Free TensorFlow 2.0 Complete Course
Are you a beginner python programmer aiming to make a career in Machine Learning? If yes, then you are at the right place! This FREE tutorial will give you a solid understanding of the foundations of Machine Learning and Neural Networks using TensorFlow 2.0.
Image by Author
AI solutions and machine learning systems have revolutionized everything from the way we work to the way we learn. It has achieved an inflection point and is shaping the future of humanity. The top global tech giants such as Google, Microsoft, Amazon, Apple, Meta, Tesla, etc are investing heavily in the upgradation and development of AI applications. Moreover, according to a report by Precedence Research:
“The global artificial intelligence (AI) market size was estimated at US$ 119.78 billion in 2022 and it is expected to hit US$ 1,597.1 billion by 2030 with a registered CAGR of 38.1% from 2022 to 2030”
These recent trends in the marketplace now demand a more skillful workforce in this sector and have merged as one of the highest-paying fields. Having said so, if you are a Beginner Python Programmer with a keen interest in machine learning and artificial intelligence then this article is for you, so keep on reading.
FreeCodeCamp has released a FREE TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial in collaboration with Tim Ruscica, otherwise known as “Tech With Tim” from his educational programming YouTube channel. This 7-hour course teaches about fundamental concepts in ML & AI like core learning algorithms, deep learning with neural networks, computer vision with convolutional neural networks, natural language processing with recurrent neural networks, and reinforcement learning. Let's jump over to what it covers:
This course requires you to have a basic knowledge of programming using Python. If you have not worked with python before, then I would personally recommend you to take “Learn Python - Free Course for Beginners by FreeCodeCamp” first before moving over to this one.
The course is divided into the following 8 Modules:
Module 1: Machine learning fundamentals
This module starts by explaining the basic terminology that will be used throughout the course like Machine Learning, Artificial Intelligence, Neural Networks, etc. It also discusses the importance of data. What are the labels, and features? And how does the neural network work?
Module 2: Introduction to TensorFlow
This module walks through the following topics:
- TensorFlow Install and Setup
- Representing Tensors
- Tensor Shape and Rank
- Types of Tensors
Module 3: Core learning algorithms
This module covers the 4 fundamental machine learning algorithms. These algorithms have been applied to unique problems and datasets after highlighting the use cases of each. The 4 algorithms discussed are
- Linear Regression
- Hidden Markov Models
Module 4: Neural Networks with TensorFlow
This module goes over the following subtopics:
- How does the neural network work?
- Creating a neural network
- Data preprocessing
- Building and training the model
- Evaluating the model
- Making and verifying the predictions
Module 5: Deep computer vision - Convolutional Neural Networks
In this module, we are taught how to perform image classification and object detection/recognition using deep computer vision with convolutional neural network and explains the following concepts:
- Image Data
- Convolutional Layer
- Pooling Layer
- CNN Architectures
Module 6: Natural language processing with RNNs
A new kind of neural network that is much more capable of processing sequential data such as text or characters called a recurrent neural network (RNN) is introduced here. It explains how to use a recurrent neural network to do the following:
- Sentiment Analysis
- Character Generation
Module 7: Reinforcement learning with Q-Learning
This is the final topic in the course that covers Reinforcement Learning and uses a different technique to make predictions. The topics discussed in this module are as follows:
- Basic Terminology
- Q-Learning Example
Module 8: Conclusion and next steps
This module recommends some of the sources as the next steps to further your learning in TensorFlow.
If you are interested to dig deeper into this course then check this course video below:
This course includes comprehensive explanations and various coding examples for each module. Upon completion, you will have a strong understanding of the fundamental concepts in machine learning and AI, as well as the ability to apply them to your data and specific issues.
Kanwal Mehreen is an aspiring software developer with a keen interest in data science and applications of AI in medicine. Kanwal was selected as the Google Generation Scholar 2022 for the APAC region. Kanwal loves to share technical knowledge by writing articles on trending topics, and is passionate about improving the representation of women in tech industry.