A Beginner's Guide to Python Programming for Machine Learning and Deep Learning, Data Analysis, Algorithms and Data Science With Scikit Learn, TensorFlow, PyTorch and Keras.
Slutsåld
ZACH CODINGS was born in Seattle in 1975. He is one of the world's leading experts in computer science and cybersecurity.
Since childhood, Zach has always loved computers, which were much less evolved than today's machines. He was self-taught and wrote his first program when he was only 14 years old. It was a simple game but making it at that time was not easy!
Thanks to his resourcefulness, he was noticed by the high school's computer science professor, who immediately recognized his talent. Through his teachings, he quickly learned the basics of computer science. He became increasingly passionate about the world of computers.
During his college he began to take an interest in cybersecurity.
After graduating with top grades, he worked hard to create an innovative security system, which he still distributes today through his computer company.
Zach runs his cybersecurity company and contributes to the digital security of hundreds of his customers.
The author's mission is to help beginners worldwide master the art of programming and fascinate even the youngest to the beautiful computer science world.
Zach sees programming languages as an accurate means of creative expression that can provide excellent personal and professional satisfaction if well mastered.
Introduction
Chapter 1: Machine Learning: A Brief History
Chapter 2: Fundamentals of Python for Machine Learning
why python?
other programming languages
effective implementation of machine learning algorithms
mastering machine learning with python
Chapter 3: Data Analysis in Python
importance of learning data analysis in python
building predictive models in python
python data structures
python libraries for data analysis
Chapter 4: Comparing Deep Learning and Machine Learning
deep learning vs machine learning
problem solving approaches
different use cases
Chapter 5: Machine Learning with Scikit-Learn
Chapter 6: Deep Learning with TensorFlow
Chapter 7: Deep Learning with PyTorch and Keras
Chapter 8: Role of Machine Learning in the Internet of Things (IoT)
fusing machine learning and iot
machine learning challenges in iot
Chapter 9: Looking to the Future with Machine Learning
the business angle
ai in the future
Conclusion