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Python for Machine Learning: The Complete Beginner's Course

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Python for Machine Learning: The Complete Beginner's Course, available at $59.99, has an average rating of 4.32, with 88 lectures, 1 quizzes, based on 1040 reviews, and has 117589 subscribers.

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You will learn about Learn Python programming and Scikit learn applied to machine learning regression Understand the underlying theory behind simple and multiple linear regression techniques Learn to solve regression problems (linear regression and logistic regression) Learn the theory and the practical implementation of logistic regression using sklearn Learn the mathematics behind decision trees Learn about the different algorithms for clustering This course is ideal for individuals who are Anyone who want to pursue a career in Machine Learning or Any Python programming enthusiast willing to add machine learning proficiency to their portfolio or Technologists who are curious about how Machine Learning works in the real world or Programmers who are looking to add machine learning to their skillset It is particularly useful for Anyone who want to pursue a career in Machine Learning or Any Python programming enthusiast willing to add machine learning proficiency to their portfolio or Technologists who are curious about how Machine Learning works in the real world or Programmers who are looking to add machine learning to their skillset.

Enroll now: Python for Machine Learning: The Complete Beginner's Course

Summary

Title: Python for Machine Learning: The Complete Beginner's Course

Price: $59.99

Average Rating: 4.32

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: $54.99

Quality Status: approved

Status: Live

What You Will Learn

  • Learn Python programming and Scikit learn applied to machine learning regression
  • Understand the underlying theory behind simple and multiple linear regression techniques
  • Learn to solve regression problems (linear regression and logistic regression)
  • Learn the theory and the practical implementation of logistic regression using sklearn
  • Learn the mathematics behind decision trees
  • Learn about the different algorithms for clustering

Who Should Attend

  • Anyone who want to pursue a career in Machine Learning
  • Any Python programming enthusiast willing to add machine learning proficiency to their portfolio
  • Technologists who are curious about how Machine Learning works in the real world
  • Programmers who are looking to add machine learning to their skillset

Target Audiences

  • Anyone who want to pursue a career in Machine Learning
  • Any Python programming enthusiast willing to add machine learning proficiency to their portfolio
  • Technologists who are curious about how Machine Learning works in the real world
  • Programmers who are looking to add machine learning to their skillset

To understand how organizations like Google, Amazon, and even Udemy use machine learning and artificial intelligence (AI) to extract meaning and insights from enormous data sets, this machine learning course will provide you with the essentials. According to Glassdoor and Indeed, data scientists earn an average income of $120,000, and that is just the norm! 

When it comes to being attractive, data scientists are already there. In a highly competitive job market, it is tough to keep them after they have been hired. People with a unique mix of scientific training, computer expertise, and analytical abilities are hard to find.

Like the Wall Street “quants” of the 1980s and 1990s, modern-day data scientists are expected to have a similar skill set. People with a background in physics and mathematics flocked to investment banks and hedge funds in those days because they could come up with novel algorithms and data methods.

That being said, data science is becoming one of the most well-suited occupations for success in the twenty-first century. It is computerized, programming-driven, and analytical in nature. Consequently, it comes as no surprise that the need for data scientists has been increasing in the employment market over the last several years.

The supply, on the other hand, has been quite restricted. It is challenging to get the knowledge and abilities required to be recruited as a data scientist.

In this course, mathematical notations and jargon are minimized, each topic is explained in simple English, making it easier to understand. Once you’ve gotten your hands on the code, you’ll be able to play with it and build on it. The emphasis of this course is on understanding and using these algorithms in the real world, not in a theoretical or academic context. 

You’ll walk away from each video with a fresh idea that you can put to use right away!

All skill levels are welcome in this course, and even if you have no prior statistical experience, you will be able to succeed!

Course Curriculum

Chapter 1: Introduction to Machine Learning

Lecture 1: What is Machine Learning?

Lecture 2: Applications of Machine Learning

Lecture 3: Machine learning Methods

Lecture 4: What is Supervised learning?

Lecture 5: What is Unsupervised learning?

Lecture 6: Supervised learning vs Unsupervised learning

Lecture 7: Course Materials

Chapter 2: Optional: Setting Up Python & ML Algorithms Implementation

Lecture 1: Introduction

Lecture 2: Python libraries for Machine Learning

Lecture 3: Setting up Python

Lecture 4: What is Jupyter?

Lecture 5: Anaconda Installation Windows Mac and Ubuntu

Lecture 6: Implementing Python in Jupyter

Lecture 7: Managing Directories in Jupyter Notebook

Chapter 3: Simple Linear Regression

Lecture 1: Introduction to regression

Lecture 2: How Does Linear Regression Work?

Lecture 3: Line representation

Lecture 4: Implementation in python: Importing libraries & datasets

Lecture 5: Implementation in python: Distribution of the data

Lecture 6: Implementation in python: Creating a linear regression object

Chapter 4: Multiple Linear Regression

Lecture 1: Understanding Multiple linear regression

Lecture 2: Implementation in python: Exploring the dataset

Lecture 3: Implementation in python: Encoding Categorical Data

Lecture 4: Implementation in python: Splitting data into Train and Test Sets

Lecture 5: Implementation in python: Training the model on the Training set

Lecture 6: Implementation in python: Predicting the Test Set results

Lecture 7: Evaluating the performance of the regression model

Lecture 8: Root Mean Squared Error in Python

Chapter 5: Classification Algorithms: K-Nearest Neighbors

Lecture 1: Introduction to classification

Lecture 2: K-Nearest Neighbors algorithm

Lecture 3: Example of KNN

Lecture 4: K-Nearest Neighbours (KNN) using python

Lecture 5: Implementation in python: Importing required libraries

Lecture 6: Implementation in python: Importing the dataset

Lecture 7: Implementation in python: Splitting data into Train and Test Sets

Lecture 8: Implementation in python: Feature Scaling

Lecture 9: Implementation in python: Importing the KNN classifier

Lecture 10: Implementation in python: Results prediction & Confusion matrix

Chapter 6: Classification Algorithms: Decision Tree

Lecture 1: Introduction to decision trees

Lecture 2: What is Entropy?

Lecture 3: Exploring the dataset

Lecture 4: Decision tree structure

Lecture 5: Implementation in python: Importing libraries & datasets

Lecture 6: Implementation in python: Encoding Categorical Data

Lecture 7: Implementation in python: Splitting data into Train and Test Sets

Lecture 8: Implementation in python: Results prediction & Accuracy

Chapter 7: Classification Algorithms: Logistic regression

Lecture 1: Introduction

Lecture 2: Implementation steps

Lecture 3: Implementation in python: Importing libraries & datasets

Lecture 4: Implementation in python: Splitting data into Train and Test Sets

Lecture 5: Implementation in python: Pre-processing

Lecture 6: Implementation in python: Training the model

Lecture 7: Implementation in python: Results prediction & Confusion matrix

Lecture 8: Logistic Regression vs Linear Regression

Chapter 8: Clustering

Lecture 1: Introduction to clustering

Lecture 2: Use cases

Lecture 3: K-Means Clustering Algorithm

Lecture 4: Elbow method

Lecture 5: Steps of the Elbow method

Lecture 6: Implementation in python

Lecture 7: Hierarchical clustering

Lecture 8: Density-based clustering

Lecture 9: Implementation of k-means clustering in python

Lecture 10: Importing the dataset

Lecture 11: Visualizing the dataset

Lecture 12: Defining the classifier

Lecture 13: 3D Visualization of the clusters

Lecture 14: 3D Visualization of the predicted values

Lecture 15: Number of predicted clusters

Chapter 9: Recommender System

Lecture 1: Introduction

Lecture 2: Collaborative Filtering in Recommender Systems

Lecture 3: Content-based Recommender System

Lecture 4: Implementation in python: Importing libraries & datasets

Lecture 5: Merging datasets into one dataframe

Lecture 6: Sorting by title and rating

Lecture 7: Histogram showing number of ratings

Lecture 8: Frequency distribution

Lecture 9: Jointplot of the ratings and number of ratings

Lecture 10: Data pre-processing

Lecture 11: Sorting the most-rated movies

Lecture 12: Grabbing the ratings for two movies

Lecture 13: Correlation between the most-rated movies

Lecture 14: Sorting the data by correlation

Lecture 15: Filtering out movies

Lecture 16: Sorting values

Lecture 17: Repeating the process for another movie

Chapter 10: Conclusion

Lecture 1: Conclusion

Chapter 11: BONUS Section – Don't Miss Out

Lecture 1: BONUS Section – Don't Miss Out

Instructors

Rating Distribution

  • 1 stars: 12 votes
  • 2 stars: 32 votes
  • 3 stars: 148 votes
  • 4 stars: 390 votes
  • 5 stars: 458 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!

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