Machine Learning (ML) has become a hot topic in recent years because of its potential benefits for many companies. Especially some big companies such as Amazon, Google and Microsoft have shown several successful cases on integrating AI capability in their own businesses. Although the interest in machine learning is rapidly increasing in almost all the business sectors, there is still a lack of knowledge of how to apply ML in small companies. Different from the big companies, smaller companies usually do not have massive resources such as the capital investment, infrastructures and expertise in machine learning. The lack of resources does not only make it difficult to adopt machine learning in small companies, but also makes the existing solutions from big companies difficult to be applied in the more general business environments. This thesis aims to help small companies to adopt machine learning by integrating the machine learning activities in their existing agile software development processes. Thus the companies can reuse most of their existing resources and let the developers apply machine learning by following the proposed model. Throughout the work, a literature review, a survey and an interview were conducted to find the challenges for small companies regarding adopting machine learning. The identified major challenges include affording to hire the right talents working with machine learning, the sufficient amount of data at their disposals, the general knowledge regarding the Machine Learning Process (MLP) and what can/cannot be done with machine learning. These challenges were identified as requirements for the proposed model to make machine learning more accessible to small companies. The proposed model called Machine Learning for All (ML4A), is divided into two major sections for the investigated challenges. The first section is a Machine Learning Usage Model to give a guideline to the non-experts for a better view of what can be accomplished with machine learning techniques. This should be used before starting a project related to machine learning. To implement the project, a process model called Agile Machine Learning (AML) is proposed in the second section. AML is a proposition of how machine learning can be integrated into an agile software engineering process, which also fits the requirements for small companies. A case study was conducted at a small Swedish company called SportAdmin to validate ML4A. The results showed the effectiveness of the proposed model by letting non-experts in machine learning to apply machine learning techniques in a small company.