Complete Python Machine Learning And Data Science for Dummies

Machine Learning and Data Science for programming beginners using python with scikit-learn, SciPy, Matplotlib and Pandas

  • (5.0)

Course Overview

Artificial Intelligence, Machine Learning and Deep Learning Neural Networks are the most used terms nowadays in the technology world. It is also the most misunderstood and confusing terms too.

Artificial Intelligence is a broad spectrum of science which tries to make machines intelligent like humans. Machine Learning and Neural Networks are two subsets that come under this vast machine learning platform

Let us check what's machine learning now. Just like we human babies, we were actually in our learning phase then. We learned how to crawl, stand, walk, then speak words, then make simple sentences. We learned from our experiences. We had many trials and errors before we learned how to walk and talk. The best trails for walking and talking which gave positive results were kept in our memory and made use later. This process is higher compared to a Machine Learning Mechanism

Then we grew young and started thinking logically about many things, had emotional feelings, etc. We kept on thinking and found solutions to problems in our daily life. That's what the Deep Learning Neural Network Scientists are trying to achieve. A thinking machine.

But in this course, we are focusing mainly on Machine Learning. Throughout this course, we are preparing our machine to make it ready for a prediction test. Just like how you prepare for your Mathematics Test in school or college. We learn and train ourselves by solving the most possible number of similar mathematical problems. Let us call these sample data of similar problems and their solutions as the 'Training Input' and 'Training Output' Respectively. And then the day comes when we have the actual test. We will be given a new set of problems to solve, but very similar to the problems we learned, and based on the previous practice and learning experiences, we have to solve them. We can call those problems as 'Testing Input' and our answers as 'Predicted Output'. Later, our professor will evaluate these answers and compare it with its actual answers, we call the actual answers as 'Test Output'. Then a mark will be given based on the correct answers. We call this mark as our 'Accuracy'. The life of a machine learning engineer and a data scientist is dedicated to making this accuracy as good as possible through different techniques and evaluation measures.

Here are the major topics that are included in this course. We are using Python as our programming language. Python is a great tool for the development of programs which perform data analysis and prediction. It has tons of classes and features which perform the complex mathematical analysis and give solutions in simple one or two lines of code so that we don't have to be a statistic genius or mathematical Nerd to learn data science and machine learning. Python makes things easy.

These are the main topics that are included in our course

System and Environment preparation

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Installing Python and Required Libraries (Anaconda)

Basics of python and sci-py

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Python, Numpy, Matplotlib and Pandas Quick Courses

Load data set from CSV / URL

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Load CSV data with Python, NumPy and Pandas

Summarize data with description

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Peeking data, Data Dimensions, Data Types, Statistics, Class Distribution, Attribute Correlations, Univariate Skew

Summarize data with visualization

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Univariate, Multivariate Plots

Prepare data

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Data Transforms, Rescaling, Standardizing, Normalizing and Binarization

Feature selection – Automatic selection techniques

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Univariate Selection, Recursive Feature Elimination, Principle Component Analysis and Feature Importance

Machine Learning Algorithm Evaluation

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Train and Test Sets, K-fold Cross-Validation, Leave One Out Cross Validation, Repeated Random Test-Train Splits.

Algorithm Evaluation Metrics

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Classification Metrics - Classification Accuracy, Logarithmic Loss, Area Under ROC Curve, Confusion Matrix, Classification Report.

Regression Metrics - Mean Absolute Error, Mean Squared Error, R 2.

Spot-Checking Classification Algorithms

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Linear Algorithms -  Logistic Regression, Linear Discriminant Analysis.

Non-Linear Algorithms - k-Nearest Neighbours, Naive Bayes, Classification and Regression Trees, Support Vector Machines.

Spot-Checking Regression Algorithms

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Linear Algorithms -   Linear Regression, Ridge Regression, LASSO Linear Regression and Elastic Net Regression.

Non-Linear Algorithms - k-Nearest Neighbours, Classification and Regression Trees, Support Vector Machines.

Choose The Best Machine Learning Model

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Compare Logistic Regression, Linear Discriminant Analysis, k-Nearest Neighbours, Classification and Regression Trees, Naive Bayes, Support Vector Machines.

Automate and Combine Workflows with Pipeline

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Data Preparation and Modelling Pipeline

Feature Extraction and Modelling Pipeline

Performance Improvement with Ensembles

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Voting Ensemble

Bagging: Bagged Decision Trees, Random Forest, Extra Trees

Boosting: AdaBoost, Gradient Boosting

Performance Improvement with Algorithm Parameter Tuning

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Grid Search Parameter

Random Search Parameter Tuning

Save and Load (serialize and deserialize) Machine Learning Models

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Using pickle

Using Joblib

finalize a machine learning project

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steps For Finalizing classification models - Pima Indian dataset

Dealing with an imbalanced class problem

steps For Finalizing multi-class models - iris flower dataset

steps For Finalizing regression models - Boston housing dataset

Predictions and Case Studies

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Case study 1: predictions using the Pima Indian Diabetes Dataset

Case study: Iris Flower Multi-Class Dataset

Case study 2: the Boston Housing cost Dataset

Machine Learning and Data Science is the most lucrative job in the technology arena nowadays. Learning this course will make you equipped to compete in this area.

What are the requirements?

  • A medium configuration computer and the willingness to indulge in the world of Machine Learning

What am I going to get from this course?

  • Machine Learning and Data Science using Python for Beginners

What is the target audience?

  • Beginners who are interested in Machine Learning using Python

About the Author

I  am a pioneering, talented and security-oriented Android/iOS Mobile and PHP/Python Web Developer Application Developer offering more than eight years’ overall IT experience which involves designing, implementing, integrating, testing and supporting impact-full web and mobile applications. I am a Post Graduate Masters Degree holder in Computer Science and Engineering. My experience with PHP/Python Programming is an added advantage for server based Android and iOS Client Applications. I am currently serving full time as a Senior Solution Architect managing my client's projects from start to finish to ensure high quality, innovative and functional design.

Course Curriculum

Course Overview and Table of Contents
1 Video Lectures | 00:09:08

  • Course Overview and table of contents
    09:08
     

Introduction to Machine Learning - Part 1 - Concepts , Definitions and Types
1 Video Lectures | 00:04:37

  • Introduction to Machine Learning - Part 1 - Concepts , Definitions and Types
    04:37
     

Introduction to Machine Learning - Part 2 - Classifications and Applications
1 Video Lectures | 00:05:55

  • Introduction to Machine Learning - Part 2 - Classifications and Applications
    05:55
     

System and Environment preparation - Part 1
1 Video Lectures | 00:08:21

  • System and Environment preparation - Part 1
    08:21
     

System and Environment preparation - Part 2
1 Video Lectures | 00:05:45

  • System and Environment preparation - Part 2
    05:45
     

Learn Basics of python - Assignment
1 Video Lectures | 00:09:41

  • Learn Basics of python - Assignment
    09:41
     

Learn Basics of python - Flow Control
1 Video Lectures | 00:09:25

  • Learn Basics of python - Flow Control
    09:25
     

Learn Basics of python - Functions
1 Video Lectures | 00:03:58

  • Learn Basics of python - Functions
    03:58
     

Learn Basics of python - Data Structures
1 Video Lectures | 00:12:13

  • Learn Basics of python - Data Structures
    12:13
     

Learn Basics of NumPy - NumPy Array
1 Video Lectures | 00:05:57

  • Learn Basics of NumPy - NumPy Array
    05:57
     

Learn Basics of NumPy - NumPy Data
1 Video Lectures | 00:08:09

  • Learn Basics of NumPy - NumPy Data
    08:09
     

Learn Basics of NumPy - NumPy Arithmetic
1 Video Lectures | 00:04:12

  • Learn Basics of NumPy - NumPy Arithmetic
    04:12
     

Learn Basics of Matplotlib
1 Video Lectures | 00:07:06

  • Learn Basics of Matplotlib
    07:06
     

Learn Basics of Pandas - Part 1
1 Video Lectures | 00:05:36

  • Learn Basics of Pandas - Part 1
    05:36
     

Learn Basics of Pandas - Part 2
1 Video Lectures | 00:07:12

  • Learn Basics of Pandas - Part 2
    07:12
     

Understanding the CSV data file
1 Video Lectures | 00:08:56

  • Understanding the CSV data file
    08:56
     

Load and Read CSV data file using Python Standard Library
1 Video Lectures | 00:08:59

  • Load and Read CSV data file using Python Standard Library
    08:59
     

Load and Read CSV data file using NumPy
1 Video Lectures | 00:03:50

  • Load and Read CSV data file using NumPy
    03:50
     

Load and Read CSV data file using Pandas
1 Video Lectures | 00:05:20

  • Load and Read CSV data file using Pandas
    05:20
     

Dataset Summary - Peek, Dimensions and Data Types
1 Video Lectures | 00:09:28

  • Dataset Summary - Peek, Dimensions and Data Types
    09:28
     

Dataset Summary - Class Distribution and Data Summary
1 Video Lectures | 00:08:52

  • Dataset Summary - Class Distribution and Data Summary
    08:52
     

Dataset Summary - Explaining Correlation
1 Video Lectures | 00:10:51

  • Dataset Summary - Explaining Correlation
    10:51
     

Dataset Summary - Explaining Skewness - Gaussian and Normal Curve
1 Video Lectures | 00:06:35

  • Dataset Summary - Explaining Skewness - Gaussian and Normal Curve
    06:35
     

Dataset Visualization - Using Histograms
1 Video Lectures | 00:06:43

  • Dataset Visualization - Using Histograms
    06:43
     

Dataset Visualization - Using Density Plots
1 Video Lectures | 00:05:37

  • Dataset Visualization - Using Density Plots
    05:37
     

Dataset Visualization - Box and Whisker Plots
1 Video Lectures | 00:05:00

  • Dataset Visualization - Box and Whisker Plots
    05:00
     

Multivariate Dataset Visualization - Correlation Plots
1 Video Lectures | 00:08:09

  • Multivariate Dataset Visualization - Correlation Plots
    08:09
     

Multivariate Dataset Visualization - Scatter Plots
1 Video Lectures | 00:05:15

  • Multivariate Dataset Visualization - Scatter Plots
    05:15
     

Data Preparation (Pre-Processing) - Introduction
1 Video Lectures | 00:08:48

  • Data Preparation (Pre-Processing) - Introduction
    08:48
     

Data Preparation - Re-scaling Data - Part 1
1 Video Lectures | 00:08:31

  • Data Preparation - Re-scaling Data - Part 1
    08:31
     

Data Preparation - Re-scaling Data - Part 2
1 Video Lectures | 00:09:15

  • Data Preparation - Re-scaling Data - Part 2
    09:15
     

Data Preparation - Standardizing Data - Part 1
1 Video Lectures | 00:07:16

  • Data Preparation - Standardizing Data - Part 1
    07:16
     

Data Preparation - Standardizing Data - Part 2
1 Video Lectures | 00:03:49

  • Data Preparation - Standardizing Data - Part 2
    03:49
     

Data Preparation - Normalizing Data
1 Video Lectures | 00:08:16

  • Data Preparation - Normalizing Data
    08:16
     

Data Preparation - Binarizing Data
1 Video Lectures | 00:05:35

  • Data Preparation - Binarizing Data
    05:35
     

Feature Selection - Introduction
1 Video Lectures | 00:07:13

  • Feature Selection - Introduction
    07:13
     

Feature Selection - Uni-variate Part 1 - Chi-Squared Test
1 Video Lectures | 00:08:35

  • Feature Selection - Uni-variate Part 1 - Chi-Squared Test
    08:35
     

Feature Selection - Uni-variate Part 2 - Chi-Squared Test
1 Video Lectures | 00:10:11

  • Feature Selection - Uni-variate Part 2 - Chi-Squared Test
    10:11
     

Feature Selection - Recursive Feature Elimination
1 Video Lectures | 00:10:45

  • Feature Selection - Recursive Feature Elimination
    10:45
     

Feature Selection - Principal Component Analysis (PCA)
1 Video Lectures | 00:08:55

  • Feature Selection - Principal Component Analysis (PCA)
    08:55
     

Feature Selection - Feature Importance
1 Video Lectures | 00:06:30

  • Feature Selection - Feature Importance
    06:30
     

Refresher Session - The Mechanism of Re-sampling, Training and Testing
1 Video Lectures | 00:12:04

  • Refresher Session - The Mechanism of Re-sampling, Training and Testing
    12:04
     

Algorithm Evaluation Techniques - Introduction
1 Video Lectures | 00:07:07

  • Algorithm Evaluation Techniques - Introduction
    07:07
     

Algorithm Evaluation Techniques - Train and Test Set
1 Video Lectures | 00:11:26

  • Algorithm Evaluation Techniques - Train and Test Set
    11:26
     

Algorithm Evaluation Techniques - K-Fold Cross Validation
1 Video Lectures | 00:08:35

  • Algorithm Evaluation Techniques - K-Fold Cross Validation
    08:35
     

Algorithm Evaluation Techniques - Leave One Out Cross Validation
1 Video Lectures | 00:04:32

  • Algorithm Evaluation Techniques - Leave One Out Cross Validation
    04:32
     

Algorithm Evaluation Techniques - Repeated Random Test-Train Splits
1 Video Lectures | 00:06:48

  • Algorithm Evaluation Techniques - Repeated Random Test-Train Splits
    06:48
     

Algorithm Evaluation Metrics - Introduction
1 Video Lectures | 00:08:58

  • Algorithm Evaluation Metrics - Introduction
    08:58
     

Algorithm Evaluation Metrics - Classification Accuracy
1 Video Lectures | 00:08:03

  • Algorithm Evaluation Metrics - Classification Accuracy
    08:03
     

Algorithm Evaluation Metrics - Log Loss
1 Video Lectures | 00:03:25

  • Algorithm Evaluation Metrics - Log Loss
    03:25
     

Algorithm Evaluation Metrics - Area Under ROC Curve
1 Video Lectures | 00:06:10

  • Algorithm Evaluation Metrics - Area Under ROC Curve
    06:10
     

Algorithm Evaluation Metrics - Confusion Matrix
1 Video Lectures | 00:10:21

  • Algorithm Evaluation Metrics - Confusion Matrix
    10:21
     

Algorithm Evaluation Metrics - Classification Report
1 Video Lectures | 00:04:11

  • Algorithm Evaluation Metrics - Classification Report
    04:11
     

Algorithm Evaluation Metrics - Mean Absolute Error - Dataset Introduction
1 Video Lectures | 00:06:10

  • Algorithm Evaluation Metrics - Mean Absolute Error - Dataset Introduction
    06:10
     

Algorithm Evaluation Metrics - Mean Absolute Error
1 Video Lectures | 00:06:40

  • Algorithm Evaluation Metrics - Mean Absolute Error
    06:40
     

Algorithm Evaluation Metrics - Mean Square Error
1 Video Lectures | 00:02:51

  • Algorithm Evaluation Metrics - Mean Square Error
    02:51
     

Algorithm Evaluation Metrics - R Squared
1 Video Lectures | 00:03:51

  • Algorithm Evaluation Metrics - R Squared
    03:51
     

Classification Algorithm Spot Check - Logistic Regression
1 Video Lectures | 00:11:32

  • Classification Algorithm Spot Check - Logistic Regression
    11:32
     

Classification Algorithm Spot Check - Linear Discriminant Analysis
1 Video Lectures | 00:03:49

  • Classification Algorithm Spot Check - Linear Discriminant Analysis
    03:49
     

Classification Algorithm Spot Check - K-Nearest Neighbors
1 Video Lectures | 00:04:50

  • Classification Algorithm Spot Check - K-Nearest Neighbors
    04:50
     

Classification Algorithm Spot Check - Naive Bayes
1 Video Lectures | 00:04:01

  • Classification Algorithm Spot Check - Naive Bayes
    04:01
     

Classification Algorithm Spot Check - CART
1 Video Lectures | 00:03:49

  • Classification Algorithm Spot Check - CART
    03:49
     

Classification Algorithm Spot Check - Support Vector Machines
1 Video Lectures | 00:04:37

  • Classification Algorithm Spot Check - Support Vector Machines
    04:37
     

Regression Algorithm Spot Check - Linear Regression
1 Video Lectures | 00:07:38

  • Regression Algorithm Spot Check - Linear Regression
    07:38
     

Regression Algorithm Spot Check - Ridge Regression
1 Video Lectures | 00:03:14

  • Regression Algorithm Spot Check - Ridge Regression
    03:14
     

Regression Algorithm Spot Check - Lasso Linear Regression
1 Video Lectures | 00:02:55

  • Regression Algorithm Spot Check - Lasso Linear Regression
    02:55
     

Regression Algorithm Spot Check - Elastic Net Regression
1 Video Lectures | 00:02:10

  • Regression Algorithm Spot Check - Elastic Net Regression
    02:10
     

Regression Algorithm Spot Check - K-Nearest Neighbors
1 Video Lectures | 00:05:57

  • Regression Algorithm Spot Check - K-Nearest Neighbors
    05:57
     

Regression Algorithm Spot Check - CART
1 Video Lectures | 00:04:03

  • Regression Algorithm Spot Check - CART
    04:03
     

Regression Algorithm Spot Check - Support Vector Machines (SVM)
1 Video Lectures | 00:04:03

  • Regression Algorithm Spot Check - Support Vector Machines (SVM)
    04:03
     

Compare Algorithms - Part 1 : Choosing the best Machine Learning Model
1 Video Lectures | 00:08:56

  • Compare Algorithms - Part 1 : Choosing the best Machine Learning Model
    08:56
     

Compare Algorithms - Part 2 : Choosing the best Machine Learning Model
1 Video Lectures | 00:05:02

  • Compare Algorithms - Part 2 : Choosing the best Machine Learning Model
    05:02
     

Pipelines : Data Preparation and Data Modelling
1 Video Lectures | 00:10:56

  • Pipelines : Data Preparation and Data Modelling
    10:56
     

Pipelines : Feature Selection and Data Modelling
1 Video Lectures | 00:09:36

  • Pipelines : Feature Selection and Data Modelling
    09:36
     

Performance Improvement: Ensembles - Voting
1 Video Lectures | 00:06:58

  • Performance Improvement: Ensembles - Voting
    06:58
     

Performance Improvement: Ensembles - Bagging
1 Video Lectures | 00:08:21

  • Performance Improvement: Ensembles - Bagging
    08:21
     

Performance Improvement: Ensembles - Boosting
1 Video Lectures | 00:04:36

  • Performance Improvement: Ensembles - Boosting
    04:36
     

Performance Improvement: Parameter Tuning using Grid Search
1 Video Lectures | 00:07:36

  • Performance Improvement: Parameter Tuning using Grid Search
    07:36
     

Performance Improvement: Parameter Tuning using Random Search
1 Video Lectures | 00:06:00

  • Performance Improvement: Parameter Tuning using Random Search
    06:00
     

Export, Save and Load Machine Learning Models : Pickle
1 Video Lectures | 00:09:41

  • Export, Save and Load Machine Learning Models : Pickle
    09:41
     

Export, Save and Load Machine Learning Models : Joblib
1 Video Lectures | 00:05:53

  • Export, Save and Load Machine Learning Models : Joblib
    05:53
     

Finalizing a Model - Introduction and Steps
1 Video Lectures | 00:06:39

  • Finalizing a Model - Introduction and Steps
    06:39
     

Finalizing a Classification Model - The Pima Indian Diabetes Dataset
1 Video Lectures | 00:06:46

  • Finalizing a Classification Model - The Pima Indian Diabetes Dataset
    06:46
     

Quick Session: Imbalanced Data Set - Issue Overview and Steps
1 Video Lectures | 00:08:35

  • Quick Session: Imbalanced Data Set - Issue Overview and Steps
    08:35
     

Iris Dataset : Finalizing Multi-Class Dataset
1 Video Lectures | 00:09:16

  • Iris Dataset : Finalizing Multi-Class Dataset
    09:16
     

Finalizing a Regression Model - The Boston Housing Price Dataset
1 Video Lectures | 00:08:17

  • Finalizing a Regression Model - The Boston Housing Price Dataset
    08:17
     

Real-time Predictions: Using the Pima Indian Diabetes Classification Model
1 Video Lectures | 00:06:39

  • Real-time Predictions: Using the Pima Indian Diabetes Classification Model
    06:39
     

Real-time Predictions: Using Iris Flowers Multi-Class Classification Dataset
1 Video Lectures | 00:03:26

  • Real-time Predictions: Using Iris Flowers Multi-Class Classification Dataset
    03:26
     

Real-time Predictions: Using the Boston Housing Regression Model
1 Video Lectures | 00:08:07

  • Real-time Predictions: Using the Boston Housing Regression Model
    08:07
     

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