Video description
Machine learning and data science for programming beginners using Python with scikit-learn, SciPy, Matplotlib, and Pandas.
About This Video
- Learn machine learning and data science using Python
- A practical course designed for beginners who are interested in machine learning using Python
- Work on predictions and case studies
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.
Publisher resources
Table of contents
- Chapter 1 : Course Overview and Table of Contents
- Chapter 2 : Introduction to Machine Learning
- Chapter 3 : System and Environment Preparation
- Chapter 4 : Learn Basics of Python
- Chapter 5 : Learn Basics of NumPy
- Chapter 6 : Learn Basics of Matplotlib
- Chapter 7 : Learn Basics of Pandas
- Chapter 8 : Understanding the CSV Data File
- Chapter 9 : Load and Read CSV Data File
- Chapter 10 : Dataset Summary
- Chapter 11 : Dataset Visualization
- Chapter 12 : Data Preparation
- Chapter 13 : Feature Selection
- Chapter 14 : Refresher Session - the Mechanism of Re-Sampling, Training, and Testing
- Chapter 15 : Algorithm Evaluation Techniques
- Chapter 16 : Algorithm Evaluation Metrics
- Chapter 17 : Classification Algorithm Spot Check - Logistic Regression
- Chapter 18 : Classification Algorithm Spot Check - Linear Discriminant Analysis
- Chapter 19 : Classification Algorithm Spot Check - K-Nearest Neighbors
- Chapter 20 : Classification Algorithm Spot Check - Naive Bayes
- Chapter 21 : Classification Algorithm Spot Check – CART
- Chapter 22 : Classification Algorithm Spot Check - Support Vector Machines
- Chapter 23 : Regression Algorithm Spot Check - Linear Regression
- Chapter 24 : Regression Algorithm Spot Check - Ridge Regression
- Chapter 25 : Regression Algorithm Spot Check - LASSO Linear Regression
- Chapter 26 : Regression Algorithm Spot Check - Elastic Net Regression
- Chapter 27 : Regression Algorithm Spot Check - K-Nearest Neighbors
- Chapter 28 : Regression Algorithm Spot Check – CART
- Chapter 29 : Regression Algorithm Spot Check - Support Vector Machines (SVM)
- Chapter 30 : Compare Algorithms - Part 1: Choosing the Best Machine Learning Model
- Chapter 31 : Compare Algorithms - Part 2: Choosing the Best Machine Learning Model
- Chapter 32 : Pipelines: Data Preparation and Data Modelling
- Chapter 33 : Pipelines: Feature Selection and Data Modelling
- Chapter 34 : Performance Improvement: Ensembles – Voting
- Chapter 35 : Performance Improvement: Ensembles – Bagging
- Chapter 36 : Performance Improvement: Ensembles – Boosting
- Chapter 37 : Performance Improvement: Parameter Tuning Using Grid Search
- Chapter 38 : Performance Improvement: Parameter Tuning Using Random Search
- Chapter 39 : Export, Save and Load Machine Learning Models: Pickle
- Chapter 40 : Export, Save and Load Machine Learning Models: Joblib
- Chapter 41 : Finalizing a Model - Introduction and Steps
- Chapter 42 : Finalizing a Classification Model - the Pima Indian Diabetes Dataset
- Chapter 43 : Quick Session: Imbalanced Dataset - Issue Overview and Steps
- Chapter 44 : Iris Dataset: Finalizing Multi-Class Dataset
- Chapter 45 : Finalizing a Regression Model - the Boston Housing Price Dataset
- Chapter 46 : Real-Time Predictions: Using the Pima Indian Diabetes Classification Model
- Chapter 47 : Real-time Predictions: Using Iris Flowers Multi-Class Classification Dataset
- Chapter 48 : Real-Time Predictions: Using the Boston Housing Regression Model
Product information
- Title: Machine Learning and Data Science with Python: A Complete Beginners Guide
- Author(s):
- Release date: May 2019
- Publisher(s): Packt Publishing
- ISBN: 9781838980689
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