Machine Learning Algorithms in 7 Days

Name: Machine Learning Algorithms in 7 Days
Rating: 4.0 (8 reviews)

Master the top 7 powerful and advanced algorithms and excel in Machine Learning

Created byPackt Publishing

Last updated 4/2019

English

What you'll learn

Build awesome ML solutions for your business problems
Easy and fast way to learn and use ML algorithms without being bothered about theoretical jargons
Apply ML algorithms to design your own solution to business problems
The course is updated and enhanced, and fully supports Python 3. This guarantees what you're learning is quite relevant for you today
Get to know seven ML algorithms in this concise, insightful guide

Course content

7 sections • 46 lectures • 5h 40m total length

The Course Overview3:36
This video gives an overview of this section.
Introduction to Linear Regression8:58
The video explains what Linear regression is and how does it work
• A brief overview about various components of Linear Regression
• Example showing the working logic of Linear Regression
• Example showing the working logic of Logistic Regression
Various concepts around Linear Regression7:12
The video explains the different concepts of Linear Regression
• Talk about the major steps on estimation and prediction in Linear Regression
• Explains how Linear Regression can deal with the overfitting issue
• Discuss different methods of regularization to deal with the overfitting issue for Linear Regression
Using Linear Regression for prediction14:23
The video discusses about various types of extension in Linear Regression
• Talks about various evaluation metrics of model’s performance for Linear Regression
Advantages and Limitations of Linear Regression3:44
This video discusses about the pros and cons of using Linear Regression
• Discuss the advantages of Linear Regression
• Note down the limitations of using Linear Regression
Case Study – Linear Regression19:46
The video talks about the case study on Linear Regression
• Overview the dataset
• Learn about the application of Linear Regression
• Talk about performance evaluation of the model
Introduction to Logistic Regression7:15
The video explains what Logistic Regression is and how does it work.
• A brief overview about the various components of Logistic Regression
• Explains why Linear Regression can’t be a suitable approach even for linear classification
• Example showing the working logic of Logistic Regression
Various Concepts around Logistic Regression7:30
The video explains the different concepts of Logistic Regression
• Talk about major steps on estimation and prediction in Logistic Regression
• Explain how Logistic Regression can deal with overfitting issue
• Explain different methods of regularization to deal with the overfitting issue for Logistic Regression
How Logistic Regression Can Be Used for Multi-Class Classification22:13
The video discusses about the various types of extension for multi-class classification exercise.
• Talk about the various evaluation metrics of the model’s performance for Logistic Regression
• Discuss about different types of Logistic Regression
• Learn how Logistic Regression can deal with the class imbalance problem
Advantages and Limitations of Logistic Regression3:18
This video discusses about the various pros and cons of Logistic Regression
• List down the advantages of Logistic Regression
• Discuss the cons on using Logistic Regression
Case Study – Logistic Regression22:56
The video talks about the case study on Logistic Regression using bank data
• Discuss how we can apply Logistic Regression to solve a binary classification exercise
• Look at the examples of dealing with class Imbalance if any
• Talk about the performance evaluation of the Logistic Regression
Homework Assignment – Linear Models6:55
The video aims at giving an assignment to the viewer.
• Explore the links mentioned

Introduction to Decision Tree4:57
The video explains what Decision Tree algorithm is and how does it work
• A brief overview about the Decision Tree algorithm
• Talk about the working logic
• Discuss an example of a fully-grown Decision Tree
Concepts - Various Decision Tree Algorithms5:36
The video explains the overall landscape of Decision Tree (DT) and various nuances of it.
• Overview the Decision Tree landscape
• Understand the splitting logic
• Explain how does DT deal with the issue of overfitting
Various Components of Decision Tree3:59
The video explains the different types of knobs or hyperparameters of the DT algorithm.
• Understand the various knobs
• Overview of the method - CHAID
• An example to discuss in detail the working logic of DT
Advantages and Disadvantages of Decision Tree Algorithm4:20
The video explains the various advantages and disadvantages of the DT algorithm.
• Discuss about the wide range of applications of Decision Tree in real world
• Learn how does DT deal with class imbalance problem
Case Study – IBM’s HR Attrition Data24:47
The video talks about the case study on DT using Customer Attrition data
• Overview the dataset and discuss the basic EDA
• Look at model development and model validation
• Visualize the Decision Tree
Homework Assignment – Decision Tree Algorithm1:46
The video aims at giving an assignment to the viewer.
• Explore the links mentioned

Introduction to Random Forest Algorithm5:03
The video explains what is Random Forest (RF) algorithm and how does it work.
• A brief overview about the RF algorithm
• Talk about the working logic
• Look at an example of classification and regression tree
Concepts of Random Forest Algorithm5:26
The video explains the different steps in RF algorithm
• Get introduced to OOB error in the context of RF algorithm
• Understand the splitting logic
• Look at an illustrative example of RF
Various components of Random Forest Algorithm5:39
The video explains different types of knobs or hyperparameters of the RF algorithm.
• Discuss various Knobs
• Overview of probability calibration
• Discuss the missing value imputation using RF and various applications of RF in real world
Advantages and Disadvantages of Random Forest Algorithm4:02
The video explains various advantage and disadvantages of the RF algorithm.
• Discuss about various model evaluation metrics
Case Study - IBM's HR Attrition Data12:15
The video talks about the case study on RF using Customer Attrition Data
• Get an overview on the dataset
• Learn about model development and validation
• Discuss the multi-variate analysis
Homework Assignment – Random Forest Algorithm1:52
The video aims at giving an assignment to the viewer.
• Explore the links mentioned

Introduction to K-Means Clustering4:43
The video explains what is K-Means algorithm and how does it work.
• A brief overview about the K-Means algorithm
• Talks about the working logic
• Example showing the working logic of K-Means algorithm
Concepts of K-Means Clustering Algorithm6:17
The video explains the different concepts in K-Means algorithm.
• Talks about major data processing steps before applying K-Means algorithm
• Illustrative Example of K-Means algorithm
• Explains different methods to decide optimal value of K
Different Clustering Methods4:32
The video explains different types of clustering methods other than K-Means.
• Talks about model-based clustering approaches like GMM etc
• Provides a quick introduction of different other types of clustering approaches
• Clustering in presence of mixed /categorical input features
Advantages and Disadvantages of K-Means Clustering Algorithm1:04
The video explains various advantages and disadvantages of the K-Means algorithm.
• Discuss the advantages of K-Means
• Look at the cons of using K-Means
Case Study – Iris Dataset11:45
The video talks about the case study on K-Means using Iris Dataset
• Dataset Overview
• Model-based clustering algorithm
• Comparison of various types of clustering approaches
Homework Assignment - K-Means Clustering Algorithm1:17
The video gives an exercise based on the theory discussed in previous videos
• Explore the exercise given

Introduction to KNN Algorithm4:05
The video explains what KNN algorithm is and how does it work.
• A brief overview about the KNN algorithm
• Talks about the working logic
• Discuss an example showing the working logic of KNN algorithm
Concepts of KNN Algorithm5:52
The video explains the different concepts in KNN algorithm
• Talks about major data processing steps before applying KNN Algorithm
• Learn how to choose the optimal value of K
• Discuss different methods to choose neighbors in KNN algorithm
Advantages and Limitations of KNN Algorithm2:36
The video explains various advantage and disadvantages of the KM algorithm.
• Discuss the advantages of using KNN algorithm
• List down the cons of using KNN algorithm
Case Study – Income Census Dataset15:35
The video talks about the case study on KNN using Income Census Dataset
• Overview the dataset and basic EDA
• Look at hyper-parameter tuning to obtain the best result for the dataset
• Compare the performance through various classification matrices
Homework Assignment – KNN Algorithm1:52
The video talks about the assignment given to the viewers.
• Explore additional knowledge on KNN

Introduction to Naïve Bayes Algorithm3:04
The video explains what Naïve Bayes algorithm is and how does it work.
• A brief overview about the Naïve Bayes algorithm
• Talk about the two building blocks like what is Bayes rule
• Look at an example showing the working logic of Naïve Bayes algorithm
Concepts of Naïve Bayes Algorithm5:42
The video explains the different concepts in Naïve Bayes algorithm.
• Talk about major concepts like likelihood, prior probability, posterior probability in the context of Naïve Bayes algorithm
• Understand what "Naïve" in Naïve Bayes algorithm is
• Discuss various types of Naïve Bayes algorithms
Advantages and Limitations of Naïve Bayes Algorithm3:02
The video explains various advantages and disadvantages of the Naïve Bayes algorithm.
• Discuss the advantages of Naïve Bayes algorithm
• List down the cons of using Naïve Bayes algorithm
Case Study – Bank Marketing Dataset14:16
The video talks about the case study on Naïve Bayes algorithm using the bank marketing dataset.
• Overview the dataset and understand the basic EDA
• Discuss the applications of Naïve Bayes algorithm
Homework Assignment - Naïve Bayes Algorithm4:34
In this video, you are asked to work on an exercise based on the concepts and NB methods learned in previous videos.
• Try other Naïve Bayes methods and compare the solutions

Introduction to Time Series Analysis3:16
The video explains what Time Series Analysis and its various components are.
• A brief overview about the Time Series analysis
• Talks about various key components of Time Series data
• Look at an example showing how various components can be combined to represent the overall Time Series
Various Concepts around Time Series Model4:38
The video explains key mathematical concepts of Time Series model.
• Talk about major concepts such as auto-correlation, stationarity, and so on
Full overview of ARIMA/ SARIMA Model8:28
The video explains major steps to build an ARIMA / SARIMA Model.
• Choose the parameters for an ARIMA model
• Discuss the overall flow of the ARIMA model
Forecast Accuracy Measure – Time Series Analysis3:23
This video aims at discussing some important measures of Forecast accuracy.
• List down the important measure of Forecast accuracy
• Look at the different types of Forecasting model
Case Study – CPI Inflation Dataset18:43
The video talks about the case study on Time Series Analysis.
• Overview the dataset
• Basic exploration of the data
• Build ARIMA/SARIMA model using the statsmodels package in Python
Homework Assignment - Time Series Analysis3:58
This video aims at giving an exercise to the viewers based on the concepts learned in previous videos.
• Go through the link mentioned in the video
• Apply Holt-Winter’s Exponential smoothing method to derive forecast for the series

Requirements

Basic Knowledge of Python and some background about statistics.

Description

Are you really keen to learn some cool machine learning algorithms that are making headlines these days? Machine learning applications are highly automated and self-modifying, and they continue to improve over time with minimal human intervention as they learn with more data. To address the complex nature of various real-world data problems, specialized machine learning algorithms have been developed that solve these problems perfectly.

This course offers an easy gateway to learn about 7 key algorithms in the realm of Data Science and Machine Learning. You will learn how to pre-cluster your data to optimize and classify it for large datasets. You will then find out how to predict data based on existing trends in your datasets.

This video addresses problems related to accurate and efficient data classification and prediction. Over the course of 7 days, you will be introduced to seven algorithms, along with exercises that will help you learn different aspects of machine learning. This course covers algorithms such as: k-Nearest Neighbors, Naive Bayes, Decision Trees, Random Forest, k-Means, Regression, and Time-Series.

On completion of the course, you will understand which machine learning algorithm to pick for clustering, classification, or regression and which is best suited for your problem. You will be able to easily and confidently build and implement data science algorithms.

About the Author

Shovon Sengupta is an experienced data scientist with over 10 years' experience in advanced predictive analytics, machine learning, deep learning, and reinforcement learning. He has worked extensively in designing award winning solutions for various organizations, for different business problems in the realm of Finance. Currently, he works as Senior Lead Data Scientist at one of the leading NBFCs in USA.

Shovon holds an MS in Advanced Econometrics from one of the leading universities in India.

Who this course is for:

This course is for aspiring data science professionals who are familiar with Python and have some background about statistics. It is ideal for developers who are currently implementing one or two data science algorithms and want to learn more to expand their skillset. This course will be a great enabler for those who aspire to master some of the most relevant and oft-used algorithms in Machine Learning.

Machine Learning Algorithms in 7 Days

What you'll learn

Explore related topics

Course content

Linear Models12 lectures • 2hr 8min

Decision Tree Algorithm6 lectures • 45min

Random Forest Algorithm6 lectures • 34min

K-Means Clustering Algorithm6 lectures • 30min

K-Nearest Neighbors Algorithm5 lectures • 30min

Naïve Bayes Algorithm5 lectures • 31min

Time Series Analysis6 lectures • 42min

Requirements

Description

Who this course is for: