What you'll learn:
- 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 & Wrangling In Python
- Implement Unsupervised/Clustering Techniques Such As k-means Clustering
- Implement Dimensional Reduction Techniques (PCA) & Feature Selection
- Implement Supervised Learning Techniques/Classification Such As Random Forests In Python
- Neural Network & Deep Learning Based Classification
HERE IS WHY YOU SHOULD TAKE THIS COURSE:
This course yourcomplete guideto both supervised & unsupervised learningusing Python. This means, this course coversall the main aspectsof 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 inPython, you can give your company a competitive edge and boost your career to the next level.
LEARN FROM AN EXPERT DATA SCIENTIST WITH +5 YEARS OF EXPERIENCE:
My name isMinerva Singh and Iam an Oxford University MPhil (Geography and Environment) graduate. I also just recently finished aPhD at Cambridge University.
I have several yearsofexperience in analyzing real life data from different sources using data sciencetechniques 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 theredo not account for the multidimensional nature of the topic .
This course will give you a robust grounding in themainaspects 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
THE COURSE COMPOSES OF 7 SECTIONS TO HELP YOU MASTER PYTHON MACHINE LEARNING:
• A full introduction to Python Data Science andpowerful Python driven framework for data science, Anaconda • Getting started with Jupyter notebooks for implementing data science techniquesin Python • Data Structures and Reading in Pandas, including CSV, Excel andHTML 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 by the end of the course.
NO PRIOR PYTHON OR STATISTICS OR MACHINE LEARNING KNOWLEDGE IS REQUIRED:
You’ll start by absorbing the most valuable Python Data Science basics and techniques.
I useeasy-to-understand, hands-on methods to simplify and address even the most difficult concepts in Python.
My course willhelp youimplement the methods using real dataobtained from different sources.
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!
JOIN MY COURSE NOW!
