Learning the key elements for a successful machine learning project can be daunting. There is a lot of confusion about Machine Learning and Artificial Intelligence today. It's not magic, it's another form of statistics, computer science, and development that is powerful but can be easily misunderstood. What used to make Machine Learning a very difficult and confusing space to be in, has dramatically changed for the better. So what does successful Machine Learning look likeand how have things changed?

Successful artificial intelligence (AI) and machine learning (ML) initiatives bring value to the entire organization by delivering insights to the right person or system at the right time within the right context. But many organizations are unable to do this because they are too focused on algorithms. Data science is more than neural networks and deep learning! Organizations need to instead leverage people, processes, and technology to infuse AI and ML into business processes. It sounds simple, only four ingredients: flour, water, yeast, and a bit of salt.

One of the most common misconceptions about machine learning is that success is solely due to its dynamic algorithms. In reality, the learning potential of those algorithms and their models are driven by the data preparation, staging and delivery. When suitably fed, machine learning algorithms work wonders. Their success, however, is ultimately rooted in the data logistics. Data logistics are integral to how sufficient training data is accessed.

This upcoming 45-minute webinar explores efficient methods to explore and organize complex data, how to marry multiple datasets for feature engineering, and optimal target selection and how to address information leakage.