Artificial Intelligence: Advanced Machine Learning

Learn all the advanced skills you need to perform various real-world machine learning tasks in different environments.

What you'll learn
Extract features from categorical variables, text, and images
Solve real-world problems using machine learning techniques
Exploit the power of Python to handle data extraction, manipulation, and exploration techniques
Implement machine learning classification and regression algorithms from scratch in Python
Dive deep into the world of analytics to predict situations correctly
Predict the values of continuous variables
Classify documents and images using logistic regression and support vector machines
Create ensembles of estimators using bagging and boosting techniques
Evaluate the performance of machine learning systems in common tasks
Requirements
Knowledge of some undergraduate level mathematics would be an added advantage
Description
Data science and machine learning are some of the top buzzwords in the technical world today. Machine learning is the buzzword bringing computer science and statistics together to build smart and efficient models. Using powerful algorithms and techniques offered by machine learning you can automate any analytical model. 
Python is one of the most popular languages used for machine learning and arguably, the best entry point to the fascinating world of machine learning (ML). If you're interested to explore both the programming and machine learning world with python, then go for this course.
In this course, you will work through various examples on advanced algorithms, and focus a bit more on some visualization options. We’ll show you how to use random forest to predict what type of insurance a patient has based on their treatment and you will get an overview of how to use random forest/decision tree and examine the model. And then, we’ll walk you through the next example on letter recognition, where you will train a program to recognize letters using a support Vector machine, examine the results, and plot a confusion matrix. With the help of various projects included, you will find it intriguing to acquire the mechanics of several important machine learning algorithms – they are no more obscure as they thought. You will build systems that classify documents, recognize images, detect ads, and more. You will learn to use scikit-learn’s API to extract features from categorical variables, text and images; evaluate model performance, and develop an intuition for how to improve your model’s performance.
At the end of this course, you will master all required concepts of machine learning to build efficient models at work to carry out advanced tasks with the practical approach.

Overview

Section 1: Introduction

Lecture 1 Welcome

Section 2: Getting Started With This Course

Lecture 2 Set up the environment

Lecture 3 Machine Learning - Classification

Lecture 4 Machine Learning - Regression

Lecture 5 Machine Learning - Transformers

Lecture 6 Machine Learning - Clustering

Lecture 7 Machine Learning - Manifold Learning

Lecture 8 Machine Learning - Scikit-learn's estimator interface

Lecture 9 Machine Learning - Cross-Validation

Lecture 10 Machine Learning - Grid Searches

Section 3: Machine Learning - Model Complexity

Lecture 11 Introduction

Lecture 12 Linear models for regression

Lecture 13 Support Vector Machines

Lecture 14 Trees and Forests

Lecture 15 Learning Curves

Lecture 16 Validation Curves

Lecture 17 EstimatorCV Objects for Efficient Parameter Search

Section 4: Understanding Pipelines

Lecture 18 Pipelines - Motivation

Lecture 19 Pipeline Baiscs

Lecture 20 Cross Validation With Pipelines

Lecture 21 Using Pipelines with Grid-Search

Section 5: Machine Learning - Imbalanced Classes & Metrics

Lecture 22 Default metrics

Lecture 23 Classification Metrics

Lecture 24 Precision - Recall tradeoff and Area Under the Curve

Lecture 25 Built-In and custom scoring functions

Section 6: Machine Learning - Model Selection For Unsupervised Learning

Lecture 26 How to evaluate unsupervised models?

Lecture 27 Kernel Density Estimation

Lecture 28 Model Selection For Clustering

Section 7: Machine Learning - Handling Real Data

Lecture 29 Dealing with Real Data

Lecture 30 OneHotEncoder

Lecture 31 Encoding Features from Dictionaries

Lecture 32 Handling missing values

Section 8: Machine Learning - Dealing with Text Data

Lecture 33 Text Data Motivation

Lecture 34 Text Feature Extraction with Bag-of-Words

Lecture 35 Text Classification of Movie Reviews

Lecture 36 Text Classification continuation

Lecture 37 Text Feature Extraction Hashing Trick

Lecture 38 Vector Representations

Section 9: Machine Learning - Out Of Core Learning

Lecture 39 Out of Core and Online Learning

Lecture 40 The Partial Fit Interface

Lecture 41 Kernel Approximations

Lecture 42 Subsampling for supervised transformations

Lecture 43 Out of core text classification with the Hashing Vectorizer

Section 10: Course Summary

Lecture 44 Course Summary

Section 11: Code Files

Lecture 45 Working Files

Lecture 46 Thank You

The course is intended for both professionals and students.,Anyone who wants to learn advanced machine learning skills