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Machine Learning Algorithms: Basics to Advanced

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Machine Learning Algorithms: Basics to Advanced, available at $44.99, has an average rating of 3.81, with 88 lectures, 1 quizzes, based on 8 reviews, and has 109 subscribers.

You will learn about Master concepts involved in interacting with databases. Learn to apply multiple and different functions to dataframe columns. Implement the concept of exponentially weighted windows. Build awesome ML solutions for your business problems. Apply ML algorithms to design your own solution to business problems. Transform your weak models to strong models using boosting. Learn how to combine different types of model sequentially. This course is ideal for individuals who are Developers, aspiring Data Science Professionals 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. or Some programming knowledge in R or Python will be useful (some background about statistics). It is particularly useful for Developers, aspiring Data Science Professionals 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. or Some programming knowledge in R or Python will be useful (some background about statistics).

Enroll now: Machine Learning Algorithms: Basics to Advanced

Summary

Title: Machine Learning Algorithms: Basics to Advanced

Price: $44.99

Average Rating: 3.81

Number of Lectures: 88

Number of Quizzes: 1

Number of Published Lectures: 88

Number of Published Quizzes: 1

Number of Curriculum Items: 89

Number of Published Curriculum Objects: 89

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

  • Master concepts involved in interacting with databases.
  • Learn to apply multiple and different functions to dataframe columns.
  • Implement the concept of exponentially weighted windows.
  • Build awesome ML solutions for your business problems.
  • Apply ML algorithms to design your own solution to business problems.
  • Transform your weak models to strong models using boosting.
  • Learn how to combine different types of model sequentially.

Who Should Attend

  • Developers, aspiring Data Science Professionals 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.
  • Some programming knowledge in R or Python will be useful (some background about statistics).

Target Audiences

  • Developers, aspiring Data Science Professionals 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.
  • Some programming knowledge in R or Python will be useful (some background about statistics).

Are you really keen to learn some cool Machine Learning algorithms along with mastering advanced data analysis using financial examples in Pandas? Then this Course is for you!

To address the complex nature of various real-world data problems, specialized Machine Learning algorithms have been developed that solve these problems perfectly. On the other hand, the Ensemble is a powerful way to upgrade your model as it combines models and doesn’t assume a single model is the most accurate.

This well thought out sequential course takes a practical approach to Mastering Python Data Analysis with Pandas helping you exploring various Machine Learning algorithms to develop your own Ensemble Learning models and methods to use them efficiently. Then, you will learn how to pre-cluster your data to optimize and classify it for large datasets. Along with this, you will also focus on algorithms such as k-Nearest Neighbors, Naive Bayes, Decision Trees, Random Forest, k-Means, and much more. Finally, you will combine various models to achieve higher accuracy than base models can and develop robust models using the bagging technique.

Contents and Overview

This training program includes 3 complete courses, carefully chosen to give you the most comprehensive training possible.

The first course, Mastering Python Data Analysis with Pandas, you will learn how to apply Pandas to important but simple financial tasks such as modeling portfolios, calculating optimal portfolios based upon risk, and more. This video not only teaches you why Pandas is a great tool for solving real-world problems in quantitative finance, it also takes you meticulously through every step of the way, with practical, real-world examples, especially from the financial domain where Pandas is a popular choice. By the end of this video, you will be an expert in using the Pandas library for any data analysis problem, especially related to finance.

The second course, Machine Learning Algorithms in 7 Daysyou’ll 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.

The third course, Ensemble Machine Learning Techniques will show you how to combine various models to achieve higher accuracy than base models can. This has been the case in various contests such as Netflix and Kaggle, where the winning solutions used ensemble methods. If you want more than a superficial look at machine learning models and wish to build reliable models, then this course is for you.

About the Authors:

  • Prabhat Ranjan has extensive industry experience in Python, R, and Machine Learning. He has a passion for using Python, Pandas, and R for various new, real-time project scenarios. He is a passionate and experienced trainer when it comes to teaching concepts and advanced scenarios in Python, R, data science, and big data Hadoop.His teaching experience and strong industry expertise make him the best in this arena.

  • 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.

  • Arish Ali started his machine learning journey 5 years ago by winning an All-India machine learning competition conducted by the Indian Institute of Science and Microsoft. He worked as a data scientist at Mu Sigma, one of the biggest analytics firms in India. He has also worked on some cutting-edge problems in Multi-Touch Attribution Modeling, Market Mix Modeling, and Deep Neural Networks. He has also been an Adjunct faculty for Predictive Business Analytics at Bridge School of Management, which offers a course in Predictive Business Analytics along with North-western University (SPS). Currently, he is working at a mental health startup called Bemo as an AI developer where his role is to help automate the therapy provided to users and make it more personalized.

Course Curriculum

Chapter 1: Mastering Python Data Analysis with Pandas

Lecture 1: The Course Overview

Lecture 2: Reading and Writing Data in Text Format

Lecture 3: XML and HTML Web Scrapping

Lecture 4: Interacting with Databases

Lecture 5: Binary Data Formats (Excel and HDF5)

Lecture 6: Data Wrangling/ Munging and Pandas Data Structures

Lecture 7: Combining and Merging Data Sets

Lecture 8: Reshaping, Pivoting, and Advanced Indexing Data Sets

Lecture 9: Data Transformation on Data Sets

Lecture 10: String Manipulations on Data Sets

Lecture 11: Working with Missing Data Sets

Lecture 12: Data Aggregation on Data Sets

Lecture 13: Group-Wise Operations on Data Sets

Lecture 14: Statistical Functions Example

Lecture 15: Windows Functions Example

Lecture 16: Applying Multiple and Different Functions to Dataframe Columns

Lecture 17: Exponentially Weighted Windows

Chapter 2: Machine Learning Algorithms in 7 Days

Lecture 1: The Course Overview

Lecture 2: Introduction to Linear Regression

Lecture 3: Various concepts around Linear Regression

Lecture 4: Using Linear Regression for prediction

Lecture 5: Advantages and Limitations of Linear Regression

Lecture 6: Case Study – Linear Regression

Lecture 7: Introduction to Logistic Regression

Lecture 8: Various Concepts around Logistic Regression

Lecture 9: How Logistic Regression Can Be Used for Multi-Class Classification

Lecture 10: Advantages and Limitations of Logistic Regression

Lecture 11: Case Study – Logistic Regression

Lecture 12: Homework Assignment – Linear Models

Lecture 13: Introduction to Decision Tree

Lecture 14: Concepts – Various Decision Tree Algorithms

Lecture 15: Various Components of Decision Tree

Lecture 16: Advantages and Disadvantages of Decision Tree Algorithm

Lecture 17: Case Study – IBM’s HR Attrition Data

Lecture 18: Homework Assignment – Decision Tree Algorithm

Lecture 19: Introduction to Random Forest Algorithm

Lecture 20: Concepts of Random Forest Algorithm

Lecture 21: Various components of Random Forest Algorithm

Lecture 22: Advantages and Disadvantages of Random Forest Algorithm

Lecture 23: Case Study – IBM's HR Attrition Data

Lecture 24: Homework Assignment – Random Forest Algorithm

Lecture 25: Introduction to K-Means Clustering

Lecture 26: Concepts of K-Means Clustering Algorithm

Lecture 27: Different Clustering Methods

Lecture 28: Advantages and Disadvantages of K-Means Clustering Algorithm

Lecture 29: Case Study – Iris Dataset

Lecture 30: Homework Assignment – K-Means Clustering Algorithm

Lecture 31: Introduction to KNN Algorithm

Lecture 32: Concepts of KNN Algorithm

Lecture 33: Advantages and Limitations of KNN Algorithm

Lecture 34: Case Study – Income Census Dataset

Lecture 35: Homework Assignment – KNN Algorithm

Lecture 36: Introduction to Naïve Bayes Algorithm

Lecture 37: Concepts of Naïve Bayes Algorithm

Lecture 38: Advantages and Limitations of Naïve Bayes Algorithm

Lecture 39: Case Study – Bank Marketing Dataset

Lecture 40: Homework Assignment – Naïve Bayes Algorithm

Lecture 41: Introduction to Time Series Analysis

Lecture 42: Various Concepts around Time Series Model

Lecture 43: Full overview of ARIMA/ SARIMA Model

Lecture 44: Forecast Accuracy Measure – Time Series Analysis

Lecture 45: Case Study – CPI Inflation Dataset

Lecture 46: Homework Assignment – Time Series Analysis

Chapter 3: Ensemble Machine Learning Techniques

Lecture 1: The Course Overview

Lecture 2: Introduction to Ensemble Learning

Lecture 3: Setting Up Python

Lecture 4: Setting Up Dependencies

Lecture 5: Problems that Ensemble Learning Solves

Lecture 6: Ensemble Learning for Classification

Lecture 7: Implementing Ensemble Learning for Classification

Lecture 8: Ensemble Learning for Regression

Lecture 9: Implementing Ensemble Learning for Regression

Lecture 10: Basics of Bagging

Lecture 11: How Bagging Works

Lecture 12: Making Predictions on Movie Ratings Using SVM

Lecture 13: Random Forest

Lecture 14: Using Random Forest to Analyze Sonar Chirp Data

Lecture 15: Using the Decision Tree to Determine Weight at Birth

Lecture 16: Introduction to Boosting

Lecture 17: AdaBoost Algorithm

Lecture 18: Other Boosting Algorithms

Lecture 19: Predicting Churn Using Boosting

Lecture 20: Overview of Stacking Technique

Lecture 21: Implementing Blending in Python

Lecture 22: How to Use Stacking

Lecture 23: Practical Advice on Using Different Ensemble Learning Techniques

Lecture 24: Combining Different Ensemble Models Together

Lecture 25: Practical Example on Kaggle Competition

Instructors

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Frequently Asked Questions


How long do I have access to the course materials?


You can view and review the lecture materials indefinitely, like an on-demand channel.


Can I take my courses with me wherever I go?


Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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