The Forex market otherwise known as the foreign exchange market is the center of attention for many people on a daily basis. Due to its high cash flow, people think they can make huge amounts of money in short amounts of time. But, due to their lack of knowledge and information as well as their emotional behaviors, their capital is lost. In fact, 95% of those who are active in the Forex market ultimately lose their money. In this thesis, we are trying to develop two new trading models using the machine learning to predict the future directions of the price movement in the foreign exchange market. In the first model, using a Recurrent Neural Network, the current market conditions and price movements are analyzed to create long or short trade decisions on a single currency pair in a minute by minute fashion. The model is capable of achieving 22.5% in the Kelly risk-adjusted return criteria. The second model uses a Reinforcement Learning algorithm in combination with Convolutional Neural Networks to choose the optimal action in the Forex market. The main difference between the second model and the first model is that in the second model, in addition to considering the profit or loss of each trade decision, the cost of entering into the transactions is also considered. As a result, the developed model will be more useful in the real market conditions. The model has the ability to gain 389 pips of profit over a period of two weeks. Also, a unique pre-processing technique is used to preserve the time series dependencies of price movements in the developed models. Key Words : Forex Market Prediction, Recurrent Neural Networks, Deep Learning, Reinforcement Learning.