## Clustering & Classification With Machine Learning In Python

### 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

### Requirements

Be Able To Operate & Install Software On A Computer

Prior Exposure To Common Machine Learning Terms Such As Unsupervised & Supervised Learning

### 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 PythonTHE 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!

### Overview

Section 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

Section 2: Read in Data From Different Sources With Pandas

Lecture 9 What are Pandas?

Lecture 10 Read in Data from CSV

Lecture 11 Read in Online CSV

Lecture 12 Read in Excel Data

Lecture 13 Read in HTML Data

Lecture 14 Read in Data from Databases

Section 3: Data Cleaning & Munging

Lecture 15 Remove Missing Values

Lecture 16 Conditional Data Selection

Lecture 17 Data Grouping

Lecture 18 Data Subsetting

Lecture 19 Ranking & Sorting

Lecture 20 Concatenate

Lecture 21 Merging & Joining Data Frames

Section 4: Unsupervised Learning in Python

Lecture 22 Unsupervised Classification- Some Basic Concepts

Lecture 23 K-Means Clustering:Theory

Lecture 24 Implement K-Means on the Iris Data

Lecture 25 Quantifying K-Means Clustering Performance

Lecture 26 K-Means Clustering with Real Data

Lecture 27 How To Select the Optimal Number of Clusters?

Lecture 28 Gaussian Mixture Modelling (GMM)

Lecture 29 Hierarchical Clustering-theory

Lecture 30 Hierarchical Clustering-practical

Section 5: Dimension Reduction & Feature Selection for Machine Learning

Lecture 31 Principal Component Analysis (PCA)-Theory

Lecture 32 Principal Component Analysis (PCA)-Case Study 1

Lecture 33 Principal Component Analysis (PCA)-Case Study 2

Lecture 34 Linear Discriminant Analysis(LDA) for Dimension Reduction

Lecture 35 t-SNE Dimension Reduction

Lecture 36 Feature Selection to Select the Most Relevant Predictors

Lecture 37 Recursive Feature Elimination (RFE)

Section 6: Supervised Learning: Classification

Lecture 38 Concepts Behind Supervised Learning

Lecture 39 Data Preparation for Supervised Learning

Lecture 40 Pointers on Evaluating the Accuracy of Classification Modelling

Lecture 41 Using Logistic Regression as a Classification Model

Lecture 42 kNN- Classification

Lecture 43 Naive Bayes Classification

Lecture 44 Linear Discriminant Analysis

Lecture 45 SVM- Linear Classification

Lecture 46 Non-Linear SVM Classification

Lecture 47 RF-Classification

Lecture 48 Gradient Boosting Machine (GBM)

Lecture 49 Voting Classifier

Section 7: Neural Networks and Deep Learning Based Classification Techniques

Lecture 50 Perceptrons for Binary Classification

Lecture 51 Artificial Neural Networks (ANN) for Binary Classification

Lecture 52 Multi-class Classification With MLP

Lecture 53 Introduction to H20

Lecture 54 Use H20 for Deep Learning Classification

Lecture 55 Specify the Activation Function

Lecture 56 H20 Deep Learning for Classification

Section 8: Miscellaneous Information

Lecture 57 Using Colabs for Online Jupyter Notebooks

Lecture 58 Colab GPU

Lecture 59 Github

Lecture 60 What Is Data Science?

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

#### Course Information:

Udemy | English | 6h 3m | 3.09 GB

Created by: Minerva Singh

You Can See More Courses in the Developer >> Greetings from CourseDown.com