Introduction to Machine Learning with Python
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Introduction to Machine Learning with Python
This course is part of Python: A Guided Journey from Introduction to Application Specialization
Instructor: Adwith Malpe
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What you'll learn
Students will be able to apply advanced python coding skills in the real world by creating machine learning models.
Skills you'll gain
- Model Evaluation
- Machine Learning
- Data Processing
- Regression Analysis
- Unsupervised Learning
- Artificial Neural Networks
- Model Training
- Supervised Learning
- Computer Vision
- Machine Learning Algorithms
- Computer Programming
- Machine Learning Methods
- Applied Machine Learning
- Image Analysis
- Generative Model Architectures
- Deep Learning
Details to know
9 assignments
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There are 4 modules in this course
This course will give you an introduction to machine learning with the Python programming language. You will learn about supervised learning, unsupervised learning, deep learning, image processing, and generative adversarial networks. You will implement machine learning models using Python and will learn about the many applications of machine learning used in industry today. You will also learn about and use different machine learning algorithms to create your models.
You do not need a programming or computer science background to learn the material in this course. This course is open to anyone who is interested in learning how to code and write programs in Python. We are very excited that you will be learning with us and hope you enjoy the course!
This course will give you an introduction to machine learning with the Python programming language. You will learn about supervised learning, unsupervised learning, deep learning, image processing, and generative adversarial networks. You will implement machine learning models using Python and will learn about the many applications of machine learning used in industry today. You will also learn about and use different machine learning algorithms to create your models. You do not need a programming or computer science background to learn the material in this course. This course is open to anyone who is interested in learning how to code and write programs in Python. We are very excited that you will be learning with us and hope you enjoy the course!
What's included
1 video1 reading
1 videoβ’Total 2 minutes
- Introduction to the Courseβ’2 minutes
1 readingβ’Total 10 minutes
- Python Recommended Links and Readingsβ’10 minutes
In this module you will learn about machine learning and how each branch of machine learning works in Python.
What's included
6 videos12 readings3 assignments
6 videosβ’Total 51 minutes
- Overview of Machine Learningβ’6 minutes
- Introduction to Supervised Learningβ’4 minutes
- Introduction to Unsupervised Learningβ’4 minutes
- How to Load and Process Data Setsβ’10 minutes
- What is a Perceptronβ’14 minutes
- What is Linear Regression?β’14 minutes
12 readingsβ’Total 124 minutes
- Lesson 1 Overviewβ’1 minute
- Weekly Lesson PowerPointβ’10 minutes
- Supervised Learning Exampleβ’10 minutes
- Unsupervised Learning Exampleβ’10 minutes
- Lesson 2 Overviewβ’1 minute
- Weekly Lesson PowerPointβ’10 minutes
- Lesson 3 Overviewβ’1 minute
- Weekly Lesson PowerPointβ’10 minutes
- Perceptron Code Exampleβ’30 minutes
- Lesson 4 Overviewβ’1 minute
- Weekly Lesson PowerPointβ’10 minutes
- Linear Regression Code Exampleβ’30 minutes
3 assignmentsβ’Total 90 minutes
- Machine Learning Quizβ’30 minutes
- Datasets Quizβ’30 minutes
- Formative Assessment: Perceptrons and Linear Regressionβ’30 minutes
In this module, you will learn about two other supervised machine learning models: k-nearest neighbors (kNN) and support vector machines (SVM). You will learn under which conditions youβd use these two models. You will also learn about unsupervised machine learning models and how they work.
What's included
4 videos11 readings3 assignments1 discussion prompt
4 videosβ’Total 42 minutes
- What is K-Nearest Neighborsβ’14 minutes
- What are Support Vector Machines?β’12 minutes
- What is Regression Analysis?β’4 minutes
- What is Cluster Analysis? (K-Means and DBSCAN)β’13 minutes
11 readingsβ’Total 134 minutes
- Lesson 1 Overviewβ’1 minute
- Weekly Lesson PowerPointβ’10 minutes
- KNN Code Exampleβ’30 minutes
- Lesson 2 Overviewβ’1 minute
- Weekly Lesson PowerPointβ’10 minutes
- Support Vector Machines Code Exampleβ’30 minutes
- Lesson 3 Overviewβ’1 minute
- Weekly Lesson PowerPointβ’10 minutes
- Lesson 4 Overviewβ’1 minute
- Weekly Lesson PowerPointβ’10 minutes
- K-Means Clustering Code Example β’30 minutes
3 assignmentsβ’Total 90 minutes
- Supervised Learning Quizβ’30 minutes
- Unsupervised Learning Quiz β’30 minutes
- Formative Assessment: Supervised Learningβ’30 minutes
1 discussion promptβ’Total 10 minutes
- Unsupervised Learningβ’10 minutes
In this module, you will gain an overview of advanced machine learning topics, including deep learning, image processing, and generative adversarial networks (GANs).
What's included
4 videos6 readings3 assignments1 peer review1 discussion prompt
4 videosβ’Total 14 minutes
- Overview of Deep Learningβ’4 minutes
- Overview of Image Processingβ’4 minutes
- Overview of GANsβ’4 minutes
- Course Reviewβ’1 minute
6 readingsβ’Total 33 minutes
- Lesson 1 Overviewβ’1 minute
- Weekly Lesson PowerPointβ’10 minutes
- Lesson 2 Overviewβ’1 minute
- Weekly Lesson PowerPointβ’10 minutes
- Lesson 3 Overviewβ’1 minute
- Weekly Lesson PowerPointβ’10 minutes
3 assignmentsβ’Total 90 minutes
- Deep Learning Quiz β’30 minutes
- Image Processing Codeβ’30 minutes
- Generative Adversarial Networks β’30 minutes
1 peer reviewβ’Total 60 minutes
- Make a Classifierβ’60 minutes
1 discussion promptβ’Total 10 minutes
- Generative Adversarial Networks β’10 minutes
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