A+ guide to using Machine Learning to classify objects, predict future prices, and automatically learn fixes to problems
About This Video
Learn about supervised learning: how to classify data points and predict future numbers
Practical exercises on unsupervised learning: how to segment clients and cluster documents
Intuition-driven practical tour through Machine Learning, packed with step-by-step instructions, working examples, and …
Getting Started with Machine Learning in Python
Video description
A+ guide to using Machine Learning to classify objects, predict future prices, and automatically learn fixes to problems
About This Video
Learn about supervised learning: how to classify data points and predict future numbers
Practical exercises on unsupervised learning: how to segment clients and cluster documents
Intuition-driven practical tour through Machine Learning, packed with step-by-step instructions, working examples, and helpful advice
In Detail
Machine Learning is a hot topic. And you want to get involved! From developers to analysts, this course aims to bring Machine Learning to those with coding experience and numerical skills.
In this course, we introduce, via intuition rather than theory, the core of what makes Machine Learning work. Learn how to use labeled datasets to classify objects or predict future values, so that you can provide more accurate and valuable analysis. Use unlabelled datasets to do segmentation and clustering, so that you can separate a large dataset into sensible groups.
You will learn to understand and estimate the value of your dataset. We guide you through creating the best performance metric for your task at hand, and how that takes you to the correct model to solve your problem. Understand how to clean data for your application, and how to recognize which Machine Learning task you are dealing with.
If you want to move past Excel and if-then-else into automatically learned ML solutions, this course is for you!
This course uses Python 3.6, while not the latest version available, it provides relevant and informative content for legacy users of Python.
Audience
This course is for anyone, with a little coding experience and basic numerical skills, who wants to go beyond hardcoded, rule-based programming and use their datasets to automatically learn new algorithms that solve problems. From developers to analysts, this course aims to bring Machine Learning to everyone. It uses intuition as a base from which to explain the theory behind Machine Learning and its algorithms. Basic Python skills are assumed.
Chapter 1 : Launching a Python Environment to Create Machine Learning Models
The Course Overview
Machine Learning versus Rule-Based Programming
Understanding What Machine Learning Can Do Using the Tasks Framework
Creating Machine-Learned Models with Python and scikit-learn
Supervised Versus Unsupervised Learning
Chapter 2 : Prepare Your Datasets for Machine Learning with Data Cleaning
In this video, we will fix your machine learning models by understanding your data source
Dealing with Missing Values – An Example
Standardization and Normalization to Deal with Variables with Different Scales
Eliminating Duplicate Entries
Chapter 3 : Put Data into Their Right Categories with Classification
How Do We Learn Rules to Classify Objects?
Understanding Logistic Regression – Your First Classifier
Applying Logistic Regression to the Iris Classification Task
Closing Our First Machine Learning Pipeline with a Simple Model Evaluator
Chapter 4 : Predict Numbers in the Future with Regression
Creating Formulas That Predict the Future – A House Price Example
Understanding Linear Regression – Your First Regressor
Applying Linear Regression to the Boston House Price Task
Evaluating Numerical Predictions with Least Squares
Chapter 5 : Unsupervised Learning: Segmenting Groups and Detecting Outliers
Exploring Unsupervised Learning and Its Usefulness
Finding Groups Automatically with K-means Clustering
Reducing the Number of Variables in Your Data with PCA
Smooth out Your Histograms with Kernel Density Estimation
Chapter 6 : Modeling Complex Relationships with Nonlinear Models
Create Explainable Models with Decision Trees
Automatic Feature Engineering with Support Vector Machines
Deal with Nonlinear Relationships with Polynomial Regression
Reduce the Number of Learned Rules with Regularization
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