◇ Could this help me?
Yes. The three named resources are genuinely good and genuinely free: Raschka is a careful, well-regarded machine learning educator, the practical Python track gets you building early rather than staying theoretical, and the Stanford lecture is a legitimate university course rather than repackaged content. The sequencing is also right, since foundations before application before applied agent work is the order that actually sticks. General practice, and the only thing to discount is the framing that this list determines whether you matter as an engineer for the next several decades, which is motivation rather than information.
I am a software engineer who wants to move toward AI engineering, and I have a set of free course recommendations covering machine learning foundations, practical machine learning with Python, and applied work with agents, prompts, and retrieval. Help me turn that into a plan I will actually finish: ask me how much time per week I realistically have, then sequence the material into weekly blocks, and for each block define one small thing I will build so the learning is applied rather than passive. Point me only at freely published material, and tell me when a resource is out of date rather than recommending it anyway.