Learn Machine Learning Skill & Data Science with Python for Dummies: The Complete Beginners Guide
Abhilash Nelson, Senior Software Engineer, Dubai


1. Course Overview & Table of Contents
9:08 
2. Introduction to Machine Learning  Part 1  Concepts , Definitions and Types
4:37 
3. Introduction to Machine Learning  Part 2  Classifications and Applications
5:54 
4. System and Environment preparation  Part 1
8:20 
5. System and Environment preparation  Part 2
5:44 
6. Learn Basics of python  Assignment
9:41 
7. Learn Basics of python  Flow Control
9:25 
8. Learn Basics of python  Functions
3:58 
9. Learn Basics of python  Data Structures
12:13 
10. Learn Basics of NumPy  NumPy Array
5:57 
11. Learn Basics of NumPy  NumPy Data
8:08 
12. Learn Basics of NumPy  NumPy Arithmetic
4:12 
13. Learn Basics of Matplotlib
7:06 
14. Learn Basics of Pandas  Part 1
5:36 
15. Learn Basics of Pandas  Part 2
7:11 
16. Understanding the CSV data file
8:55 
17. Load and Read CSV data file using Python Standard Library
8:59 
18. Load and Read CSV data file using NumPy
3:49 
19. Load and Read CSV data file using Pandas
5:20 
20. Dataset Summary  Peek, Dimensions and Data Types
9:27 
21. Dataset Summary  Class Distribution and Data Summary
8:51 
22. Dataset Summary  Explaining Correlation
10:51 
23. Dataset Summary  Explaining Skewness  Gaussian and Normal Curve
6:35 
24. Dataset Visualization  Using Histograms
6:42 
25. Dataset Visualization  Using Density Plots
5:36 
26. Dataset Visualization  Box and Whisker Plots
5:00 
27. Multivariate Dataset Visualization  Correlation Plots
8:08 
28. Multivariate Dataset Visualization  Scatter Plots
5:15 
29. Data Preparation (PreProcessing)  Introduction
8:47 
30. Data Preparation  Rescaling Data  Part 1
8:30 
31. Data Preparation  Rescaling Data  Part 2
9:15 
32. Data Preparation  Standardizing Data  Part 1
7:16 
33. Data Preparation  Standardizing Data  Part 2
3:49 
34. Data Preparation  Normalizing Data
8:15 
35. Data Preparation  Binarizing Data
5:35 
36. Feature Selection  Introduction
7:12 
37. Feature Selection  Univariate Part 1  ChiSquared Test
8:35 
38. Feature Selection  Univariate Part 2  ChiSquared Test
10:11 
39. Feature Selection  Recursive Feature Elimination
10:44 
40. Feature Selection  Principal Component Analysis (PCA)
8:55 
41. Feature Selection  Feature Importance
6:30 
42. Refresher Session  The Mechanism of Resampling, Training and Testing
12:04 
43. Algorithm Evaluation Techniques  Introduction
7:07 
44. Algorithm Evaluation Techniques  Train and Test Set
11:25 
45. Algorithm Evaluation Techniques  KFold Cross Validation
8:34 
46. Algorithm Evaluation Techniques  Leave One Out Cross Validation
4:32 
47. Algorithm Evaluation Techniques  Repeated Random TestTrain Splits
6:47 
48. Algorithm Evaluation Metrics  Introduction
8:57 
49. Algorithm Evaluation Metrics  Classification Accuracy
8:02 
50. Algorithm Evaluation Metrics  Log Loss
3:25 
51. Algorithm Evaluation Metrics  Area Under ROC Curve
6:09 
52. Algorithm Evaluation Metrics  Confusion Matrix
10:20 
53. Algorithm Evaluation Metrics  Classification Report
4:10 
54. Algorithm Evaluation Metrics  Mean Absolute Error  Dataset Introduction
6:09 
55. Algorithm Evaluation Metrics  Mean Absolute Error
6:40 
56. Algorithm Evaluation Metrics  Mean Square Error
2:50 
57. Algorithm Evaluation Metrics  R Squared
3:51 
58. Classification Algorithm Spot Check  Logistic Regression
11:31 
59. Classification Algorithm Spot Check  Linear Discriminant Analysis
3:48 
60. Classification Algorithm Spot Check  KNearest Neighbors
4:49 
61. Classification Algorithm Spot Check  Naive Bayes
4:00 
62. Classification Algorithm Spot Check  CART
3:48 
63. Classification Algorithm Spot Check  Support Vector Machines
4:36 
64. Regression Algorithm Spot Check  Linear Regression
7:38 
65. Regression Algorithm Spot Check  Ridge Regression
3:13 
66. Regression Algorithm Spot Check  LASSO Linear Regression
2:55 
67. Regression Algorithm Spot Check  Elastic Net Regression
2:09 
68. Regression Algorithm Spot Check  KNearest Neighbors
5:56 
69. Regression Algorithm Spot Check  CART
4:03 
70. Regression Algorithm Spot Check  Support Vector Machines (SVM)
4:03 
71. Compare Algorithms  Part 1 : Choosing the best Machine Learning Model
8:56 
72. Compare Algorithms  Part 2 : Choosing the best Machine Learning Model
5:01 
73. Pipelines : Data Preparation and Data Modelling
10:56 
74. Pipelines : Feature Selection and Data Modelling
9:35 
75. Performance Improvement: Ensembles  Voting
6:57 
76. Performance Improvement: Ensembles  Bagging
8:21 
77. Performance Improvement: Ensembles  Boosting
4:35 
78. Performance Improvement: Parameter Tuning using Grid Search
7:36 
79. Performance Improvement: Parameter Tuning using Random Search
5:59 
80. Export, Save and Load Machine Learning Models : Pickle
9:41 
81. Export, Save and Load Machine Learning Models : Joblib
5:52 
82. Finalizing a Model  Introduction and Steps
6:39 
83. Finalizing a Classification Model  The Pima Indian Diabetes Dataset
6:45 
84. Quick Session: Imbalanced Data Set  Issue Overview and Steps
8:35 
85. Iris Dataset : Finalizing MultiClass Dataset
9:16 
86. Finalizing a Regression Model  The Boston Housing Price Dataset
8:16 
87. Realtime Predictions: Using the Pima Indian Diabetes Classification Model
6:39 
88. Realtime Predictions: Using Iris Flowers MultiClass Classification Dataset
3:25 
89. Realtime Predictions: Using the Boston Housing Regression Model
8:06

About This Class
Hi.. Hello and welcome to my new course, Machine Learning with Python for Dummies. We will discuss about the overview of the course and the contents included in this course.
Artificial Intelligence, Machine LearningÂ and Deep Learning Neural Networks are the most used terms now a days in the technology world. Its also the most misunderstood and confused 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 comes under this vast machine learning platform
Lets 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 trials for walking and talking which gave positive results were kept in our memory and made use later. This process is highly 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 in Machine Learning. Throughout this course, we are preparing our machine to make it ready for a prediction test. Its Just like how you prepare for your Mathematics Test in school or college.Â We learn and train ourselves by solving the most possible number of similar mathematical problems. Lets 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 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 on basis of the correct answers. We call this mark as our 'Accuracy'. The life of a machine learning engineer and a datascientist is dedicated to make 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 really makes things easy.
These are the main topics that are included in our course
System and Environment preparation

Installing Python and Required Libraries (Anaconda)
Basics of python and scipy

Python, Numpy , Matplotlib and Pandas Quick Courses
Load data set from csv / url

Load CSV data with Python, NumPY and Pandas
Summarize data with description

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

Univariate, Multivariate Plots
Prepare data

Data Transforms, Rescaling, Standardizing, Normalizing and Binarization
Feature selection â€“ Automatic selection techniques

Univariate Selection, Recursive Feature Elimination, Principle Component Analysis and Feature Importance
Machine Learning Algorithm Evaluation

Train and Test Sets, Kfold Cross Validation, Leave One Out Cross Validation, Repeated Random TestTrain Splits.
Algorithm Evaluation Metrics

Classification Metrics  Classification Accuracy, Logarithmic Loss, Area Under ROC Curve, Confusion Matrix, Classification Report.
Regression Metrics  Mean Absolute Error, Mean Squared Error, R 2.
SpotChecking Classification Algorithms

Linear Algorithms Â Logistic Regression, Linear Discriminant Analysis.
NonLinear Algorithms  kNearest Neighbours, Naive Bayes, Classification and Regression Trees, Support Vector Machines.
SpotChecking Regression Algorithms

Linear Algorithms Â Â Linear Regression, Ridge Regression, LASSO Linear Regression and Elastic Net Regression.
NonLinear Algorithms  kNearest Neighbours, Classification and Regression Trees, Support Vector Machines.
Choose The Best Machine Learning Model

Compare Logistic Regression, Linear Discriminant Analysis, kNearest Neighbours, Classification and Regression Trees, Naive Bayes, Support Vector Machines.
Automate and Combine Workflows with Pipeline

Data Preparation and Modelling Pipeline
Feature Extraction and Modelling Pipeline
Performance Improvement with Ensembles

Voting Ensemble
Bagging: Bagged Decision Trees, Random Forest, Extra Trees
Boosting: AdaBoost, Gradient Boosting
Performance Improvement with Algorithm Parameter Tuning

Grid Search Parameter
Random Search Parameter Tuning
Save and Load (serialize and deserialize) Machine Learning Models

Using pickle
Using Joblib
finalize a machine learning project

steps For Finalizing classification models  pima indian dataset
steps For Finalizing regression models  boston housing dataset
Predictions and Case Studies

Case study 1: predictions using the Pima Indian Diabetes Dataset
Case study 2: the Boston Housing cost Dataset
Machine Learning and Data Science is the most lucrative job in the technology arena now a days. Learning this course will make you equipped to compete in this area.
Best wishes with your learning. Se you soon in the class room.