Statistical and Machine Learning For Regression Modelling in Python
Learn Complete Hands-On Regression Analysis for Practical Statistical Modelling and Machine Learning in R
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1
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Segment - 01 - Inroduction
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Segment - 02 - Python Data Science Environment
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Segment - 03 - For Mac Users
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Segment - 04 - Introduction to IPython
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Segment - 05 - IPython in Browser
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Segment - 06 - Python Data Science Packages To Be Used
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2
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Segment - 07 - What are Pandas?
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Segment - 08 - Read in Data from CSV
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Segment - 09 - Read in Excel Data
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Segment - 10 - Read in HTML Data
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3
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Segment - 11 - Remove Missing Values
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Segment - 12 - Conditional Data Selection
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Segment - 13 - Data Grouping
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Segment - 14 - Data Subsetting
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Segment - 15- Ranking and Sorting
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Segment - 16 - Concatenate
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Segment - 17 - Merging and Joining Data Frames
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4
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Segment - 18 - What is Statistical Data Analysis?
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Segment - 19 - Some Pointers on Collecting Data for Statistical Studies
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Segment - 20 - Explore the Quantitative Data: Descriptive Statistics
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Segment - 21 - Grouping and Summarizing Data by Categories
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Segment - 22 - Visualize Descriptive Statistics - Boxplots
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Segment - 23 - Common Terms Relating to Descriptive Statistics
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Segment - 24 - Data Distribution- Normal Distribution
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Segment - 25 - Check for Normal Distribution
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Segment - 26 - Standard Normal Distribution and Z-scores
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Segment - 28 - Confidence Interval - Calculation
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Segment - 27 - Confidence Interval - Theory
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5
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Segment - 29 - Explore the Relationship Between Two Quantitative Variables
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Segment - 30 - Correlation Analysis
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Segment - 31 - Linear Regression - Theory
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Segment - 32 - Linear Regression - Implementation in Python
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Segment - 33 - Conditions of Linear Regression
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Segment - 34 - Conditions of Linear Regression - Check in Python
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Segment - 35 - Polynomial Regression
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Segment - 36 - GLM: Generalized Linear Model
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Segment - 37 - Logistic Regression
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6
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Segment - 38 - How is Machine Learning Different from Statistical Data Analysis?
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Segment - 39 - What is Machine Learning About ?
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7
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Segment - 40 - Introduction
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Segment - 41 - Data Preparation for Supervised Learning
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Segment - 42 - Pointers on Evaluating the Accuracy of Classification and Regression Modelling
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Segment - 43 - RF - Regression
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Segment - 44 - Support Vector Regression
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Segment - 45 - Knn - Regression
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Segment - 46 - Gradient Boosting - Regression
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Segment - 47 - Theory Behind ANN and DNN
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Segment - 48 - Regression with MLP