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On-Demand course
8 minutes
All levels
19 sections • 99 lectures • 00:08:00 total length
•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 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
•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
•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
•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
•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
•Pandas Built-in Data Visualization: 00:34:00
•Pandas Data Visualization Exercises Overview: 00:03:00
•Panda Data Visualization Exercises Solutions: 00:13:00
•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
•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
•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
•Theory: Logistic Regression, conf. mat., TP, TN, Accuracy, Specificityetc.: 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
•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
•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
•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
•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
•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
•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
•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
•Resources- Python for Data Analysis: 00:00:00
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