Udemy - Clustering & Classification With Machine Learning in Python
$200 Free 100% Off- Lectures 56
- Length 5.5 hours
- Skill Levels Beginner Level
- Languages English
- Published 7/2018
Udemy Coupon Code : PROMO1000
Description
CLUSTERING & CLASSIFICATION WITH MACHINE LEARNING IN PYTHON
With so many Python based Data Science & Machine Learning courses around, why this course?
As the title name suggests- this course your complete guide to both supervised & unsupervised learning using Python. This means, this course covers MAIN ASPECTS of practical data science and if you take this course, you can do away with taking other courses or buying books on Python based data science. In this age of big data, companies across the globe use Python to sift through the avalanche of information at their disposal. By becoming proficient in unsupervised & supervised learning in Python, you can give your company a competitive edge –and boost your career to the next level.
BOOST YOUR CAREER TO THE NEXT LEVEL
LEARN FROM AN EXPERT DATA SCIENTIST WITH +5 YEARS OF EXPERIENCE
But first things first. My name is MINERVA SINGH and I am an Oxford University MPhil (Geography and Environment) graduate. I recently finished a PhD at Cambridge University. I have several years of experience in analyzing real life data from different sources using data science techniques and producing publications for international peer reviewed journals.
Over the course of my research I realized almost all the Python data science courses and books out there do not account for the multidimensional nature of the topic . This course will give you a robust grounding in the main aspects of machine learning- clustering & classification.
Unlike other Python instructors, I dig deep into the machine learning features of Python and gives you a one-of-a-kind grounding in Python Data Science! You will go all the way from carrying out data reading & cleaning to machine learning to finally implementing simple deep learning based models using Python
Inside this course, you’ll discover 7 complete sections addressing every aspect of Python Machine Learning:
• A full introduction to Python Data Science and powerful Python driven framework for data science, Anaconda • Getting started with Jupyter notebooks for implementing data science techniques in Python • Data Structures and Reading in Pandas, including CSV, Excel and HTML data • How to Pre-Process and “Wrangle” your Python data by removing NAs/No data, handling conditional data, grouping by attributes, etc. • Machine Learning, Supervised Learning, Unsupervised Learning in Python • Artificial neural networks (ANN) and Deep Learning. You’ll even discover how to use artificial neural networks and deep learning structures for classification! With such a rigorous grounding in so many topics, you will be an unbeatable data scientist
With this course, you’ll have the keys to the entire Python Machine Learning Kingdom!
You DO NOT need any prior Python or Statistics/Machine Learning Knowledge to get Started
You’ll start by absorbing the most valuable Python Data Science basics and techniques. I use easy-to-understand, hands-on methods to simplify and address even the most difficult concepts in Python. My course will help you implement the methods using real data obtained from different sources. Many courses use made-up data that does not empower students to implement Python based data science in real -life
After taking this course, you’ll easily use packages like Numpy, Pandas, and Matplotlib to work with real data in Python. You’ll even understand concepts like unsupervised learning, dimension reduction and supervised learning.. I will even introduce you to deep learning and neural networks using the powerful H2o framework!
Most importantly, you will learn to implement these techniques practically using Python. You will have access to all the data and scripts used in this course. Remember, I am always around to support my students!!.
With so many Python based Data Science & Machine Learning courses around, why this course?
As the title name suggests- this course your complete guide to both supervised & unsupervised learning using Python. This means, this course covers MAIN ASPECTS of practical data science and if you take this course, you can do away with taking other courses or buying books on Python based data science. In this age of big data, companies across the globe use Python to sift through the avalanche of information at their disposal. By becoming proficient in unsupervised & supervised learning in Python, you can give your company a competitive edge –and boost your career to the next level.
BOOST YOUR CAREER TO THE NEXT LEVEL
LEARN FROM AN EXPERT DATA SCIENTIST WITH +5 YEARS OF EXPERIENCE
But first things first. My name is MINERVA SINGH and I am an Oxford University MPhil (Geography and Environment) graduate. I recently finished a PhD at Cambridge University. I have several years of experience in analyzing real life data from different sources using data science techniques and producing publications for international peer reviewed journals.
Over the course of my research I realized almost all the Python data science courses and books out there do not account for the multidimensional nature of the topic . This course will give you a robust grounding in the main aspects of machine learning- clustering & classification.
Unlike other Python instructors, I dig deep into the machine learning features of Python and gives you a one-of-a-kind grounding in Python Data Science! You will go all the way from carrying out data reading & cleaning to machine learning to finally implementing simple deep learning based models using Python
Inside this course, you’ll discover 7 complete sections addressing every aspect of Python Machine Learning:
• A full introduction to Python Data Science and powerful Python driven framework for data science, Anaconda • Getting started with Jupyter notebooks for implementing data science techniques in Python • Data Structures and Reading in Pandas, including CSV, Excel and HTML data • How to Pre-Process and “Wrangle” your Python data by removing NAs/No data, handling conditional data, grouping by attributes, etc. • Machine Learning, Supervised Learning, Unsupervised Learning in Python • Artificial neural networks (ANN) and Deep Learning. You’ll even discover how to use artificial neural networks and deep learning structures for classification! With such a rigorous grounding in so many topics, you will be an unbeatable data scientist
With this course, you’ll have the keys to the entire Python Machine Learning Kingdom!
You DO NOT need any prior Python or Statistics/Machine Learning Knowledge to get Started
You’ll start by absorbing the most valuable Python Data Science basics and techniques. I use easy-to-understand, hands-on methods to simplify and address even the most difficult concepts in Python. My course will help you implement the methods using real data obtained from different sources. Many courses use made-up data that does not empower students to implement Python based data science in real -life
After taking this course, you’ll easily use packages like Numpy, Pandas, and Matplotlib to work with real data in Python. You’ll even understand concepts like unsupervised learning, dimension reduction and supervised learning.. I will even introduce you to deep learning and neural networks using the powerful H2o framework!
Most importantly, you will learn to implement these techniques practically using Python. You will have access to all the data and scripts used in this course. Remember, I am always around to support my students!!.
What are the requirements?
- Should be able to operate & install software on a computer
- Interested in learning machine learning based techniques for analyzing data
- Interested in implementing machine learning techniques in the Python environment
- Prior exposure to common machine learning terms such as unsupervised and supervised learning
What am I going to get from this course?
- Will be able to harness the power of Anaconda/iPython for practical data science
- Read in data into the Python environment from different sources
- Carry out basic data pre-processing and wrangling in Python
- Implement unsupervised/clustering techniques such as k-means clustering
- Implement dimensional reduction techniques (PCA) and feature selection
- Implement supervised learning techniques/classification such as random forests in Python
- Neural network and deep learning based classification
