Machine Learning Course A Beginner Guide

Machine Learning Course – A Beginners Guide, available at $19.99, has an average rating of 4, with 161 lectures, 90 quizzes, based on 1 reviews, and has 16 subscribers.

You will learn about Understanding the basics of supervised and unsupervised learning Python libraries like Numpy, Pandas, etc. to analyze your data efficiently Linear Regression, Logistic Regression, and Decision Trees for building machine learning models Understand how to solve Classification and Regression problems using machine learning How to evaluate your machine learning models using the right evaluation metrics? Improve and enhance your machine learning model’s accuracy through feature engineering Projects covered – a) Customer Churn Prediction and b) NYC Taxi Trip Duration Prediction This course is ideal for individuals who are Beginners in Data Science It is particularly useful for Beginners in Data Science.

Enroll now: Machine Learning Course – A Beginners Guide

Summary

Title: Machine Learning Course – A Beginners Guide

Price: $19.99

Average Rating: 4

Number of Lectures: 161

Number of Quizzes: 90

Number of Published Lectures: 161

Number of Published Quizzes: 90

Number of Curriculum Items: 251

Number of Published Curriculum Objects: 251

Original Price: ?1,199

Quality Status: approved

Status: Live

What You Will Learn

Understanding the basics of supervised and unsupervised learning

Python libraries like Numpy, Pandas, etc. to analyze your data efficiently

Linear Regression, Logistic Regression, and Decision Trees for building machine learning models

Understand how to solve Classification and Regression problems using machine learning

How to evaluate your machine learning models using the right evaluation metrics?

Improve and enhance your machine learning model’s accuracy through feature engineering

Projects covered – a) Customer Churn Prediction and b) NYC Taxi Trip Duration Prediction

Who Should Attend

Beginners in Data Science

Target Audiences

Beginners in Data Science

Machine Learning is the science of teaching machines how to learn by themselves. Machine Learning is re-shaping and revolutionising the world and disrupting industries and job functions globally.

Machine learning is so extensive that you probably use it numerous times a day without even knowing it. From unlocking your mobile phones using your face to giving your attendance using a biometric machine, machine learning is being used in almost every stage.

In this age of machine learning, every aspiring data scientist is expected to up-skill themselves in machine learning techniques & tools and apply them in real-world business problems.

Machine Learning problems can be divided into 3 broad classes:

Supervised Machine Learning

Unsupervised Machine Learning

Reinforcement Learning

Supervised Machine Learning: When you have past data with outcomes (labels in machine learning terminology) and you want to predict the outcomes for the future – you would use Supervised Machine Learning algorithms. Supervised Machine Learning problems can again be divided into 2 kinds of problems:

Classification Problems: When you want to classify outcomes into different classes. For example – whether a customer would default on their loan or not is a classification problem which is of high interest to any Bank

Regression Problem: When you are interested in answering how much – these problems would fall under the Regression umbrella. For example – what is the expected amount of default from a customer is a Regression problem

Unsupervised Machine Learning: There are times when you don’t want to exactly predict an Outcome. You just want to perform a segmentation or clustering. For example – a bank would want to have a segmentation of its customers to understand their behavior. This is an Unsupervised Machine Learning problem as we are not predicting any outcomes here.

Reinforcement Learning: It is said to be the hope of true artificial intelligence. And it is rightly said so because the potential that Reinforcement Learning possesses is immense. It is a slightly complex topic as compared to traditional machine learning but an equally crucial one for the future.

Course Curriculum

Chapter 1: Introduction to Data Science and Machine Learning

Lecture 1: Overview of the Course

Lecture 2: Introduction

Lecture 3: Common Terminology used in Data Science

Lecture 4: Applications of Data Science

Chapter 2: Setting up your system

Lecture 1: Installation steps for Windows

Lecture 2: Installation steps for Linux

Lecture 3: Installation steps for Mac

Chapter 3: Introduction to Python

Lecture 1: Introduction to Python

Lecture 2: Introduction to Jupyter Notebook

Chapter 4: Variables and Data Types

Lecture 1: Introduction to Variables

Lecture 2: Implementing Variables in Python

Chapter 5: Operators

Lecture 1: Introduction to Operators

Lecture 2: Implementing Operators in Python

Chapter 6: Conditional Statements

Lecture 1: Introduction to Conditional Statements

Lecture 2: Implementing Conditional Statements in Python

Chapter 7: Looping Constructs

Lecture 1: Introduction to Looping Constructs

Lecture 2: Implementing Loops in Python

Lecture 3: Break, Continue and Pass Statements

Chapter 8: Data Structures

Lecture 1: Introduction to Data structures

Lecture 2: List and Tuple

Lecture 3: Implementing List in Python

Lecture 4: List- Project in Python

Lecture 5: Implementing Tuple in Python

Lecture 6: Introduction to sets

Lecture 7: Implementing Sets in Python

Lecture 8: Introduction to Dictionary

Lecture 9: Implementing Dictionary in Python

Chapter 9: String Manipulation

Lecture 1: Introduction to String Manipulation

Chapter 10: Functions

Lecture 1: Introductions to Functions

Lecture 2: Implementing Function in Python

Lecture 3: Lambda Expression

Lecture 4: Recursion

Lecture 5: Implementing Recursion in Python

Chapter 11: Module, Packages and Standard Libraries

Lecture 1: Introduction to Modules

Lecture 2: Modules: Intuition

Lecture 3: Introduction to Packages

Lecture 4: Standard Libraries in Python

Lecture 5: Unser Defined Libraries in Python

Chapter 12: Handling Text Files in Python

Lecture 1: Handling Text Files in Python

Chapter 13: Introduction to Python Libraries in Python

Lecture 1: Important Libraries of Data Science

Chapter 14: Python Libraries for Data Science

Lecture 1: Basics of Numpy in Python

Lecture 2: Basics of Scipy in Python

Lecture 3: Basics of Pandas in Python

Lecture 4: Basics of Matplotlib in Python

Lecture 5: Basics of Scikit-Learn in Python

Lecture 6: Basics of Statsmodels in Python

Chapter 15: Reading Data Files in Python

Lecture 1: Reading Data in Python

Lecture 2: Reading CSV files in Python

Lecture 3: Reading Big CSV Files in Python

Lecture 4: Reading Excel & Spreadsheet files in Python

Lecture 5: Reading Excel & Spreadsheet files in Python

Lecture 6: Reading JSON files in Python

Chapter 16: Preprocessing, Subsetting and Modifying Pandas Dataframes

Lecture 1: Subsetting and Modifying Data in Python

Lecture 2: Overview of Subsetting in Pandas I

Lecture 3: Overview of Subsetting in Pandas II

Lecture 4: Subsetting based on Position

Lecture 5: Subsetting based on Label

Lecture 6: Subsetting based on Value

Lecture 7: Modifying data in Pandas

Chapter 17: Sorting and Aggregating Data in Pandas

Lecture 1: Preprocessing, Sorting and Aggregating Data

Lecture 2: Sorting the Dataframe

Instructors

Analytics Vidhya
Data Science Community

Rating Distribution

1 stars: 0 votes

2 stars: 0 votes

3 stars: 0 votes

4 stars: 1 votes

5 stars: 0 votes

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!