</ Leverage data to solve real-life data science problems >
</ Master the fundamentals
of coding >
</ Build the skills to become a Machine Learning Engineer >
</ Master the fundamentals
of coding >
</ Build the skills to become a Machine Learning Engineer >
Intro to Machine Learning
Soon →
14-17 y.o.
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This course exposes students to machine learning and data science disciplines. It covers a range of topics including essential data analysis techniques, supervised learning, and computer vision. Students will learn the packages, algorithms in data science and the logic behind them. They will apply gained knowledge to create modern data science applications such as traffic signs recognition, stock price reduction and even chat-bots.
About the course
Tech requirements:
Laptop/PC, Wi-Fi (25+ Mbps), Webcam
Laptop/PC, Wi-Fi (25+ Mbps), Webcam
Prerequisites:
Python Fundamentals, Math (Linear Algebra basics)
Python Fundamentals, Math (Linear Algebra basics)
Time Commitment:
2 hours per week in class
+
1-2 hours per week outside of class
2 hours per week in class
+
1-2 hours per week outside of class
Outcomes
(* Create modern data science applications *)
// Master the fundamentals of Machine Learning
# Be exposed to advanced real-life Machine Learning Topics
/* Understand how the classificators work and build yours */
Curriculum:
Week 1-2
First Model
Pandas and Scikit-learn Packages; Machine Learning Problem Formulation;
Regression Problems;
1st lesson
Course Introduction
Meaning and Application of Machine Learning;
Week 3-4
Machine Learning algorithms
Classification Algorithms (Decision Trees, SVM, Naive Bayes);
Clustering Algorithms;
Week 5-6
Neural Networks;
Deep Learning Toolbox in Python;
Deep Learning Toolbox in Python;
Week 7-8
Capstone Project
Real-world Machine Learning Problems;
Presentations;
Introduction to Deep Learning
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