Clustering & Classification With Machine Learning In Python

Clustering & Classification With Machine Learning In Python, available at $54.99, has an average rating of 4.4, with 60 lectures, based on 396 reviews, and has 8093 subscribers.

You will learn about 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 This course is ideal for individuals who are Students Interested In Getting Started With Data Science Applications In The Python Environment or People Wanting To Master The Anaconda iPython Environment For Data Science & Scientific Computations or Students Wishing To Learn The Implementation Of Unsupervised Learning On Real Data Using Python or Students Wishing To Learn The Implementation Of Supervised Learning (Classification) On Real Data Using Python or Students Looking To Get Started With Artificial Neural Networks & Deep Learning It is particularly useful for Students Interested In Getting Started With Data Science Applications In The Python Environment or People Wanting To Master The Anaconda iPython Environment For Data Science & Scientific Computations or Students Wishing To Learn The Implementation Of Unsupervised Learning On Real Data Using Python or Students Wishing To Learn The Implementation Of Supervised Learning (Classification) On Real Data Using Python or Students Looking To Get Started With Artificial Neural Networks & Deep Learning.

Enroll now: Clustering & Classification With Machine Learning In Python

Summary

Title: Clustering & Classification With Machine Learning In Python

Price: $54.99

Average Rating: 4.4

Number of Lectures: 60

Number of Published Lectures: 60

Number of Curriculum Items: 60

Number of Published Curriculum Objects: 60

Original Price: $199.99

Quality Status: approved

Status: Live

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

Who Should Attend

Students Interested In Getting Started With Data Science Applications In The Python Environment
People Wanting To Master The Anaconda iPython Environment For Data Science & Scientific Computations
Students Wishing To Learn The Implementation Of Unsupervised Learning On Real Data Using Python
Students Wishing To Learn The Implementation Of Supervised Learning (Classification) On Real Data Using Python
Students Looking To Get Started With Artificial Neural Networks & Deep Learning

Target Audiences

Students Interested In Getting Started With Data Science Applications In The Python Environment
People Wanting To Master The Anaconda iPython Environment For Data Science & Scientific Computations
Students Wishing To Learn The Implementation Of Unsupervised Learning On Real Data Using Python
Students Wishing To Learn The Implementation Of Supervised Learning (Classification) On Real Data Using Python
Students Looking To Get Started With Artificial Neural Networks & Deep Learning

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:

• 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!

Course Curriculum

Chapter 1: INTRODUCTION TO THE COURSE: The Key Concepts and Software Tools

Lecture 1: Welcome to Clustering & Classification with Machine Learning in Python

Lecture 2: What is Machine Learning?

Lecture 3: Data and Scripts For the Course

Lecture 4: Python Data Science Environment

Lecture 5: For Mac Users

Lecture 6: Introduction to IPython

Lecture 7: IPython in Browser

Lecture 8: Python Data Science Packages To Be Used

Chapter 2: Read in Data From Different Sources With Pandas

Lecture 1: What are Pandas?

Lecture 2: Read in Data from CSV

Lecture 3: Read in Online CSV

Lecture 4: Read in Excel Data

Lecture 5: Read in HTML Data

Lecture 6: Read in Data from Databases

Chapter 3: Data Cleaning & Munging

Lecture 1: Remove Missing Values

Lecture 2: Conditional Data Selection

Lecture 3: Data Grouping

Lecture 4: Data Subsetting

Lecture 5: Ranking & Sorting

Lecture 6: Concatenate

Lecture 7: Merging & Joining Data Frames

Chapter 4: Unsupervised Learning in Python

Lecture 1: Unsupervised Classification- Some Basic Concepts

Lecture 2: K-Means Clustering:Theory

Lecture 3: Implement K-Means on the Iris Data

Lecture 4: Quantifying K-Means Clustering Performance

Lecture 5: K-Means Clustering with Real Data

Lecture 6: How To Select the Optimal Number of Clusters?

Lecture 7: Gaussian Mixture Modelling (GMM)

Lecture 8: Hierarchical Clustering-theory

Lecture 9: Hierarchical Clustering-practical

Chapter 5: Dimension Reduction & Feature Selection for Machine Learning

Lecture 1: Principal Component Analysis (PCA)-Theory

Lecture 2: Principal Component Analysis (PCA)-Case Study 1

Lecture 3: Principal Component Analysis (PCA)-Case Study 2

Lecture 4: Linear Discriminant Analysis(LDA) for Dimension Reduction

Lecture 5: t-SNE Dimension Reduction

Lecture 6: Feature Selection to Select the Most Relevant Predictors

Lecture 7: Recursive Feature Elimination (RFE)

Chapter 6: Supervised Learning: Classification

Lecture 1: Concepts Behind Supervised Learning

Lecture 2: Data Preparation for Supervised Learning

Lecture 3: Pointers on Evaluating the Accuracy of Classification Modelling

Lecture 4: Using Logistic Regression as a Classification Model

Lecture 5: kNN- Classification

Lecture 6: Naive Bayes Classification

Lecture 7: Linear Discriminant Analysis

Lecture 8: SVM- Linear Classification

Lecture 9: Non-Linear SVM Classification

Lecture 10: RF-Classification

Lecture 11: Gradient Boosting Machine (GBM)

Lecture 12: Voting Classifier

Chapter 7: Neural Networks and Deep Learning Based Classification Techniques

Lecture 1: Perceptrons for Binary Classification

Lecture 2: Artificial Neural Networks (ANN) for Binary Classification

Lecture 3: Multi-class Classification With MLP

Lecture 4: Introduction to H20

Lecture 5: Use H20 for Deep Learning Classification

Lecture 6: Specify the Activation Function

Lecture 7: H20 Deep Learning for Classification

Chapter 8: Miscellaneous Information

Lecture 1: Using Colabs for Online Jupyter Notebooks

Lecture 2: Colab GPU

Lecture 3: Github

Lecture 4: What Is Data Science?

Instructors

Minerva Singh
Bestselling Instructor & Data Scientist(Cambridge Uni)

Rating Distribution

1 stars: 16 votes
2 stars: 22 votes
3 stars: 64 votes
4 stars: 87 votes
5 stars: 207 votes

Frequently Asked Questions

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