The Introduction to Machine Learning with TensorFlow program covers supervised and unsupervised learning methods for machine learning. Course 1 introduces regression, perceptron algorithms, decision trees, naive Bayes, support vector machines, and evaluation metrics. Course 2 focuses on neural networks and creating an image classifier. Course 3 covers unsupervised learning methods, including clustering and dimensionality reduction for customer segmentation. Key skills include data preprocessing, model selection, and evaluation using TensorFlow and Python.
Introduction to Machine Learning with TensorFlow
Kaggle , Amazon Web Services and Amazon via Udacity Nanodegree
Overview
Syllabus
- Introduction to Machine Learning
- Supervised Learning
- In this course, you'll learn about different types of supervised learning and how to use them to solve real-world problems.
- Introduction to Neural Networks with TensorFlow
- Learn the fundamentals of neural networks with Python and TensorFlow, and then use your new skills to create your own image classifier—an application that will first train a deep learning model on a dataset of images and then use the trained model to classify new images.
- Unsupervised Learning
- In this course, you'll learn how to apply unsupervised learning to solve real-world problems.
- Congratulations!
- Prerequisite: Python for Data Analysis
- Prerequisite: SQL for Data Analysis
- Prerequisite: Command Line Essentials
- Prerequisite: Git & Github
- Additional Material: Python for Data Visualization
- Additional Material: Statistics for Data Analysis
- Additional Material: Linear Algebra
- Career Services
Taught by
Cezanne Camacho, Mat Leonard, Luis Serrano, Dan Romuald Mbanga, Jennifer Staab, Sean Carrell, Josh Bernhard , Jay Alammar, Andrew Paster, Juan Delgado and Michael Virgo
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Reviews
5.0 rating, based on 5 Class Central reviews
4.6 rating at Udacity based on 256 ratings
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Yoo Alejandro"Amazing program! The projects are designed so delicately, integrating almost all essential and keynotes from the course material, impressive! Another most precious thing is the review and feedback from the reviewer. OMG it is very detailed and personalized, and each piece of advice and further recommendation and suggested references are so useful that keep you going forward in the right direction. Stay Udacious, stay competent!" -
The program has been great from the challenge phase till now. I've learnt a lot about the math behind many algorithms in this course and outside and I've also gotten useful tips to improve my modelling like using PCA and also getting feature importance to reduce features hence making the model interpretable and also save time. The first project was awesome as I was able to research and know more about the pros and cons of many models. This course has been great for me.
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Excellent Nanodegree program! Principles and practices of machine learning are covered with very well-structured content, video lessons and amazing real-world projects. Mentors and reviewers are very helpful with guidance, feedback and recommended references. It is worth doing this program if you not only want to learn the fundamentals but also get hands-on practice with machine learning (Supervised Learning, Deep Learning and Unsupervised Learning).
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Yemi IdrisOver the last 6weeks, I've built confidence in the Supervised Machine Learning coupled which is the Normal Curriculum. In addition, I've gained indept knowledge using some of the Extracurricular courses, such as SQL, Statistics and probability as these are just concise and well explained. With all these, I'm building the right skillsets moving forward to become a Data Scientist (Machine Learning Engineer) -
Just finished the first project, it had lots of guidance which kept me motivated. The reviewer gave me a lot to read on further too. Great experience so far.
