (Part 2) 

00:19:00 
Python for Natural Language Processing (NLP)  NLTK  (Additional Topic) 

Natural Language Processing (NLP) – (Theory Lecture) 

00:13:00 

NLTK – NLPChallenges, Data Sources, Data Processing ….. 

00:13:00 

NLTK – Feature Engineering and Text Preprocessing in Natural Language Processing 

00:19:00 

NLTK – NLP – Tokenization, Text Normalization, Vectorization, BoW…. 

00:19:00 

NLTK – BoW, TFIDF, Machine Learning, Training & Evaluation, Naive Bayes … 

00:13:00 

NLTK – NLP – Pipeline feature to assemble several steps for crossvalidation… 

00:09:00 
Resources 

Resources – Data Science and Visualisation with Machine Learning 

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