The Complete Machine Learning Course with Python
The Complete Machine Learning Course with Python, available at $94.99, has an average rating of 4.49, with 203 lectures, 3 quizzes, based on 7393 reviews, and has 41274 subscribers.
You will learn about Machine Learning Engineers earn on average $166,000 – become an ideal candidate with this course! Solve any problem in your business, job or personal life with powerful Machine Learning models Train machine learning algorithms to predict house prices, identify handwriting, detect cancer cells & more Go from zero to hero in Python, Seaborn, Matplotlib, Scikit-Learn, SVM, unsupervised Machine Learning etc This course is ideal for individuals who are Anyone willing and interested to learn machine learning algorithm with Python or Any one who has a deep interest in the practical application of machine learning to real world problems or Anyone wishes to move beyond the basics and develop an understanding of the whole range of machine learning algorithms or Any intermediate to advanced EXCEL users who is unable to work with large datasets or Anyone interested to present their findings in a professional and convincing manner or Anyone who wishes to start or transit into a career as a data scientist or Anyone who wants to apply machine learning to their domain It is particularly useful for Anyone willing and interested to learn machine learning algorithm with Python or Any one who has a deep interest in the practical application of machine learning to real world problems or Anyone wishes to move beyond the basics and develop an understanding of the whole range of machine learning algorithms or Any intermediate to advanced EXCEL users who is unable to work with large datasets or Anyone interested to present their findings in a professional and convincing manner or Anyone who wishes to start or transit into a career as a data scientist or Anyone who wants to apply machine learning to their domain.
Enroll now: The Complete Machine Learning Course with Python
Summary
Title: The Complete Machine Learning Course with Python
Price: $94.99
Average Rating: 4.49
Number of Lectures: 203
Number of Quizzes: 3
Number of Published Lectures: 111
Number of Curriculum Items: 206
Number of Published Curriculum Objects: 111
Original Price: $199.99
Quality Status: approved
Status: Live
What You Will Learn
- Machine Learning Engineers earn on average $166,000 – become an ideal candidate with this course!
- Solve any problem in your business, job or personal life with powerful Machine Learning models
- Train machine learning algorithms to predict house prices, identify handwriting, detect cancer cells & more
- Go from zero to hero in Python, Seaborn, Matplotlib, Scikit-Learn, SVM, unsupervised Machine Learning etc
Who Should Attend
- Anyone willing and interested to learn machine learning algorithm with Python
- Any one who has a deep interest in the practical application of machine learning to real world problems
- Anyone wishes to move beyond the basics and develop an understanding of the whole range of machine learning algorithms
- Any intermediate to advanced EXCEL users who is unable to work with large datasets
- Anyone interested to present their findings in a professional and convincing manner
- Anyone who wishes to start or transit into a career as a data scientist
- Anyone who wants to apply machine learning to their domain
Target Audiences
- Anyone willing and interested to learn machine learning algorithm with Python
- Any one who has a deep interest in the practical application of machine learning to real world problems
- Anyone wishes to move beyond the basics and develop an understanding of the whole range of machine learning algorithms
- Any intermediate to advanced EXCEL users who is unable to work with large datasets
- Anyone interested to present their findings in a professional and convincing manner
- Anyone who wishes to start or transit into a career as a data scientist
- Anyone who wants to apply machine learning to their domain
The Complete Machine Learning Course in Python has been FULLY UPDATED for November 2019!
With brand new sections as well as updated and improved content, you get everything you need to master Machine Learning in one course! The machine learning field is constantly evolving, and we want to make sure students have the most up-to-date information and practices available to them:
Brand new sections include:
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Foundations of Deep Learning covering topics such as the difference between classical programming and machine learning, differentiate between machine and deep learning, the building blocks of neural networks, descriptions of tensor and tensor operations, categories of machine learning and advanced concepts such as over- and underfitting, regularization, dropout, validation and testing and much more.
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Computer Vision in the form of Convolutional Neural Networks covering building the layers, understanding filters / kernels, to advanced topics such as transfer learning, and feature extractions.
And the following sections have all been improved and added to:
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All the codes have been updated to work with Python 3.6 and 3.7
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The codes have been refactored to work with Google Colab
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Deep Learning and NLP
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Binary and multi-class classifications with deep learning
Get the most up to date machine learning information possible, and get it in a single course!
* * *
The average salary of a Machine Learning Engineer in the US is $166,000! By the end of this course, you will have a Portfolio of 12 Machine Learning projects that will help you land your dream job or enable you to solve real life problems in your business, job or personal life with Machine Learning algorithms.
Come learn Machine Learning with Python this exciting course with Anthony NG, a Senior Lecturer in Singaporewho has followed Rob Percival’s “project based” teaching style to bring you this hands-on course.
With over 18 hours of content and more than fifty 5 star ratings, it’s already the longest and best rated Machine Learning course on Udemy!
Build Powerful Machine Learning Models to Solve Any Problem
You’ll go from beginner to extremely high-level and your instructor will build each algorithm with you step by step on screen.
By the end of the course, you will have trained machine learning algorithms to classify flowers, predict house price, identify handwritings or digits, identify staff that is most likely to leave prematurely, detect cancer cells and much more!
Inside the course, you’ll learn how to:
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Gain complete machine learning tool sets to tackle most real world problems
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Understand the various regression, classification and other ml algorithms performance metrics such as R-squared, MSE, accuracy, confusion matrix, prevision, recall, etc. and when to use them.
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Combine multiple models with by bagging, boosting or stacking
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Make use to unsupervised Machine Learning (ML) algorithms such as Hierarchical clustering, k-means clustering etc. to understand your data
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Develop in Jupyter (IPython) notebook, Spyder and various IDE
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Communicate visually and effectively with Matplotlib and Seaborn
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Engineer new features to improve algorithm predictions
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Make use of train/test, K-fold and Stratified K-fold cross validation to select correct model and predict model perform with unseen data
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Use SVM for handwriting recognition, and classification problems in general
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Use decision trees to predict staff attrition
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Apply the association rule to retail shopping datasets
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And much much more!
No Machine Learning required. Although having some basic Python experience would be helpful, no prior Python knowledge is necessary as all the codes will be provided and the instructor will be going through them line-by-line and you get friendly support in the Q&A area.
Make This Investment in Yourself
If you want to ride the machine learning wave and enjoy the salaries that data scientists make, then this is the course for you!
Take this course and become a machine learning engineer!
Course Curriculum
Chapter 1: Introduction
Lecture 1: What Does the Course Cover?
Lecture 2: How to Succeed in This Course
Lecture 3: Project Files and Resources
Chapter 2: Getting Started with Anaconda
Lecture 1: Installing Applications and Creating Environment
Lecture 2: Hello World
Lecture 3: Iris Project 1: Working with Error Messages
Lecture 4: Iris Project 2: Reading CSV Data into Memory
Lecture 5: Iris Project 3: Loading data from Seaborn
Lecture 6: Iris Project 4: Visualization
Chapter 3: Regression
Lecture 1: Scikit-Learn
Lecture 2: EDA
Lecture 3: Correlation Analysis and Feature Selection
Lecture 4: Correlation Analysis and Feature Selection
Lecture 5: Linear Regression with Scikit-Learn
Lecture 6: Five Steps Machine Learning Process
Lecture 7: Robust Regression
Lecture 8: Evaluate Regression Model Performance
Lecture 9: Multiple Regression 1
Lecture 10: Multiple Regression 2
Lecture 11: Regularized Regression
Lecture 12: Polynomial Regression
Lecture 13: Dealing with Non-linear Relationships
Lecture 14: Feature Importance
Lecture 15: Data Preprocessing
Lecture 16: Variance-Bias Trade Off
Lecture 17: Learning Curve
Lecture 18: Cross Validation
Lecture 19: CV Illustration
Chapter 4: Classification
Lecture 1: Logistic Regression
Lecture 2: Introduction to Classification
Lecture 3: Understanding MNIST
Lecture 4: SGD
Lecture 5: Performance Measure and Stratified k-Fold
Lecture 6: Confusion Matrix
Lecture 7: Precision
Lecture 8: Recall
Lecture 9: f1
Lecture 10: Precision Recall Tradeoff
Lecture 11: Altering the Precision Recall Tradeoff
Lecture 12: ROC
Chapter 5: Support Vector Machine (SVM)
Lecture 1: Support Vector Machine (SVM) Concepts
Lecture 2: Linear SVM Classification
Lecture 3: Polynomial Kernel
Lecture 4: Radial Basis Function
Lecture 5: Support Vector Regression
Chapter 6: Tree
Lecture 1: Introduction to Decision Tree
Lecture 2: Training and Visualizing a Decision Tree
Lecture 3: Visualizing Boundary
Lecture 4: Tree Regression, Regularization and Over Fitting
Lecture 5: End to End Modeling
Lecture 6: Project HR
Lecture 7: Project HR with Google Colab
Chapter 7: Ensemble Machine Learning
Lecture 1: Ensemble Learning Methods Introduction
Lecture 2: Bagging
Lecture 3: Random Forests and Extra-Trees
Lecture 4: AdaBoost
Lecture 5: Gradient Boosting Machine
Lecture 6: XGBoost Installation
Lecture 7: XGBoost
Lecture 8: Project HR – Human Resources Analytics
Lecture 9: Ensemble of Ensembles Part 1
Lecture 10: Ensemble of ensembles Part 2
Chapter 8: k-Nearest Neighbours (kNN)
Lecture 1: kNN Introduction
Lecture 2: Project Cancer Detection
Lecture 3: Addition Materials
Lecture 4: Project Cancer Detection Part 1
Chapter 9: Unsupervised Learning: Dimensionality Reduction
Lecture 1: Dimensionality Reduction Concept
Lecture 2: PCA Introduction
Lecture 3: Project Wine
Lecture 4: Kernel PCA
Lecture 5: Kernel PCA Demo
Lecture 6: LDA vs PCA
Lecture 7: Project Abalone
Chapter 10: Unsupervised Learning: Clustering
Lecture 1: Clustering
Lecture 2: k_Means Clustering
Chapter 11: Deep Learning
Lecture 1: Estimating Simple Function with Neural Networks
Lecture 2: Neural Network Architecture
Lecture 3: Motivational Example – Project MNIST
Lecture 4: Binary Classification Problem
Lecture 5: Natural Language Processing – Binary Classification
Chapter 12: Appendix A1: Foundations of Deep Learning
Lecture 1: Introduction to Neural Networks
Lecture 2: Differences between Classical Programming and Machine Learning
Lecture 3: Learning Representations
Lecture 4: What is Deep Learning
Lecture 5: Learning Neural Networks
Lecture 6: Why Now?
Lecture 7: Building Block Introduction
Lecture 8: Tensors
Instructors
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Codestars • over 2 million students worldwide!
Teaching the Next Generation of Coders -
Anthony NG
Algorithmic Trading Workshop Researcher and Conductor -
Rob Percival
Web Developer And Teacher
Rating Distribution
- 1 stars: 98 votes
- 2 stars: 179 votes
- 3 stars: 924 votes
- 4 stars: 2688 votes
- 5 stars: 3504 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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