Data Science Theory Crash Course
Master the core theory behind neural networks, machine learning algorithms, classifiers and more of the crucial topics you need before building projects.
The Best Data Science Theory Guide
Do you need to learn data science and algorithms?
Do you need an introduction to data science with only the core concepts, formula and topics you need to know before jumping into projects?
This is the course for you.
Enroll now to learn the core theory you need to understand before applying that theory directly into your code in beginner data science projects.
Don't wait! Enroll while spots are open.
Java Data Science
- What is the K-NN algorithm?
- What can the K-NN algorithm used for?
- How does the K-NN algorithm work? (What does the K stand for?)
- Why does K-NN matter?
- What are the pros and cons?
- What equations do we need to know to build the K-NN algorithm?
- What is Euclidean distance?
- What is normalizing?
- What is a ‘vote’ in K-NN?
Part 2: Decision Trees Theory
- What are decision trees?
- What is ‘best feature to split on’?
- What is Information Gain?
- How is Information Gain used in decision trees?
- What is entropy?
Part 3: Neural Networks Theory
- What are neural networks?
- What are the parts to a neural network?
- What are neural networks used for?
- What projects can we build?
- What is a ‘weight’?
- What do ‘target result’ and ‘error’ mean in neural networks?
- What is an activation function?
- What is a step function?
- What is an epoch?
- What is learning rate?
- What is a linear classifier?
- What is a binary classifier?
- What is supervised learning vs unsupervised learning?
- What is the perceptron algorithm?
Part 4: Data Classification and Naive Bayes Theory
- What is Sentiment Classification?
- What is Bayes Theorem?
- What is Naive Bayes?
- What are the types of Naive Bayes classifiers?
- What does the ‘Naive’ mean?
- What is the Bag of Words model?
- What is data smoothing?
- What is prior probability distribution?
- How do you avoid underflow errors?
- And more!
A SCHOOL YOU CAN TRUST
- Lifetime access that never expires
- Project-based curriculum to superboost your portfolio
- Graduation certificate for every course
- Absolute beginner-friendly
- New courses every month
- Efficient lectures with step by step explanations
- Relevant industry topics 8 years of award-winning course delivery
- 700,000 students in 186 countries
- Learn with free tools and affordable courses
- No experience necessary.
- Experience in statistics and math is helpful but not required.