You can order the standard machine learning texts from most to least math-y, and least to most modern:
- Pattern Recognition and Machine Learning (Bishop 2007)
- Machine Learning: A Probabilistic Perspective (Murphy 2012)
- Deep Learning (Goodfellow, Bengio, Courville 2016)
If you want cutting-edge material, read the Deep Learning book (which is still quite technical, though some of its content may be outdated in a few years). If you want timeless mathematical foundations very clearly presented, read Bishop. Murphy is a good middle ground.
If you're self-teaching and have trouble focusing on a textbook for long periods, the Stanford CS229 lectures combined with Andrew Ng's course notes and assignments are probably the best resource. They are still quite rigorous and working through them will give you a solid foundation, after which you'll be more prepared to understand the deeper content in any of the texts above.
- Pattern Recognition and Machine Learning (Bishop 2007)
- Machine Learning: A Probabilistic Perspective (Murphy 2012)
- Deep Learning (Goodfellow, Bengio, Courville 2016)
If you want cutting-edge material, read the Deep Learning book (which is still quite technical, though some of its content may be outdated in a few years). If you want timeless mathematical foundations very clearly presented, read Bishop. Murphy is a good middle ground.
If you're self-teaching and have trouble focusing on a textbook for long periods, the Stanford CS229 lectures combined with Andrew Ng's course notes and assignments are probably the best resource. They are still quite rigorous and working through them will give you a solid foundation, after which you'll be more prepared to understand the deeper content in any of the texts above.