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Supervised Learning for AI with Python and Tensorflow 2

Uncover the Concepts and Techniques to Build and Train your own Artificial Intelligence Models
(19 reviews)
386 students
Created by


CourseMarks Score®







Platform: Udemy
Video: 21h 12m
Language: English
Next start: On Demand

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Detailed Analysis

CourseMarks Score®

9.7 / 10

CourseMarks Score® helps students to find the best classes. We aggregate 18 factors, including freshness, student feedback and content diversity.

Freshness Score

9.9 / 10
This course was last updated on 11/2021.

Course content can become outdated quite quickly. After analysing 71,530 courses, we found that the highest rated courses are updated every year. If a course has not been updated for more than 2 years, you should carefully evaluate the course before enrolling.

Student Feedback

8.9 / 10
We analyzed factors such as the rating (4.8/5) and the ratio between the number of reviews and the number of students, which is a great signal of student commitment.

New courses are hard to evaluate because there are no or just a few student ratings, but Student Feedback Score helps you find great courses even with fewer reviews.

Content Score

9.7 / 10
Video Score: 10.0 / 10
The course includes 21h 12m video content. Courses with more videos usually have a higher average rating. We have found that the sweet spot is 16 hours of video, which is long enough to teach a topic comprehensively, but not overwhelming. Courses over 16 hours of video gets the maximum score.
Detail Score: 9.7 / 10

The top online course contains a detailed description of the course, what you will learn and also a detailed description about the instructor.

Extra Content Score: 9.5 / 10

Tests, exercises, articles and other resources help students to better understand and deepen their understanding of the topic.

This course contains:

0 article.
12 resources.
0 exercise.
0 test.

Table of contents


Gain a deep understanding of Supervised Learning techniques by studying the fundamentals and implementing them in NumPy.
Gain hands-on experience using popular Deep Learning frameworks such as Tensorflow 2 and Keras.

Section 1 – The Basics:
– Learn what Supervised Learning is, in the context of AI
– Learn the difference between Parametric and non-Parametric models
– Learn the fundamentals: Weights and biases, threshold functions and learning rates
– An introduction to the Vectorization technique to help speed up our self implemented code
– Learn to process real data: Feature Scaling, Splitting Data, One-hot Encoding and Handling missing data
– Classification vs Regression

Section 2 – Feedforward Networks:
– Learn about the Gradient Descent optimization algorithm.
– Implement the Logistic Regression model using NumPy
– Implement a Feedforward Network using NumPy
– Learn the difference between Multi-task and Multi-class Classification
– Understand the Vanishing Gradient Problem
– Overfitting
– Batching and various Optimizers (Momentum, RMSprop, Adam)

Section 3 – Convolutional Neural Networks:
– Fundamentals such as filters, padding, strides and reshaping
– Implement a Convolutional Neural Network using NumPy
– Introduction to Tensorfow 2 and Keras
– Data Augmentation to reduce overfitting
– Understand and implement Transfer Learning to require less data
– Analyse Object Classification models using Occlusion Sensitivity
– Generate Art using Style Transfer
– One-Shot Learning for Face Verification and Face Recognition
– Perform Object Detection for Blood Stream images

Section 4 – Sequential Data
– Understand Sequential Data and when data should be modeled as Sequential Data
– Implement a Recurrent Neural Network using NumPy
– Implement LSTM and GRUs in Tensorflow 2/Keras
– Sentiment Classification from the basics to the more advanced techniques
– Understand Word Embeddings
– Generate text similar to Romeo and Juliet
– Implement an Attention Model using Tensorflow 2/Keras

You will learn

✓ The basics of supervised learning: What are parameters, What is a bias node, Why do we use a learning rate
✓ Techniques for dealing with data: How to Split Datasets, One-hot Encoding, Handling Missing Values
✓ Vectors, matrices and creating faster code using Vectorization
✓ Mathematical concepts such as Optimization, Derivatives and Gradient Descent
✓ Gain a deep understanding behind the fundamentals of Feedforward, Convolutional and Recurrent Neural Networks
✓ Build Feedforward, Convolutional and Recurrent Neural Networks using only the fundamentals
✓ How to use Tensorflow 2.0 and Keras to build models, create TFRecords and save and load models
✓ Practical project: Style Transfer – Use AI to draw an image in the style of your favorite artist
✓ Practical project: Object Detection – Use AI to Detect the bounding box locations of objects inside of images
✓ Practical project: Transfer Learning – Learn to leverage large pretrained AI models to work on new datasets
✓ Practical project: One-Shot Learning – Learn to build AI models to perform tasks such as Face recognition
✓ Practical project: Text Generation – Build an AI model to generate text similar to Romeo and Juliet
✓ Practical project: Sentiment Classification – Build an AI model to determine whether text is overall negative or positive
✓ Practical project: Attention Model – Build an attention model to build an interpretable AI model


• Secondary Level (High School) Mathematics
• Some basic programming experience in Python

This course is for

• Beginner Python programmers curious about Artificial Intelligence
• People looking for an AI course that teaches both the theoretical and practical aspects of Artificial Intelligence

How much does the Supervised Learning for AI with Python and Tensorflow 2 course cost? Is it worth it?

The course costs $11.99. And currently there is a 86% discount on the original price of the course, which was $84.99. So you save $73 if you enroll the course now.

Does the Supervised Learning for AI with Python and Tensorflow 2 course have a money back guarantee or refund policy?

YES, Supervised Learning for AI with Python and Tensorflow 2 has a 30-day money back guarantee. The 30-day refund policy is designed to allow students to study without risk.

Are there any SCHOLARSHIPS for this course?

Currently we could not find a scholarship for the Supervised Learning for AI with Python and Tensorflow 2 course, but there is a $73 discount from the original price ($84.99). So the current price is just $11.99.

Who is the instructor? Is Jeremy Richard Lai Hong a SCAM or a TRUSTED instructor?

Jeremy Richard Lai Hong has created 1 courses that got 19 reviews which are generally positive. Jeremy Richard Lai Hong has taught 386 students and received a 4.8 average review out of 19 reviews. Depending on the information available, Jeremy Richard Lai Hong is a TRUSTED instructor.
Data Scientist and Software Engineer
Jeremy Richard Lai Hong was born and raised in South Africa and apologises in advance for his accent.

He has experience as both a Software Engineer and as a Data Scientist. He has practical working experience, having worked at a bank and for a brand management company.

He’s worked on many projects including Optical Character Recognition, Topic Modelling, ATM cash forecasting, Object Tracking and Sentiment Analysis.

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CourseMarks Score®







Platform: Udemy
Video: 21h 12m
Language: English
Next start: On Demand

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