Introduction to Machine Learning and Python Data Science - Total Overview
01
Skill level
All levels
02
Sections
13
03
Lectures
74
04
Instructor
Team Mammoth
What's inside
This course includes.
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13
Sections
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Certificate of completion
Included
✓
Mobile and desktop access
Included
✓
AI learning assistance
Included
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Course content
Curriculum & lectures.
+ Course Overview 2 lectures Preview
00 Course Overview
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Source Files 00
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+ 00 Python Introduction 2 lectures
00. Intro
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01. Introduction To Python
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+ 01 Code Python on the Web 3 lectures
01.01 What Is Google Colab
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01.02 What If I Get Errors
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01.03 How Do I Terminate A Session
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+ 02 Python Language Fundamentals 19 lectures
02. Variables
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02b. Variables Examples
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03. Type Conversion Examples
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04. Operators
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05. Operators Examples
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06. Collections
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07. Lists
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08. Multidimensional List Examples
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09. Tuples Examples
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10. Dictionaries Examples
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11. Ranges Examples
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12. Conditionals
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13. If Statement Examples
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14. If Statement Variants Examples
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15. Loops
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16. While Loops Examples
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17. For Loops Examples
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18. Functions
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19. Functions Examples
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+ (Prerequisite) Introduction to Machine Learning 8 lectures
00A What Is Machine Learning
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00B Types Of Machine Learning Models
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00C What Is Supervised Learning
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01 How Does A Machine Learning Agent Learn
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02 What Is Inductive Learning
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03 Performance Of A Machine Learning Algorithm
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04 Handle Noise In Data
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05 Powerful Tools With Machine Learning Libraries-
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+ 03 Probability and Statistics for Machine Learning 4 lectures
01 Probability And Information Theory Overview
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02 Combinatorics For Probability
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03 Law Of Large Numbers
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04 Calculate Center Of Distribution
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+ 04 Distributions in Machine Learning 9 lectures
01 Uniform Distribution
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02 Gaussian Distribution
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03 Log-Normal Distribution
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04 Exponential Distribution
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05 Laplace Distribution
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06 Binomial Distribution
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07 Multinomial Distribution
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08 Poisson Distribution
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Distributions Source Files
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+ 05 Machine Learning Optimization 2 lectures
01 Calculate Error Of Machine Learning Model
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+ Data Engineering and Machine Learning Masterclass -00 Course Overview 2 lectures
00b-00 Course Overview
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+ 01 Load, clean and encode data 5 lectures
01A Load And Clean A Public Dataset
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01B What Is One-Hot Encoding
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02 Build X And Y Data With One Hot Encoding
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03 Logistic Regression With One Hot Encoding
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+ 02 Data engineering for machine learning 4 lectures
01 Scale And Encode Data With Scikit-Learn
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02 What Is Scaling Data
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03 Build, Train And Test A Machine Learning Model
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+ 03 Build regression and discretizer models 7 lectures
01 Compare Decision Tree And Linear Regression Models
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02 What Is The Kbins Discretizer
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03 Bin Data With Kbins Discretizer
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04 Build A Linear Regression Model On Stacked Data
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05 What Is K Means Clustering
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06 Compare Binned Regression Models
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+ 04 Data transformation and feature selection for ridge regression 7 lectures
01 Build Univariate Nonlinear Transformatio
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02 What Is Gaussian Probability Distribution-
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03 What Is Poisson Distribution
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04 Build X Y Data With Poisson Distribution In Numpy
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05 What Is Logarithmic Data Transformation
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06 Build A Ridge Regression Model
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Instructors
Taught by people who ship.
Team Mammoth
Instructor
Produced by a team of Mammoth Club industry experts. Over 14 years, Mammoth Club has built a global student community in 190+ countries with 9+ million courses sold, releasing over 1,000+ courses and 5,000+ hours of video content.
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