Machine Learning and Data Science with Python: A Complete Beginners Guide [Video]
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Free ChapterCourse Overview and Table of Contents
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Introduction to Machine Learning
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System and Environment Preparation
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Learn Basics of Python
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Learn Basics of NumPy
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Learn Basics of Matplotlib
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Learn Basics of Pandas
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Understanding the CSV Data File
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Load and Read CSV Data File
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Dataset Summary
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Dataset Visualization
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Data Preparation
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Feature Selection
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Refresher Session - the Mechanism of Re-Sampling, Training, and Testing
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Algorithm Evaluation Techniques
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Algorithm Evaluation Metrics
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Classification Algorithm Spot Check - Logistic Regression
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Classification Algorithm Spot Check - Linear Discriminant Analysis
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Classification Algorithm Spot Check - K-Nearest Neighbors
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Classification Algorithm Spot Check - Naive Bayes
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Classification Algorithm Spot Check – CART
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Classification Algorithm Spot Check - Support Vector Machines
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Regression Algorithm Spot Check - Linear Regression
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Regression Algorithm Spot Check - Ridge Regression
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Regression Algorithm Spot Check - LASSO Linear Regression
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Regression Algorithm Spot Check - Elastic Net Regression
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Regression Algorithm Spot Check - K-Nearest Neighbors
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Regression Algorithm Spot Check – CART
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Regression Algorithm Spot Check - Support Vector Machines (SVM)
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Compare Algorithms - Part 1: Choosing the Best Machine Learning Model
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Compare Algorithms - Part 2: Choosing the Best Machine Learning Model
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Pipelines: Data Preparation and Data Modelling
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Pipelines: Feature Selection and Data Modelling
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Performance Improvement: Ensembles – Voting
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Performance Improvement: Ensembles – Bagging
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Performance Improvement: Ensembles – Boosting
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Performance Improvement: Parameter Tuning Using Grid Search
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Performance Improvement: Parameter Tuning Using Random Search
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Export, Save and Load Machine Learning Models: Pickle
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Export, Save and Load Machine Learning Models: Joblib
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Finalizing a Model - Introduction and Steps
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Finalizing a Classification Model - the Pima Indian Diabetes Dataset
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Quick Session: Imbalanced Dataset - Issue Overview and Steps
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Iris Dataset: Finalizing Multi-Class Dataset
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Finalizing a Regression Model - the Boston Housing Price Dataset
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Real-Time Predictions: Using the Pima Indian Diabetes Classification Model
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Real-time Predictions: Using Iris Flowers Multi-Class Classification Dataset
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Real-Time Predictions: Using the Boston Housing Regression Model
About this
video
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.
All the code and supporting files for this course are available at https://github.com/PacktPublishing/Machine-Learning-and-Data-Science-with-Python-A-Complete-Beginners-Guide
- Publication date:
- May 2019
- Publisher
- Packt
- Duration
- 10 hours 19 minutes
- ISBN
- 9781838980689
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