Automated machine learning (AutoML) represents a fundamental shift in the way organizations of all sizes approach machine learning and data science. Applying traditional machine learning methods to real-world business problems is time-consuming, resource-intensive, and challenging. It requires experts in several disciplines, including data scientists – some of the most sought-after professionals in the job market right now.
Automated machine learning changes that, making it easier to build and use machine learning models in the real world by running systematic processes on raw data and selecting models that pull the most relevant information from the data – what is often referred to as “the signal in the noise.” Automated machine learning incorporates machine learning best practices from top-ranked data scientists to make data science more accessible across the organization.
Manually constructing a machine learning model is a multistep process that requires domain knowledge, mathematical expertise, and computer science skills – which is a lot to ask of one company, let alone one data scientist (provided you can hire and retain one). Not only that, there are countless opportunities for human error and bias, which degrades model accuracy and devalues the insights you might get from the model. Automated machine learning enables organizations to use the baked-in knowledge of data scientists without expending time and money to develop the capabilities themselves, simultaneously improving return on investment in data science initiatives and reducing the amount of time it takes to capture value.
We'll cover everything you need to know for the full data science and machine learning tech stack required at the world's top companies. Our students have gotten jobs at Dell, Google Developers, TCS, Wipro, and other top tech companies! We've structured the course using our experience teaching both online and in-person to deliver a clear and structured approach that will guide you through understanding not just how to use data science and machine learning libraries, but why we use them. This course is balanced between practical real-world case studies and the mathematical theory behind machine learning algorithms.
How much does a Data Scientist make in the United States?
The national average salary for a Data Scientist is US$1,20,718 per year in the United States, 2.8k salaries reported, updated on July 15, 2021 (source: glassdoor)
Salaries by Company, Role, Average Base Salary in (USD)
Facebook Data Scientist makes US$1,36,000/yr. Analyzed from 1,014 salaries.
Amazon Data Scientist makes US$1,25,704/yr. Analyzed from 307 salaries.
Apple Data Scientist makes US$1,53,885/yr. Analyzed from 147 salaries.
Google Data Scientist makes US$1,48,316/yr. Analyzed from 252 salaries.
IBM Data Scientist makes US$1,32,662/yr. Analyzed from 388 salaries.
Microsoft Data Scientist makes US$1,33,810/yr. Analyzed from 205 salaries.
Intel Corporation Data Scientist makes US$1,25,930/yr. Analyzed from 131 salaries.
In This Course, We Are Going To Build 15 Real World Auto-ML Projects Listed Below:
Project-1 Heart Attack Risk Prediction using Eval ML
Project-2 Credit Card Fraud Detection using Pycaret
Project-3 Flight Fare Prediction using Auto SK Learn(Regression)
Project-4 Petrol Price Forecasting using Auto Keras
Project-5 Bank Customer Churn Prediction using H2O Auto ML
Project-6 Air Quality Index Predictor using TPOT with End-To-End Deployment
Project-7 Rain Prediction using ML models and PyCaret with End-To-End Deployment
Project-8 Pizza Price Prediction using ML and EVALML(Auto ML)
Project-9 IPL Cricket Score prediction using TPOT
Project-10 Predicting Bike Rentals Count using ML and H2O Auto ML
Project-11 Concrete Compressive Strength Prediction using Auto Keras
Project-12 Bangalore House Price using Auto SK Learn
Project-13 Hospital Mortality Prediction using PyCaret
Project-14 Employee Evaluation for Promotion using ML and Eval Auto ML
Project-15 Drinking Water Potability Prediction using ML and H2O Auto ML
The Only Course With 15 Auto-ML Projects
(Read This): This Course Is Worth Of Your Time And Money, Enroll Now Before Offer Expires.
