Google has offered a free online course on machine learning that interested participants can take. The course entitled “Machine Learning Crash Course with TensorFlow API” is Google’s fast-paced, practical introduction to machine learning that can be completed in 15 hours.
The Google Free Online Machine Learning Crash Course features a series of lessons with video lectures, real-world case studies, and practical exercises. The interested participants in the course are required to have basic knowledge of Machine Learning, NumPy, Pandas, Algebra, Trigonometry, Calculus, etc. If participants are new to machine learning, they can also take an introduction to machine learning problem framing provided by Google. The one-hour self-study course teaches participants to identify problems suitable for machine learning.
This course teaches the basics of machine learning through a series of lessons that include video lectures from researchers at Google, text written specifically for newcomers to ML, interactive visualizations of algorithms in action, and real-world case studies. While learning new concepts, you’ll immediately put them into practice with coding exercises that walk you through implementing models in TensorFlow, an open-source machine intelligence library.
What is mandatory for the candidates google free online course on machine learning.
It is not mandatory for the candidates to have any prior knowledge in Machine Learning in the course. However, in order to understand the concepts presented and complete the exercises, students must meet the following prerequisites:
Participants should be comfortable with variables, linear equations, graphs of functions, histograms, and statistical tools.
Participants must be good programmers, and ideally, have some experience programming in Python as programming exercises are in Python. However, experienced programmers without Python experience can usually complete the programming exercise anyway, Google says.
What will Google’s free online course on machine learning cover?
After completing the course, participants will be able to recognize the practical benefits of mastering Machine Learning and understand the philosophy behind Machine Learning. Some of the topics that will be covered are as follows and details of Course time:
- Introduction to ML (3 min)
- Framing (15 min)
- Descending into ML (20 min)
- Reducing Loss (60 min)
- First Steps with TF (65 min)
- Generalization (15 min)
- Training and Test Sets (25 min)
- Validation Set (35 min)
- Representation (35 min)
- Feature Crosses (70 min)
- Regularization: Simplicity (40 min
- Logistic Regression (20 min)
- Classification (90 min)
- Regularization: Sparsity (20 min)
- Neural Networks(65 Minute)
- Training Neural nets (10 min)
- Malti Class neural nets(45 min)
- Embeddings (50 min)
- O Production ML Systems (3 min)
- Static vs. Dynamic Training (7 min)
- Static vs. Dynamic Inference (7 min)
- Data Dependencies (14 min)
- Fairness (70 min)
- ML Systems in the Real World Cancer Prediction (5 min)
- Literature (5 min)
- Guidelines (2 min)
Those who want to know more and want to take Google’s free online course on Machine Learning are advised to visit the official website for details.