Python for Machine Learning: The Complete Beginner's Course
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.
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
-
Meta Brains
Let's code & build the metaverse together! -
Skool of AI
Unlock Your AI Potential
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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