Artificial Intelligence Full Course
1 lesson, about 5 hours
Five hours covering artificial intelligence end to end: what it is, how machine learning and deep learning sit inside it, the algorithms in common use, and where the field is applied.
Growth Hacking
A paid course from GrowthHackers University.

Translating Data Into Business Intelligence with Sina Fak is a paid course on GrowthHackers University: 28 lessons across 6 modules.
The provider describes it as: "Turn data into actionable insights, optimize costs, and drive continuous growth with the IIEA Framework."
It is organised as: - Foundation - Sample Lesson - Insights - Ideation - Experimentation - Analysis
The course is taken on their platform.
1.1 Intro
1.2 Data Analytics vs. Business Intelligence 1.3 7 Ways Combining Business Intelligence Insights And Experiments Will Exponentially Grow Your Business 1.4 The Evolution of Business Intelligence Systems
2.1 Extract Transform Load – The Data Infrastructure Required For Business Intelligence 2.2 Evaluating what data you need to capture and how 2.3 Mapping your customer buying journey 2.4 Mapping your business ecosystem 2.5 Data segmentation analysis 2.6 Example of Insights from real customers 2.7 Evaluating the quality of insights generated
3.1 The Scientific Method 3.2 Setting objectives for ideation 3.3 Formulating your hypothesis 3.4 Setting KPIs and learning objectives 3.5 Prioritizing ideas 3.6 Evaluating the quality of ideas generated
4.1 Using experiments as a tool to translate data into intelligence 4.2 Managing an experiment plan (PIE KPIs Hypothesis) 4.3 Establishing an ongoing experiment process and culture 4.4 Evaluating quality of experiments generated
5.1 Post test analysis 5.2 Experiment Segmentation 5.3 Reporting visualizing modeling data 5.4 Communicating results across your team 5.5 Conclusions 5.6 Special Offer for INSIGHTS by ConversionAdvocates BI Live Class – Free Resource
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1 lesson, about 5 hours
Five hours covering artificial intelligence end to end: what it is, how machine learning and deep learning sit inside it, the algorithms in common use, and where the field is applied.
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