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Machine Learning with Python – Complete Course & Projects

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Machine Learning with Python – Complete Course & Projects, available at $74.99, has an average rating of 4.55, with 50 lectures, based on 64 reviews, and has 6058 subscribers.

You will learn about Learn Data Science Learn the theories behind the Machine Learning Algorithms Learn applying the Machine Learning Algorithms in Python Learn feature engineering Learn Python fundamentals Learn Data Analysis This course is ideal for individuals who are People who wants to learn Machine Learning or People who wants to learn Python It is particularly useful for People who wants to learn Machine Learning or People who wants to learn Python.

Enroll now: Machine Learning with Python – Complete Course & Projects

Summary

Title: Machine Learning with Python – Complete Course & Projects

Price: $74.99

Average Rating: 4.55

Number of Lectures: 50

Number of Published Lectures: 50

Number of Curriculum Items: 50

Number of Published Curriculum Objects: 50

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

  • Learn Data Science
  • Learn the theories behind the Machine Learning Algorithms
  • Learn applying the Machine Learning Algorithms in Python
  • Learn feature engineering
  • Learn Python fundamentals
  • Learn Data Analysis

Who Should Attend

  • People who wants to learn Machine Learning
  • People who wants to learn Python

Target Audiences

  • People who wants to learn Machine Learning
  • People who wants to learn Python

Welcome to the Machine Learning in Python – Theory and Implementation course. This course aims to teach students the machine learning algorithms by simplfying how they work on theory and the application of the machine learning algorithms in Python. Course starts with the basics of Python and after that machine learning concepts like evaluation metrics or feature engineering topics are covered in the course. Lastly machine learning algorithms are covered. By taking this course you are going to have the knowledge of how machine learning algorithms work and you are going to be able to apply the machine learning algorithms in Python. We are going to be covering python fundamentals, pandas, feature engineering, machine learning evaluation metrics, train test split and machine learning algorithms in this course. Course outline is

  • Python Fundamentals

  • Pandas Library

  • Feature Engineering

  • Evaluation of Model Performances

  • Supervised vs Unsupervised Learning

  • Machine Learning Algorithms

The machine learning algorithms that are going to be covered in this course is going to be Linear Regression, Logistic Regression, K-Nearest Neighbors, Support Vector Machines, Decision Tree, Random Forests and K-Means Clustering. If you are interested in Machine Learning and want to learn the algorithms theories and implementations in Python you can enroll into the course. You can always ask questions from course Q&A section. Thanks for reading the course description, have a nice day.

Course Curriculum

Chapter 1: Pandas

Lecture 1: Pandas part 1

Lecture 2: Pandas part 2

Lecture 3: Pandas Coding 1

Lecture 4: Pandas Coding 2

Chapter 2: Numpy

Lecture 1: Numpy – Introduction to Arrays

Lecture 2: Array Indexing

Lecture 3: Array Slicing and Array Iterating

Chapter 3: Feature Engineering

Lecture 1: Feature Scaling

Lecture 2: Feature Scaling in Python

Lecture 3: Label Encoding

Lecture 4: One Hot Encoding

Lecture 5: Outlier Detection

Chapter 4: Evaluation of the Model Performances

Lecture 1: Train-Test Split

Lecture 2: MSE – RMSE

Lecture 3: Confusion Matrix – Accuracy Score

Chapter 5: Machine Learning – Supervised vs Unsupervised

Lecture 1: Supervised vs Unsupervised Machine Learning

Chapter 6: Data Set Analysis & Feature Engineering for Regression Tasks

Lecture 1: Data Set

Lecture 2: EDA

Lecture 3: Feature Engineering

Chapter 7: Data Set Analysis & Feature Engineering for Classification Tasks

Lecture 1: Data Set

Lecture 2: EDA

Lecture 3: Feature Engineering

Chapter 8: Supervised Learning

Lecture 1: Linear Regression

Lecture 2: Linear Regression 2

Lecture 3: Linear Regression 3

Lecture 4: Linear Regression Coding

Lecture 5: Logistic Regression

Lecture 6: Logistic Regression Coding

Lecture 7: K Nearest Neighbors

Lecture 8: K-Nearest Neighbors Coding (Elbow Method)

Lecture 9: K-Nearest Neighbors Coding

Lecture 10: Support Vector Machines

Lecture 11: Support Vector Classifier Coding

Lecture 12: Support Vector Regression Coding

Lecture 13: Decision Tree

Lecture 14: Decision Tree Coding

Lecture 15: Random Forest

Lecture 16: Random Forest Regression Coding

Lecture 17: Random Forest Classification Coding

Chapter 9: Unsupervised Learning

Lecture 1: K-means Clustering

Lecture 2: K-means Clustering Coding

Chapter 10: Lets apply what we learned – Machine Learning Project: Classification

Lecture 1: Data Set

Lecture 2: Data Analysis

Lecture 3: Data Analysis II & Feature Engineering

Lecture 4: Machine Learning

Chapter 11: Lets apply what we learned – Machine Learning Project: Regression

Lecture 1: Data Set

Lecture 2: Data Analysis

Lecture 3: Feature Engineering

Lecture 4: Machine Learning

Chapter 12: Bonus Section

Lecture 1: bonus lecture

Instructors

Rating Distribution

  • 1 stars: 0 votes
  • 2 stars: 3 votes
  • 3 stars: 6 votes
  • 4 stars: 26 votes
  • 5 stars: 29 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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