Welcome, Course Introduction & overview, and Environment set-up |
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Welcome & Course Overview |
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00:07:00 |
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Set-up the Environment for the Course (lecture 1) |
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00:09:00 |
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Set-up the Environment for the Course (lecture 2) |
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00:25:00 |
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Two other options to setup environment |
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00:04:00 |
Python Essentials |
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Python data types Part 1 |
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00:21:00 |
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Python Data Types Part 2 |
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00:15:00 |
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Loops, List Comprehension, Functions, Lambda Expression, Map and Filter (Part 1) |
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00:16:00 |
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Loops, List Comprehension, Functions, Lambda Expression, Map and Filter (Part 2) |
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00:20:00 |
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Python Essentials Exercises Overview |
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00:02:00 |
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Python Essentials Exercises Solutions |
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00:22:00 |
Python for Data Analysis using NumPy |
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What is Numpy? A brief introduction and installation instructions. |
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00:03:00 |
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NumPy Essentials – NumPy arrays, built-in methods, array methods and attributes. |
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00:28:00 |
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NumPy Essentials – Indexing, slicing, broadcasting & boolean masking |
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00:26:00 |
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NumPy Essentials – Arithmetic Operations & Universal Functions |
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00:07:00 |
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NumPy Essentials Exercises Overview |
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00:02:00 |
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NumPy Essentials Exercises Solutions |
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00:25:00 |
Python for Data Analysis using Pandas |
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What is pandas? A brief introduction and installation instructions. |
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00:02:00 |
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Pandas Introduction |
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00:02:00 |
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Pandas Essentials – Pandas Data Structures – Series |
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00:20:00 |
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Pandas Essentials – Pandas Data Structures – DataFrame |
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00:30:00 |
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Pandas Essentials – Handling Missing Data |
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00:12:00 |
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Pandas Essentials – Data Wrangling – Combining, merging, joining |
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00:20:00 |
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Pandas Essentials – Groupby |
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00:10:00 |
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Pandas Essentials – Useful Methods and Operations |
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00:26:00 |
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Pandas Essentials – Project 1 (Overview) Customer Purchases Data |
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00:08:00 |
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Pandas Essentials – Project 1 (Solutions) Customer Purchases Data |
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00:31:00 |
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Pandas Essentials – Project 2 (Overview) Chicago Payroll Data |
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00:04:00 |
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Pandas Essentials – Project 2 (Solutions Part 1) Chicago Payroll Data |
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00:18:00 |
Python for Data Visualization using matplotlib |
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Matplotlib Essentials (Part 1) – Basic Plotting & Object Oriented Approach |
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00:13:00 |
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Matplotlib Essentials (Part 2) – Basic Plotting & Object Oriented Approach |
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00:22:00 |
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Matplotlib Essentials (Part 3) – Basic Plotting & Object Oriented Approach |
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00:22:00 |
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Matplotlib Essentials – Exercises Overview |
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00:06:00 |
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Matplotlib Essentials – Exercises Solutions |
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00:21:00 |
Python for Data Visualization using Seaborn |
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Seaborn – Introduction & Installation |
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00:04:00 |
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Seaborn – Distribution Plots |
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00:25:00 |
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Seaborn – Categorical Plots (Part 1) |
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00:21:00 |
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Seaborn – Categorical Plots (Part 2) |
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00:16:00 |
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Seborn-Axis Grids |
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00:25:00 |
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Seaborn – Matrix Plots |
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00:13:00 |
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Seaborn – Regression Plots |
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00:11:00 |
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Seaborn – Controlling Figure Aesthetics |
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00:10:00 |
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Seaborn – Exercises Overview |
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00:04:00 |
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Seaborn – Exercise Solutions |
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00:19:00 |
Python for Data Visualization using pandas |
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Pandas Built-in Data Visualization |
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00:34:00 |
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Pandas Data Visualization Exercises Overview |
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00:03:00 |
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Panda Data Visualization Exercises Solutions |
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00:13:00 |
Python for interactive & geographical plotting using Plotly and Cufflinks |
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Plotly & Cufflinks – Interactive & Geographical Plotting (Part 1) |
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00:19:00 |
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Plotly & Cufflinks – Interactive & Geographical Plotting (Part 2) |
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00:14:00 |
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Plotly & Cufflinks – Interactive & Geographical Plotting Exercises (Overview) |
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00:11:00 |
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Plotly & Cufflinks – Interactive & Geographical Plotting Exercises (Solutions) |
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00:37:00 |
Capstone Project - Python for Data Analysis & Visualization |
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Project 1 – Oil vs Banks Stock Price during recession (Overview) |
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00:15:00 |
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Project 1 – Oil vs Banks Stock Price during recession (Solutions Part 1) |
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00:18:00 |
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Project 1 – Oil vs Banks Stock Price during recession (Solutions Part 2) |
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00:18:00 |
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Project 1 – Oil vs Banks Stock Price during recession (Solutions Part 3) |
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00:17:00 |
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Project 2 (Optional) – Emergency Calls from Montgomery County, PA (Overview) |
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00:03:00 |
Python for Machine Learning (ML) - scikit-learn - Linear Regression Model |
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Introduction to ML – What, Why and Types….. |
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00:15:00 |
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Theory Lecture on Linear Regression Model, No Free Lunch, Bias Variance Tradeoff |
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00:15:00 |
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scikit-learn – Linear Regression Model – Hands-on (Part 1) |
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00:17:00 |
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scikit-learn – Linear Regression Model Hands-on (Part 2) |
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00:19:00 |
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Good to know! How to save and load your trained Machine Learning Model! |
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00:01:00 |
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scikit-learn – Linear Regression Model (Insurance Data Project Overview) |
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00:08:00 |
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scikit-learn – Linear Regression Model (Insurance Data Project Solutions) |
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00:30:00 |
Python for Machine Learning - scikit-learn - Logistic Regression Model |
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Theory: Logistic Regression, conf. mat., TP, TN, Accuracy, Specificity…etc. |
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00:10:00 |
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scikit-learn – Logistic Regression Model – Hands-on (Part 1) |
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00:17:00 |
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scikit-learn – Logistic Regression Model – Hands-on (Part 2) |
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00:20:00 |
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scikit-learn – Logistic Regression Model – Hands-on (Part 3) |
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00:11:00 |
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scikit-learn – Logistic Regression Model – Hands-on (Project Overview) |
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00:05:00 |
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scikit-learn – Logistic Regression Model – Hands-on (Project Solutions) |
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00:15:00 |
Python for Machine Learning - scikit-learn - K Nearest Neighbors |
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Theory: K Nearest Neighbors, Curse of dimensionality …. |
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00:08:00 |
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scikit-learn – K Nearest Neighbors – Hands-on |
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00:25:00 |
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scikt-learn – K Nearest Neighbors (Project Overview) |
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00:04:00 |
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scikit-learn – K Nearest Neighbors (Project Solutions) |
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00:14:00 |
Python for Machine Learning - scikit-learn - Decision Tree and Random Forests |
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Theory: D-Tree & Random Forests, splitting, Entropy, IG, Bootstrap, Bagging…. |
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00:18:00 |
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scikit-learn – Decision Tree and Random Forests – Hands-on (Part 1) |
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00:19:00 |
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scikit-learn – Decision Tree and Random Forests (Project Overview) |
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00:05:00 |
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scikit-learn – Decision Tree and Random Forests (Project Solutions) |
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00:15:00 |
Python for Machine Learning - scikit-learn -Support Vector Machines (SVMs) |
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Support Vector Machines (SVMs) – (Theory Lecture) |
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00:07:00 |
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scikit-learn – Support Vector Machines – Hands-on (SVMs) |
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00:30:00 |
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scikit-learn – Support Vector Machines (Project 1 Overview) |
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00:07:00 |
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scikit-learn – Support Vector Machines (Project 1 Solutions) |
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00:20:00 |
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scikit-learn – Support Vector Machines (Optional Project 2 – Overview) |
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00:02:00 |
Python for Machine Learning - scikit-learn - K Means Clustering |
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Theory: K Means Clustering, Elbow method ….. |
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00:11:00 |
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scikit-learn – K Means Clustering – Hands-on |
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00:23:00 |
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scikit-learn – K Means Clustering (Project Overview) |
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00:07:00 |
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scikit-learn – K Means Clustering (Project Solutions) |
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00:22:00 |
Python for Machine Learning - scikit-learn - Principal Component Analysis (PCA) |
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Theory: Principal Component Analysis (PCA) |
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00:09:00 |
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scikit-learn – Principal Component Analysis (PCA) – Hands-on |
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00:22:00 |
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scikit-learn – Principal Component Analysis (PCA) – (Project Overview) |
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00:02:00 |
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scikit-learn – Principal Component Analysis (PCA) – (Project Solutions) |
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00:17:00 |
Recommender Systems with Python - (Additional Topic) |
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Theory: Recommender Systems their Types and Importance |
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00:06:00 |
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Python for Recommender Systems – Hands-on (Part 1) |
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00:18:00 |
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Python for Recommender Systems – – Hands-on (Part 2) |
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00:19:00 |
Python for Natural Language Processing (NLP) - NLTK - (Additional Topic) |
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Natural Language Processing (NLP) – (Theory Lecture) |
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00:13:00 |
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NLTK – NLP-Challenges, Data Sources, Data Processing ….. |
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00:13:00 |
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NLTK – Feature Engineering and Text Preprocessing in Natural Language Processing |
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00:19:00 |
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NLTK – NLP – Tokenization, Text Normalization, Vectorization, BoW…. |
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00:19:00 |
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NLTK – BoW, TF-IDF, Machine Learning, Training & Evaluation, Naive Bayes … |
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00:13:00 |
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NLTK – NLP – Pipeline feature to assemble several steps for cross-validation… |
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00:09:00 |
Resources |
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Resources – Data Science and Machine Learning using Python – A Bootcamp |
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00:00:00 |
Order Your Certificate |
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Order Your Certificate Now |
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00:00:00 |