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### Course Curriculum

Welcome, Course Introduction & overview, and Environment set-up | |||

Welcome & Course Overview | 00:07:00 | ||

Set-up the Environment for the Course (lecture 1) | 00:09:00 | ||

Set-up 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, built-in 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 | ||

Seborn-Axis 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 Built-in 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) - scikit-learn - 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 | ||

scikit-learn – Linear Regression Model – Hands-on (Part 1) | 00:17:00 | ||

scikit-learn – Linear Regression Model Hands-on (Part 2) | 00:19:00 | ||

Good to know! How to save and load your trained Machine Learning Model! | 00:01:00 | ||

scikit-learn – Linear Regression Model (Insurance Data Project Overview) | 00:08:00 | ||

scikit-learn – Linear Regression Model (Insurance Data Project Solutions) | 00:30:00 | ||

Python for Machine Learning - scikit-learn - Logistic Regression Model | |||

Theory: Logistic Regression, conf. mat., TP, TN, Accuracy, Specificity…etc. | 00:10:00 | ||

scikit-learn – Logistic Regression Model – Hands-on (Part 1) | 00:17:00 | ||

scikit-learn – Logistic Regression Model – Hands-on (Part 2) | 00:20:00 | ||

scikit-learn – Logistic Regression Model – Hands-on (Part 3) | 00:11:00 | ||

scikit-learn – Logistic Regression Model – Hands-on (Project Overview) | 00:05:00 | ||

scikit-learn – Logistic Regression Model – Hands-on (Project Solutions) | 00:15:00 | ||

Python for Machine Learning - scikit-learn - K Nearest Neighbors | |||

Theory: K Nearest Neighbors, Curse of dimensionality …. | 00:08:00 | ||

scikit-learn – K Nearest Neighbors – Hands-on | 00:25:00 | ||

scikt-learn – K Nearest Neighbors (Project Overview) | 00:04:00 | ||

scikit-learn – K Nearest Neighbors (Project Solutions) | 00:14:00 | ||

Python for Machine Learning - scikit-learn - Decision Tree and Random Forests | |||

Theory: D-Tree & Random Forests, splitting, Entropy, IG, Bootstrap, Bagging…. | 00:18:00 | ||

scikit-learn – Decision Tree and Random Forests – Hands-on (Part 1) | 00:19:00 | ||

scikit-learn – Decision Tree and Random Forests (Project Overview) | 00:05:00 | ||

scikit-learn – Decision Tree and Random Forests (Project Solutions) | 00:15:00 | ||

Python for Machine Learning - scikit-learn -Support Vector Machines (SVMs) | |||

Support Vector Machines (SVMs) – (Theory Lecture) | 00:07:00 | ||

scikit-learn – Support Vector Machines – Hands-on (SVMs) | 00:30:00 | ||

scikit-learn – Support Vector Machines (Project 1 Overview) | 00:07:00 | ||

scikit-learn – Support Vector Machines (Project 1 Solutions) | 00:20:00 | ||

scikit-learn – Support Vector Machines (Optional Project 2 – Overview) | 00:02:00 | ||

Python for Machine Learning - scikit-learn - K Means Clustering | |||

Theory: K Means Clustering, Elbow method ….. | 00:11:00 | ||

scikit-learn – K Means Clustering – Hands-on | 00:23:00 | ||

scikit-learn – K Means Clustering (Project Overview) | 00:07:00 | ||

scikit-learn – K Means Clustering (Project Solutions) | 00:22:00 | ||

Python for Machine Learning - scikit-learn - Principal Component Analysis (PCA) | |||

Theory: Principal Component Analysis (PCA) | 00:09:00 | ||

scikit-learn – Principal Component Analysis (PCA) – Hands-on | 00:22:00 | ||

scikit-learn – Principal Component Analysis (PCA) – (Project Overview) | 00:02:00 | ||

scikit-learn – 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 – Hands-on (Part 1) | 00:18:00 | ||

Python for Recommender Systems – – Hands-on (Part 2) | 00:19:00 | ||

Python for Natural Language Processing (NLP) - NLTK - (Additional Topic) | |||

Natural Language Processing (NLP) – (Theory Lecture) | 00:13:00 | ||

NLTK – NLP-Challenges, 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, TF-IDF, Machine Learning, Training & Evaluation, Naive Bayes … | 00:13:00 | ||

NLTK – NLP – Pipeline feature to assemble several steps for cross-validation… | 00:09:00 | ||

Certificate | |||

Order Your Certificate. | 00:00:00 |

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