MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 29 lectures (5h 1m) | Size: 1.89 GB
Hands-on Machine Learning boot-camp with Python, Numpy, Pandas, Regression, Decision Trees, Neural Networks, and more!
What you’ll learn:
Learn the basics of data visualization and pre-processing (Python basics, Numpy, Pandas, Seaborn)
Gain theoretical and practical experience with fundamental machine learning algorithms (Linear and Logistic Regression, K-NN, Decision Trees, Neural Networks)
Understand advanced ML topics (encoding, ensemble learning techniques, etc.)
Submit to your first Kaggle Machine Learning Competition
Requirements
No programming or theoretical math prerequisites. We’ll teach you everything you need to know.
Description
Interested in machine learning but confused by the jargon? If so, we made this course for you.
Machine learning is the fastest-growing field with constant groundbreaking research. If you’re interested in any of the following, you’ll be interested in ML:
Self-driving cars
Language processing
Market prediction
Self-playing games
And so much more!
No past knowledge is required: we’ll start with the basics of Python and end with gradient-boosted decision trees and neural networks. The course will walk you through the fundamentals of machine learning, explaining mathematical foundations as well as practical implementations. By the end of our course, you’ll have worked with five public data sets and have implemented all essential supervised learning models. After the course’s completion, you’ll be equipped to apply your skills to Kaggle data science competitions, business intelligence applications, and research projects.
We made the course quick, simple, and thorough. We know you’re busy, so our curriculum cuts to the chase with every lecture. If you’re interested in the field, this is a great course to start with.
Here are some of the Python libraries you’ll be using:
Numpy (linear algebra)
Pandas (data manipulation)
Seaborn (data visualization)
Scikit-learn (optimized machine learning models)
Keras (neural networks)
XGBoost (gradient-boosted decision trees)
Here are the most important ML models you’ll use:
Linear Regression
Logistic Regression
Random Forrest Decision Trees
Gradient-Boosted Decision Trees
Neural Networks
Not convinced yet? By taking our course, you’ll also have access to sample code for all major supervised machine learning models. Use them how you please!
Start your data science journey today with The Complete Intro to Machine Learning with Python.
Who this course is for
Anyone interested in machine learning, data science, and artificial intelligence. No experience required.
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