Build 30 Real World Data Science & Machine Learning Projects
Practical Data Science Course: Learn To Build Machine Learning, Data Science Projects & Case Studies With Python
What you’ll learn
Create supervised machine learning algorithms to predict classes.
Introduction to must know concepts in Machine Learning which will help you to prepare for interview
Master Machine Learning on Python, Improve or refresh knowledge in machine learning
Learn how to Learn Machine Learning, Make powerful visualizations, analysis
Learn to pre process data, clean data, and analyze large data.
Learn which Machine Learning model to choose for each type of problem
Learn best practices when it comes to Data Science Workflow
Learn to use the popular library Scikit-learn in your projects
Interest in Machine Learning
Basic understanding of machine learning algorithms
What is Data Science?
Data science is the domain of study that deals with vast volumes of data using modern tools and techniques to find unseen patterns, derive meaningful information, and make business decisions. Data science uses complex machine learning algorithms to build predictive models.
The data used for analysis can be from multiple sources and present in various formats.
Now that you know what is data science, let’s see why data science is essential in the current scenario.
Why Data Science?
Data science or data-driven science enables better decision making, predictive analysis, and pattern discovery. It lets you:
- Find the leading cause of a problem by asking the right questions
- Perform exploratory study on the data
- Model the data using various algorithms
- Communicate and visualize the results via graphs, dashboards, etc.
In practice, data science is already helping the airline industry predict disruptions in travel to alleviate the pain for both airlines and passengers. With the help of data science, airlines can optimize operations in many ways, including:
- Plan routes and decide whether to schedule direct or connecting flights
- Build predictive analytics models to forecast flight delays
- Offer personalized promotional offers based on customers booking patterns
- Decide which class of planes to purchase for better overall performance
Prerequisites for Data Science
Here are some of the technical concepts you should know about before starting to learn what is data science.
1. Machine Learning
Machine learning is the backbone of data science. Data Scientists need to have a solid grasp on ML in addition to basic knowledge of statistics.
Mathematical models enable you to make quick calculations and predictions based on what you already know about the data. Modeling is also a part of Machine Learning and involves identifying which algorithm is the most suitable to solve a given problem and how to train these models.
Statistics are at the core of data science. A sturdy handle on statistics can help you extract more intelligence and obtain more meaningful results.
Who this course is for:
- Anyone who wants to Learn Machine Learning
- Anyone who wants to improve or recall Machine Learning skills
- Anyone who wants to prepare for a Machine Learning interview
Created by TheMachineLearning.Org .
Last updated 11/2021
Size: 10.92 GB