Course Summary
This course provides an introduction to machine learning, covering topics such as supervised and unsupervised learning, decision trees, and clustering.Key Learning Points
- Learn how to use machine learning algorithms to make predictions and decisions
- Understand the difference between supervised and unsupervised learning
- Gain experience with popular machine learning tools such as scikit-learn and TensorFlow
Related Topics for further study
Learning Outcomes
- Understand the fundamentals of machine learning
- Gain experience using popular machine learning tools
- Be able to use machine learning to solve real-world problems
Prerequisites or good to have knowledge before taking this course
- Basic programming knowledge (Python preferred)
- Familiarity with linear algebra and calculus
Course Difficulty Level
IntermediateCourse Format
- Online, self-paced
- Video lectures
- Hands-on projects
Similar Courses
- Applied Data Science with Python
- Deep Learning Specialization
Related Education Paths
Notable People in This Field
- Founder, deeplearning.ai
- Chief AI Scientist, Facebook
Related Books
Description
This class will teach you the end-to-end process of investigating data through a machine learning lens. Learn online, with Udacity.Outline
- lesson 1 Welcome to Machine Learning Learn what Machine Learning is and meet Sebastian Thrun! Find out where Machine Learning is applied in Technology and Science. lesson 2 Naive Bayes Use Naive Bayes with scikit learn in python. Splitting data between training sets and testing sets with scikit learn. Calculate the posterior probability and the prior probability of simple distributions. lesson 3 Support Vector Machines Learn the simple intuition behind Support Vector Machines. Implement an SVM classifier in SKLearn/scikit-learn. Identify how to choose the right kernel for your SVM and learn about RBF and Linear Kernels. lesson 4 Decision Trees Code your own decision tree in python. Learn the formulas for entropy and information gain and how to calculate them. Implement a mini project where you identify the authors in a body of emails using a decision tree in Python. lesson 5 Choose your own Algorithm Decide how to pick the right Machine Learning Algorithm among K-Means, Adaboost, and Decision Trees. lesson 6 Datasets and Questions Apply your Machine Learning knowledge by looking for patterns in the Enron Email Dataset. You'll be investigating one of the biggest frauds in American history! lesson 7 Regressions Understand how continuous supervised learning is different from discrete learning. Code a Linear Regression in Python with scikit-learn. Understand different error metrics such as SSE, and R Squared in the context of Linear Regressions. lesson 8 Outliers Remove outliers to improve the quality of your linear regression predictions. Apply your learning in a mini project where you remove the residuals on a real dataset and reimplement your regressor. Apply your same understanding of outliers and residuals on the Enron Email Corpus. lesson 9 Clustering Identify the difference between Unsupervised Learning and Supervised Learning. Implement K-Means in Python and Scikit Learn to find the center of clusters. Apply your knowledge on the Enron Finance Data to find clusters in a real dataset. lesson 10 Feature Scaling Understand how to preprocess data with feature scaling to improve your algorithms. Use a min mx scaler in sklearn.
Summary of User Reviews
Learn the fundamentals of machine learning with Udacity's Intro to Machine Learning course. Students praise the course for its hands-on projects, clear explanations, and engaging instructor. One key aspect that many users found good is the practical examples and real-world applications used in the course.Pros from User Reviews
- Hands-on projects for practical learning
- Clear explanations of machine learning concepts
- Engaging instructor with real-world experience
Cons from User Reviews
- Some users found the pace of the course too fast
- Limited support for technical issues
- Course may not be advanced enough for experienced data scientists
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