Machine learning and data science for programming beginners using Python with scikit-learn, SciPy, Matplotlib, and Pandas.
About This Video
In Detail
Artificial intelligence, machine learning, and deep learning neural networks …
Machine learning and data science for programming beginners using Python with scikit-learn, SciPy, Matplotlib, and Pandas.
About This Video
In Detail
Artificial intelligence, machine learning, and deep learning neural networks are the most used terms in the technology world today. They're also the most misunderstood and confused terms. Artificial intelligence is a broad spectrum of science that tries to make machines intelligent like humans, while machine learning and neural networks are two subsets that sit within this vast machine learning platform. But in this course, you will focus mainly on machine learning, which will include preparing your machine to make it ready for a prediction test.
You will be using Python as your programming language. Python is a great tool for the development of programs that perform data analysis and prediction. It has a variety of classes and features that perform complex mathematical analyses and provide solutions in just a few lines of code, making it easier for you to get up to speed with data science and machine learning.
Machine learning and data science jobs are among the most lucrative in the technology industry in recent times. Exploring this course will help you get well-versed with essential concepts and prepare you for a career in these fields.
Chapter 1 : Course Overview and Table of Contents
Course Overview and Table of Contents
Chapter 2 : Introduction to Machine Learning
Introduction to Machine Learning
Chapter 3 : System and Environment Preparation
System and Environment Preparation
Chapter 4 : Learn Basics of Python
Learn Basics of Python
Chapter 5 : Learn Basics of NumPy
Learn Basics of NumPy
Chapter 6 : Learn Basics of Matplotlib
Learn Basics of Matplotlib
Chapter 7 : Learn Basics of Pandas
Learn Basics of Pandas
Chapter 8 : Understanding the CSV Data File
Understanding the CSV Data File
Chapter 9 : Load and Read CSV Data File
Load and Read CSV Data File
Chapter 10 : Dataset Summary
Dataset Summary
Chapter 11 : Dataset Visualization
Dataset Visualization
Chapter 12 : Data Preparation
Data Preparation
Chapter 13 : Feature Selection
Feature Selection
Chapter 14 : Refresher Session - the Mechanism of Re-Sampling, Training, and Testing
Refresher Session - the Mechanism of Re-Sampling, Training, and Testing
Chapter 15 : Algorithm Evaluation Techniques
Algorithm Evaluation Techniques
Chapter 16 : Algorithm Evaluation Metrics
Algorithm Evaluation Techniques
Chapter 17 : Classification Algorithm Spot Check - Logistic Regression
Classification Algorithm Spot Check - Logistic Regression
Chapter 18 : Classification Algorithm Spot Check - Linear Discriminant Analysis
Classification Algorithm Spot Check - Linear Discriminant Analysis
Chapter 19 : Classification Algorithm Spot Check - K-Nearest Neighbors
Classification Algorithm Spot Check - K-Nearest Neighbors
Chapter 20 : Classification Algorithm Spot Check - Naive Bayes
Classification Algorithm Spot Check - Naive Bayes
Chapter 21 : Classification Algorithm Spot Check – CART
Classification Algorithm Spot Check – CART
Chapter 22 : Classification Algorithm Spot Check - Support Vector Machines
Classification Algorithm Spot Check - Support Vector Machines
Chapter 23 : Regression Algorithm Spot Check - Linear Regression
Regression Algorithm Spot Check - Linear Regression
Chapter 24 : Regression Algorithm Spot Check - Ridge Regression
Regression Algorithm Spot Check - Ridge Regression
Chapter 25 : Regression Algorithm Spot Check - LASSO Linear Regression
Regression Algorithm Spot Check - LASSO Linear Regression
Chapter 26 : Regression Algorithm Spot Check - Elastic Net Regression
Regression Algorithm Spot Check - Elastic Net Regression
Chapter 27 : Regression Algorithm Spot Check - K-Nearest Neighbors
Regression Algorithm Spot Check - K-Nearest Neighbors
Chapter 28 : Regression Algorithm Spot Check – CART
Regression Algorithm Spot Check – CART
Chapter 29 : Regression Algorithm Spot Check - Support Vector Machines (SVM)
Regression Algorithm Spot Check - Support Vector Machines
Chapter 30 : Compare Algorithms - Part 1: Choosing the Best Machine Learning Model
Compare Algorithms - Part 1: Choosing the Best Machine Learning Model
Chapter 31 : Compare Algorithms - Part 2: Choosing the Best Machine Learning Model
Compare Algorithms - Part 2: Choosing the Best Machine Learning Model
Chapter 32 : Pipelines: Data Preparation and Data Modelling
Pipelines: Data Preparation and Data Modelling
Chapter 33 : Pipelines: Feature Selection and Data Modelling
Pipelines: Feature Selection and Data Modelling
Chapter 34 : Performance Improvement: Ensembles – Voting
Performance Improvement: Ensembles – Voting
Chapter 35 : Performance Improvement: Ensembles – Bagging
Performance Improvement: Ensembles – Bagging
Chapter 36 : Performance Improvement: Ensembles – Boosting
Performance Improvement: Ensembles – Boosting
Chapter 37 : Performance Improvement: Parameter Tuning Using Grid Search
Performance Improvement: Parameter Tuning Using Grid Search
Chapter 38 : Performance Improvement: Parameter Tuning Using Random Search
Performance Improvement: Parameter Tuning Using Random Search
Chapter 39 : Export, Save and Load Machine Learning Models: Pickle
Export, Save and Load Machine Learning Models: Pickle
Chapter 40 : Export, Save and Load Machine Learning Models: Joblib
Export, Save and Load Machine Learning Models Joblib
Chapter 41 : Finalizing a Model - Introduction and Steps
Finalizing a Model - Introduction and Steps
Chapter 42 : Finalizing a Classification Model - the Pima Indian Diabetes Dataset
Finalizing a Classification Model - the Pima Indian Diabetes Dataset
Chapter 43 : Quick Session: Imbalanced Dataset - Issue Overview and Steps
Quick Session: Imbalanced Dataset - Issue Overview and Steps
Chapter 44 : Iris Dataset: Finalizing Multi-Class Dataset
Iris Dataset: Finalizing Multi-Class Dataset
Chapter 45 : Finalizing a Regression Model - the Boston Housing Price Dataset
Finalizing a Regression Model - the Boston Housing Price Dataset
Chapter 46 : Real-Time Predictions: Using the Pima Indian Diabetes Classification Model
Real-Time Predictions: Using the Pima Indian Diabetes Classification Model
Chapter 47 : Real-time Predictions: Using Iris Flowers Multi-Class Classification Dataset
Real-Time Predictions: Using Iris Flowers Multi-Class Classification Dataset
Chapter 48 : Real-Time Predictions: Using the Boston Housing Regression Model
Real-Time Predictions: Using the Boston Housing Regression Model