Course Information
Registration period
Year-round Recruitment
Course Level
Study Mode
Duration
3 Hour(s) 42 Minute(s)
Language
English
Taught by
Eduero Academy, Inc.
Rating
Course Overview
Go beyond the basics. Master Feature Engineering, SVMs, Random Forests, and Model Tuning with Scikit-Learn.
You know the basics of Data Science. Now, it’s time to master the craft.
Many courses teach you how to run a simple linear regression. But real-world data is messy, complex, and requires advanced strategies. If you are ready to move beyond "Hello World" tutorials and start building robust, deployment-ready models, this course is for you.
Welcome to Advanced Machine Learning. This course is your bridge from "Junior Analyst" to "Senior Data Scientist." We strip away the fluff and dive deep into the mathematical intuition and practical implementation of the industry's most powerful algorithms using Python and Scikit-learn.
What will you build? We believe in learning by doing. You won't just watch code; you will code along with us to build sophisticated projects, including:
Medical Prognosis: Predict insurance risk based on patient data using Random Forests.
Computer Vision: Build a letter recognition system using Support Vector Machines (SVMs).
Natural Language Processing: Create a document classification system that can read and sort text.
What skills will you master?
Advanced Algorithms: Go deep into Support Vector Machines (SVMs) and Random Forests. Understand how they work under the hood, not just how to import them.
Feature Engineering: This is the secret sauce of Data Science. Learn to extract meaningful features from categorical variables, raw text, and images to drastically improve model accuracy.
Model Evaluation: Move beyond simple accuracy scores. Learn to use Confusion Matrices, Precision, Recall, and F1-Scores to truly understand your model's performance.
Parameter Tuning: Stop guessing. Learn the scientific approach to fine-tuning your hyperparameters for peak performance.
Why take this course? In the competitive world of AI, knowing how to use a library isn't enough. You need to know which algorithm to use, why to use it, and how to optimize it. This course gives you that strategic advantage.
Whether you are a professional looking to automate complex tasks or a student aiming for a top-tier Data Science role, this curriculum is designed to get you there fast.
Enroll today, and let's start building the future of AI.
Course Content
- 11 section(s)
- 47 lecture(s)
- Section 1 Introduction
- Section 2 Getting Started With This Course
- Section 3 Machine Learning - Model Complexity
- Section 4 Understanding Pipelines
- Section 5 Machine Learning - Imbalanced Classes & Metrics
- Section 6 Machine Learning - Model Selection For Unsupervised Learning
- Section 7 Machine Learning - Handling Real Data
- Section 8 Machine Learning - Dealing with Text Data
- Section 9 Machine Learning - Out Of Core Learning
- Section 10 Course Summary
- Section 11 Code Files
What You’ll Learn
- Extract features from categorical variables, text, and images, Solve real-world problems using machine learning techniques, Exploit the power of Python to handle data extraction, manipulation, and exploration techniques, Implement machine learning classification and regression algorithms from scratch in Python, Dive deep into the world of analytics to predict situations correctly, Predict the values of continuous variables, Classify documents and images using logistic regression and support vector machines, Create ensembles of estimators using bagging and boosting techniques, Evaluate the performance of machine learning systems in common tasks
Skills covered in this course
Reviews
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JJohannes Schneider
only scratches the surface without ever explaining any of the underlying concepts of the algorithms used. Also in the examples it's never explained why a a specific algorithm is used.
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PPurnima Pandit
Nice, although addition of few introductory sildes on the concepts implemented, would aid in better understanding and enhance the course.
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DDavid Bierbrauer
The topics are decent, but some of the presentations could have been more backed up by additional graphics.
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JJwalit Bhavinkumar Miniwala
Fabulous Teaching
