| title | Python for Engineers |
|---|---|
| tags | Python |
| description | Documenting my learning journey |
- MERN stack
- Embedded systems programming
- Raspberry Pi
- SciKit-Learn
- Matplotlib
- Numpy
This repository was created to document my Python(for ML) 1-year learning journey. Most of the codes and practices are inspired by Manohar Swamynathan
He is a data science practitioner and an avid programmer, with over 13 years of experience in various data science-related areas that include data warehousing, Business Intelligence (BI), analytical tool development, ad hoc analysis, predictive modeling, data science product development, consulting, formulating strategy, and executing analytics program. He’s had a career covering life cycles of data across different domains such as U.S. mortgage banking, retail, insurance, and industrial IoT. He has a bachelor’s degree with specialization in physics, mathematics, and computers; and a master’s degree in project management. He’s currently living in Bengaluru, the Silicon Valley of India, working as Staff Data Scientist with General Electric Digital, contributing to the next big digital industrial revolution. You can visit him at to learn more about his various other activities.
link : http://www.mswamynathan.com or https://www.linkedin.com/in/manoharswamynathan/
⚠️ To avoid violating the copyright of the reference book(mastering-machine-learning-with-python-in-six-steps),the course content are as per the author of the reference book.
This chapter will help you to set up the environment, and introduce you to the key concepts of Python programming language in relevance to machine learning. If you are already well versed with Python basics, I recommend you glance through the chapter quickly and move onto the next chapter.
Here you will learn about the history, evolution, and different frameworks in practice for building machine learning systems. I think this understanding is very important as it will give you a broader perspective and set the stage for your further expedition. You’ll understand the different types of machine learning (supervised / unsupervised / reinforcement learning). You will also learn the various concepts are involved in core data analysis packages (NumPy, Pandas, Matplotlib) with example codes.
This chapter will expose you to various fundamental concepts involved in feature engineering, supervised learning (linear regression, nonlinear regression, logistic regression, time series forecasting and classification algorithms unsupervised learning (clustering techniques, dimension reduction technique) with the help of scikit-learn and statsmodel packages.
in this chapter you’ll learn advanced topics around different model diagnosis, which covers the common problems that arise, and various tuning techniques to overcome these issues to build efficient models. The topics include choosing the correct probability cutoff, handling an imbalanced dataset, the variance, and the bias issues. You’ll also learn various tuning techniques such as ensemble models and hyperparameter tuning using grid / random search.■ IntroduCtIon xxi
Statistics says 70% of the data available in the business world is in the form of text, so text mining has vast scope across various domains. You will learn the building blocks and basic concepts to advanced NLP techniques. You’ll also learn the recommender systems that are most commonly used to create personalization for customers.
There has been a great advancement in the area of Artificial Neural Network (ANN) through deep learning techniques and it has been the buzzword in recent times. You’ll learn various aspects of deep learning such as multilayer perceptrons, Convolution Neural Network (CNN) for image classification, RN (Recurrent Neural Network) for text classification, and transfer learning. And you’ll also learn the q-learning example to understand the concept of reinforcement learning.
Refer to the book below
Doc : https://www.pdfdrive.com/mastering-machine-learning-with-python-in-six-steps-e46527933.html