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Machine Learning with Python: Data Science for Beginners
Data Science / Machine Learning is the most in-demand and Highest Paying job of 2017
This Course Includes
udemy
5 (72 reviews )
11h 3m 
english
Online - Self Paced
professional certificate
Udemy
About Machine Learning with Python: Data Science for Beginners
_Machine learning_ is a field of computer science that gives computers the ability to learn without being explicitly programmed. Machine Learning is the most in-demand and Highest Paying job of 2017 and the same trend will follow for the coming years. With an
average salary of $120,000
(Glassdoor and Indeed), Machine Learning will help you to get one of the top-paying jobs. This course is designed for both complete beginners with no programming experience or experienced developers looking to make the jump to Data Science!
At the end of the course you will be able to
Master Machine Learning using PythonDemystifying Artificial Intelligence, Machine Learning, Data ScienceExplore & Define a ML use caseML Business Solution BlueprintExplore Spyder, Pandas and NumPyImplement Data EngineeringExploratory Data Analysis Introduction to Statistics and Probability DistributionsLearn Machine Learning MethodologyUnderstand Supervised Learning Supervised LearningImplement Simple & Multiple Linear RegressionDecision TreesRegression & Classification Model EvaluationCross Validation, Hyperparameter Ensemble ModelingRandom Forest & XGBoost Learning Machine Learning is a definite way to advance your career and will open doors to new Job opportunities. 100% MONEY-BACK GUARANTEE This course comes with a 30-day money back guarantee. If you're not happy, ask for a refund, all your money back, no questions asked. Feel forward to have a look at course description and demo videos and we look forward to see you inside.
What You Will Learn?
- Master Machine Learning using Python .
- Demystify Artificial Intelligence, Machine Learning, Data Science .
- ML Business Solution Blueprint .
- Explore Spyder, Pandas and NumPy .
- Implement Data Engineering and Data Analysis .
- Introduction to Statistics and Probability Distributions .
- Understand Supervised and Unsupervised Learning .
- Implement Simple & Multiple Linear Regression .
- Regression & Classification Model Evaluation .
- Cross Validation, Hyperparameter, Ensemble Modeling, Random Forest & XGBoost.