Machine Learning - Conclusion

This tutorial has introduced you to Machine Learning. Now, you know that Machine Learning is a technique of training machines to perform the activities that a human brain can do, but just a little faster and better than an average human. In games such as Chess, AlphaGO, which are considered very complex, the machines have been able to beat human champions. The machines have shown us that they can perform human tasks in several areas and can assist humans in living more fulfilling lives.

You can choose between Supervised and Unsupervised Machine Learning. It is best to use Supervised Learning if you have less data and clearly labelled data for training. Unsupervised Learning is generally more effective and gives better results. If you have a huge amount of data readily available, go for deep learning techniques. You've also learned about Reinforcement Learning and Deep Reinforcement Learning, and what Neural Networks are and what they're used for.

As a final step toward developing your own machine learning models, you looked at various development languages, IDEs, and platforms. As a next step, you need to learn and practice each machine learning technique. This subject is vast, so there is width, but if you consider depth, each topic can be learned in a few hours. Each topic is independent of the others. To begin studying Machine Learning, you should take one topic at a time, learn it, practice it, and then implement the algorithm/s using the language of your choice. By practicing one topic at a time, you will acquire the wide range required of a Machine Learning expert very quickly.

Good Luck!

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