Brief Introduction
Many of you may have already taken a course in machine learning or data science or are familiar with machine learning models. In this course we will take a more general approach, walking through the questioning, modeling and validation steps of the model building process. The goal is to get you to practice thinking in depth about a problem and coming up with your own solutions. Many examples we will attempt may not have one correct answer but will require you to work through the problems applyinCourse Summary
Learn how to build and validate models in this course, which covers topics such as data cleaning, feature engineering, and model selection. Get hands-on experience with real-world datasets and gain the skills to make informed decisions using data.Key Learning Points
- Understand the process of model building and validation
- Learn how to clean and preprocess data
- Explore different modeling techniques
- Gain experience working with real-world datasets
Job Positions & Salaries of people who have taken this course might have
- USA: $60,000 - $100,000
- India: ₹4,00,000 - ₹12,00,000
- Spain: €25,000 - €40,000
- USA: $60,000 - $100,000
- India: ₹4,00,000 - ₹12,00,000
- Spain: €25,000 - €40,000
- USA: $80,000 - $150,000
- India: ₹6,00,000 - ₹20,00,000
- Spain: €30,000 - €60,000
- USA: $60,000 - $100,000
- India: ₹4,00,000 - ₹12,00,000
- Spain: €25,000 - €40,000
- USA: $80,000 - $150,000
- India: ₹6,00,000 - ₹20,00,000
- Spain: €30,000 - €60,000
- USA: $100,000 - $180,000
- India: ₹8,00,000 - ₹25,00,000
- Spain: €40,000 - €80,000
Related Topics for further study
Learning Outcomes
- Ability to build and validate models with real-world datasets
- Understanding of data cleaning and preprocessing techniques
- Familiarity with different modeling techniques and how to select the appropriate one for a given problem
Prerequisites or good to have knowledge before taking this course
- Basic knowledge of Python
- Familiarity with statistical concepts
Course Difficulty Level
IntermediateCourse Format
- Self-paced
- Online
- Hands-on
Similar Courses
- Applied Data Science with Python
- Data Science Essentials
- Data Analysis and Interpretation
Related Education Paths
Notable People in This Field
- Andrew Ng
- Hilary Mason
Related Books
Description
This course will teach you how to start from scratch in understanding and paying attention to what is important in the data and how to answer questions about data.Requirements
- This is an advanced course, and the ideal students for this class are prepared individuals who have: Python programming knowledge, familiarity with python tools like Ipython Notebook and data analysis libraries like Scikit-learn, Scipy, and Pandas Knowledge of descriptive, inferential, and predictive statistics Knowledge of calculus, especially derivatives and integrals Knowledge of basic matrix algebra - matrices, vectors, determinant, identity matrix, multiplication, inverse Taken Intro to Machine learning and have understanding of common supervised learning and unsupervised learning algorithms, such as SVM and k-means clustering See the Technology Requirements for using Udacity.
Knowledge
- Instructor videosLearn by doing exercisesTaught by industry professionals
Outline
- lesson 1 Introduction to the QMV Process Learn about the Question Modeling and Validation (QMV) process of data analysis. Understand the basics behind each step. Apply the QMV process to analyze on how Udacity employees choose candies! lesson 2 Question Phase Learn how to turn a vague question into a statistical one that can be analyzed with statistics and machine learning. Analyze a Twitter dataset and try to predict when a person will tweet next! lesson 3 Modeling Phase Build rigorous mathematical statistical and machine learning models to make accurate predictions. Look through the recently released U.S. medicare dataset for anomalous transactions. lesson 4 Validation Phase Learn fundamental metrics to grade the performance of your models. Analyze the AT&T connected cars data set. See if you can tell the drivers apart by analyzing their driving patterns. lesson 5 Identify Hacking Attempts from Network Flow Logs Create a program that examines log data and scores the likelihood that a brute force attack is taking place on a server.
PHP OOP Complete Online Exam System with PHP jQuery Ajax
- 3.8
-
![]()
![]()
![]()
![]()
![]()
This Complete Course Where You Will Learn How You Can Create One Online Exam System with Using PHP OOP , jQuery, Ajax...
FRM Part 1 (2020) - Book 1 - Foundations of Risk Management
- 4.3
-
![]()
![]()
![]()
![]()
![]()
FRM Course by Prof. James Forjan In this course, Prof. James Forjan has taught college-level business classes for over 25 years....
How to Win a Data Science Competition: Learn from Top Kagglers
- 4.7
-
![]()
![]()
![]()
![]()
![]()
At the same time you get to do it in a competitive context against thousands of participants where each one tries to build the most predictive algorithm....
How to Validate your Startup Idea
- 4.3
-
![]()
![]()
![]()
![]()
![]()
Starting a new business begins with an idea that needs to evolve through experimentation, iteration and interactions with people. How can you learn entrepreneurship?...
