AI: The Complete Real-World Machine Learning Solutions Course - Beginner To Advanced!
Learn how to perform various machine learning tasks in different environments. Go from Beginner to Advanced level in Machine Learning.
Machine learning is increasingly pervasive in the modern data-driven world. It is used extensively across many fields such as search engines, robotics, self-driving cars, and more.
In this course, you'll learn
- Explore classification algorithms and apply them to the income bracket estimation problem
- Use predictive modeling and apply it to real-world problems
- Understand how to perform market segmentation using unsupervised learning
- Explore data visualization techniques to interact with your data in diverse ways
- Find out how to build a recommendation engine
- Understand how to interact with text data and build models to analyze it
- Work with speech data and recognize spoken words using Hidden Markov Models
- Analyze stock market data using Conditional Random Fields
- Work with image data and build systems for image recognition and biometric face recognition
- Grasp how to use deep neural networks to build an optical character recognition system
We’ll start by exploring a range of real-life scenarios where machine learning can be used, and look at various building blocks. Throughout the course, you’ll use a wide variety of machine learning algorithms to solve real-world problems and use Python to implement these algorithms.
You’ll discover how to deal with various types of data and explore the differences between machine learning paradigms such as supervised and unsupervised learning. We also cover a range of regression techniques, classification algorithms, predictive modelling, data visualization techniques, recommendation engines, and more with the help of real-world examples.
This course teaches you how to perform various machine learning tasks in different environments. Each lecture in the section will cover a real-life scenario.
So, let's get started!
Course Curriculum
Section 1 Welcome
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StartLecture 1 Welcome (3:02)
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StartLecture 2 Preprocessing Data Using Different Techniques (6:20)
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StartLecture 3 Label Encoding (2:19)
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StartLecture 4 Building a Linear Regressor (4:19)
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StartLecture 5 Regression Accuracy and Model Persistence (3:29)
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StartLecture 6 Building a Ridge Regressor (2:29)
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StartLecture 7 Building a Polynomial Regressor (2:21)
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StartLecture 8 Estimating housing prices (3:34)
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StartLecture 9 Computing relative importance of features (1:43)
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StartLecture 10 Estimating bicycle demand distribution (4:21)
Section 2 Constructing a Classifier
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StartLecture 11 Building a Simple Classifier (3:39)
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StartLecture 12 Building a Logistic Regression Classifier (4:41)
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StartLecture 13 Building a Naive Bayes’ Classifier (2:04)
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StartLecture 14 Splitting the Dataset for Training and Testing (1:15)
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StartLecture 15 Evaluating the Accuracy Using Cross-Validation (4:00)
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StartLecture 16 Visualizing the Confusion Matrix and Extracting the Performance Report (4:05)
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StartLecture 17 Evaluating Cars based on Their Characteristics (5:03)
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StartLecture 18 Extracting Validation Curves (2:40)
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StartLecture 19 Extracting Learning Curves (1:30)
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StartLecture 20 Extracting the Income Bracket (3:27)
Section 3 Predictive Modeling
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StartLecture 21 Building a Linear Classifier Using Support Vector Machine (4:21)
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StartLecture 22 Building Nonlinear Classifier Using SVMs (1:41)
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StartLecture 23 Tackling Class Imbalance (2:43)
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StartLecture 24 Extracting Confidence Measurements (2:25)
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StartLecture 25 Finding Optimal Hyper-Parameters (2:05)
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StartLecture 26 Building an Event Predictor (3:32)
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StartLecture 27 Estimating Traffic (2:27)
Bill Gates Says These Are the Jobs He Would Drop Out of College for Today
If he were to go into computer sciences today, Gates said, the area that he thought had the most potential was artificial intelligence. Gates brought up a recent victory by Google DeepMind over the top player in the world at Go, a game some predicted a computer could never master. He called it a remarkable achievement that signaled there is more to come in advancement in artificial intelligence. And he said the research being done in the field now is "profound" and on the verge of making new breakthroughs. "The ability for artificial agents to read and understand material is going to be phenomenal," says Gates. "Anything connected with that would be an exciting lifetime career."
Get started now!
Frequently Asked Questions
Why join this course?
Short answer - AI is fun, and it pays very well!
Want to start a career in the exciting field of Deep Learning, Machine Learning, and Artificial Intelligence? This course is an excellent start. Yes, AI jobs pay a LOT more than regular programming jobs!
What if I don't know how to program?
No worries. We'll have you up and running in no time. And we'll step you through every single line of code as we go in case you get lost. If you have a familiarity with some high school mathematics, you're not going to have a problem learning this course with our help!
What devices are supported?
You can take the course from any device and any location, as long as you have an internet connection. All lectures are optimized for mobile or desktop viewing.
When does the course start and finish?
The course starts now and never ends! It is a completely self-paced online course - you decide when you start and when you finish.
How long do I have access to the course?
How does lifetime access sound? After enrolling, you have unlimited access to this course for as long as you like - across any and all devices you own.
Who has to pay VAT?
Currently, Students who reside in the European Union (EU) will be charged the VAT when they purchase a course. Students can add VAT ID at checkout to remove the tax from their purchase.
What if I am unhappy with the course?
We would never want you to be unhappy! If you are unsatisfied with your purchase, contact us in the first 30 days and we will give you a full refund.