Build RealWorld Machine Learning Projects With Python
Learn to create Classical Machine learning Learning Algorithms in Python
Python,Machine Learning,Big Data,Data Science and AI ML,Development,
Lectures -5
Duration -1.5 hours
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Course Description
In This Course, Solve Business Problems Using Data Science Practically. Learn To Build & Deploy Machine Learning, Data Science, Artificial Intelligence, Auto Ml, Deep Learning, Natural Language Processing (Nlp) Web Applications Projects With Python (Flask, Django, Heroku).
Data science is the field of study that combines domain expertise, programming skills, and knowledge of mathematics and statistics to extract meaningful insights from data. Data science practitioners apply machine learning algorithms to numbers, text, images, video, audio, and more to produce artificial intelligence (AI) systems to perform tasks that ordinarily require human intelligence. In turn, these systems generate insights which analysts and business users can translate into tangible business value.
More and more companies are coming to realize the importance of data science, AI, and machine learning. Regardless of industry or size, organizations that wish to remain competitive in the age of big data need to efficiently develop and implement data science capabilities or risk being left behind.
In This Course, We Are Going To Work On 2 Real World Projects Listed Below:
- Project-1: Toxic Comment Classification
- Project-2: UK_Road_Accident_Timeseries_Forecasting
The Only Course You Need To Become A Data Scientist, Get Hired And Start A New Career
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Goals
What will you learn in this course:
- Learn to perform Classification and Regression modeling
- Master Machine Learning and use it on the job
- Use Seaborn to create beautiful statistical plots with Python.
- Get set-up quickly with the Anaconda data science stack environment.
Prerequisites
What are the prerequisites for this course?
There is no specific prerequisite to learn machine learning. But you need to be from engineering/science/Maths/Stats background to understand the theory and the techniques used. You need to be good in mathematics. If you are not, still you can machine learning, but you will face difficulty when solving complex real world problems. Many say you need to know Linear algebra, Calculus etc. but I have never learnt it, yet I am able to work on machine learning.
Curriculum
Check out the detailed breakdown of what’s inside the course
Project-1 : UK_Road_Accident_Timeseries_Forecasting
2 Lectures
- UK_Road_Accident_Timeseries_Forecasting_EDA 32:20 32:20
- Forecast UK Accident rates based on Number of Casualties on SARIMA,FbP,LSTM's 28:54 28:54
Project-2 : Toxic_Comments_Classification
3 Lectures
Instructor Details
AKHIL VYDYULA
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