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Harness The Power Of Machine Learning For Unsupervised & Supervised Learning In Python

What you will 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

Description

HERE IS WHY YOU SHOULD TAKE THIS COURSE:

This course your complete guide to both supervised & unsupervised learning using Python. This means, this course covers all the 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.

LEARN FROM AN EXPERT DATA SCIENTIST WITH +5 YEARS OF EXPERIENCE:

My name is Minerva Singh and I am an Oxford University MPhil (Geography and Environment) graduate. I also just 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

THE COURSE COMPOSES OF 7 SECTIONS TO HELP YOU MASTER PYTHON MACHINE LEARNING:


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• 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 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 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.

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!

English
language

Content

INTRODUCTION TO THE COURSE: The Key Concepts and Software Tools

Welcome to Clustering & Classification with Machine Learning in Python
What is Machine Learning?
Data and Scripts For the Course
Python Data Science Environment
For Mac Users
Introduction to IPython
IPython in Browser
Python Data Science Packages To Be Used

Read in Data From Different Sources With Pandas

What are Pandas?
Read in Data from CSV
Read in Online CSV
Read in Excel Data
Read in HTML Data
Read in Data from Databases

Data Cleaning & Munging

Remove Missing Values
Conditional Data Selection
Data Grouping
Data Subsetting
Ranking & Sorting
Concatenate
Merging & Joining Data Frames

Unsupervised Learning in Python

Unsupervised Classification- Some Basic Concepts
K-Means Clustering:Theory
Implement K-Means on the Iris Data
Quantifying K-Means Clustering Performance
K-Means Clustering with Real Data
How To Select the Optimal Number of Clusters?
Gaussian Mixture Modelling (GMM)
Hierarchical Clustering-theory
Hierarchical Clustering-practical

Dimension Reduction & Feature Selection for Machine Learning

Principal Component Analysis (PCA)-Theory
Principal Component Analysis (PCA)-Case Study 1
Principal Component Analysis (PCA)-Case Study 2
Linear Discriminant Analysis(LDA) for Dimension Reduction
t-SNE Dimension Reduction
Feature Selection to Select the Most Relevant Predictors
Recursive Feature Elimination (RFE)

Supervised Learning: Classification

Concepts Behind Supervised Learning
Data Preparation for Supervised Learning
Pointers on Evaluating the Accuracy of Classification Modelling
Using Logistic Regression as a Classification Model
kNN- Classification
Naive Bayes Classification
Linear Discriminant Analysis
SVM- Linear Classification
Non-Linear SVM Classification
RF-Classification
Gradient Boosting Machine (GBM)
Voting Classifier

Neural Networks and Deep Learning Based Classification Techniques

Perceptrons for Binary Classification
Artificial Neural Networks (ANN) for Binary Classification
Multi-class Classification With MLP
Introduction to H20
Use H20 for Deep Learning Classification
Specify the Activation Function
H20 Deep Learning for Classification

Miscellaneous Information

Using Colabs for Online Jupyter Notebooks