As the title says, this is a self-study guide for aspiring machine learning practitioners by Google Developers that features a series of lessons along with video lectures, real-world case studies, as well as hands-on practice exercises. Thus, giving you the best it can!

But, before we go ahead, what actually will this Machine Learning crash course provide you with?

The Machine Learning Course consists of the following:

• 40+ exercises

• 25 lessons

• A total time duration required is 15 hours

• Lectures from Google researchers

• Real-world case studies

• Interactive visualizations of algorithms in action

It definitely is fast-paced, practical introduction by Google to machine learning!

This Machine Learning Course helps answer the questions that every beginner would have, some of which are:

• How does machine learning differ from traditional programming?

• How do I build a deep neural network?

But how do we know if it is the right course for you? Take a look at its Prerequisites:

This Machine Learning Crash Course does not require any prior knowledge in the field of machine learning. However, in order to better understand the concepts that are being presented and to efficiently complete the exercises that will be given in this course, it is recommended that the students meet the prerequisites that are mentioned below:

Mastery of algebra at intro-level:

One should be comfortable with variables and coefficients, linear equations, graphs of functions, and histograms as Familiarity with more advanced math concepts such as logarithms and derivatives even though might not be required are still helpful.

Proficiency in programming basics, as well as some experience with coding in Python.

Programming exercises in Machine Learning Crash Course are coded in Python making the use of TensorFlow. Even though no prior experience is required with TensorFlow, but one should feel comfortable with reading as well as writing Python code that consists of basic programming constructs, such as lists and dicts, loops, function definitions/invocations, as well as conditional expressions.

Now, let's take a look at Key Concepts and Tools that will be discussed and applied:

This Machine Learning Crash Course discusses as well as applies the following concepts and tools:

Maths:

• Algebra

• Linear Algebra

• Trignometry

• Statistics

• Calculus (for advanced topics and is optional)

Python Programming:

• Basic Python

• Intermediate Python

• Third Party Python Libraries that include Matplotlib, Seaborn, pandas, NumPy etc.

• Bash Terminal/Cloud Console

So, what are you waiting for? Start right away, if this course interests you!

Learn and apply fundamental machine learning concepts with the Crash Course or visit Learn with Google AI to explore the full library of training resources.

For More Information: Google Developers