One of the best way to get better at ANNs and machine learning is to watch a lecture from an expert and work your way along with it. If you do so, you get the best of both the worlds – you learn from the experts across the globe and also get hands on knowledge. Attached video link is from one of the best machine learning teacher & expert across the globe. It is the complete playlist for the lectures on Stanford Machine Learning (CS229) by Prof. Andrew Ng at Stanford. In these lectures he covers machine learning concepts which include Linear / Logistic regression, supervised and unsupervised learning, learning theory, reinforcement learning and adaptive control. He discusses techniques like Naive Bayes, Neural Networks, SVM, Bayesian statistics, Regularization, Clustering, PCA and ICA. He also discusses some recent applications of Machine learning such as robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing. If you are a complete newbie and want a lucid introduction, which challenges you at times, go with these videos

https://www.coursera.org/learn/machine-learning

https://www.youtube.com/watch?v=UzxYlbK2c7E&list=PLA89DCFA6ADACE599

I am not involved in artificial neural networks, but I checked here and found this link that I hope would be helpful for you: https://www.researchgate.net/search.Search.html?type=publication&query=best%20for%20learn%20about%20ANN%20and%20ANN%20modelling%20for%20input%20parameters%20and%20output%20response%20in%20machining%20process

Best regards,

Debra

One of the best way to get better at ANNs and machine learning is to watch a lecture from an expert and work your way along with it. If you do so, you get the best of both the worlds – you learn from the experts across the globe and also get hands on knowledge. Attached video link is from one of the best machine learning teacher & expert across the globe. It is the complete playlist for the lectures on Stanford Machine Learning (CS229) by Prof. Andrew Ng at Stanford. In these lectures he covers machine learning concepts which include Linear / Logistic regression, supervised and unsupervised learning, learning theory, reinforcement learning and adaptive control. He discusses techniques like Naive Bayes, Neural Networks, SVM, Bayesian statistics, Regularization, Clustering, PCA and ICA. He also discusses some recent applications of Machine learning such as robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing. If you are a complete newbie and want a lucid introduction, which challenges you at times, go with these videos

https://www.coursera.org/learn/machine-learning

https://www.youtube.com/watch?v=UzxYlbK2c7E&list=PLA89DCFA6ADACE599

Thankyou very much Mahmoud Omid sir and Debra maam for your valuable suggestions.