With the material just on this site and our textbook, you can learn the fundamentals of Artificial Intelligence by following the plan laid out below.
These are online courses that can further enhance your ability in ML
This course covers both machine learning fundamentals and the terminology of traditional model training.T he course also later delves into state-of-the-art advancements in the field. The only drawback of this course is that it uses MATLAB for the assignments, but today, this language is not commonly used for ML model training. You could complete the exercises in Python or R. (This course is free to audit, but you will need to pay for the certificate)
This course is great for pure machine learning and for understanding the theory behind the training and testing of models. It makes it easy to understand current ML optimization techniques, from loss minimization to representation and regularization. It is mainly conceptual with a few applications at the end (Cancer Prediction + Literature). The drawback is that it is quite short (less than 6-8 hours of content). However, it does take the emphasis off the math.
This specialization is useful for students who have knowledge of machine learning and want to break into deep learning. This package of 5 courses (which can be audited at no cost) will allow students to gain a fundamental understanding of deep learning, one of the most sought after skills in technology. The course teaches "Convolutional networks, RNNs, LSTM, Adam, vropout, BatchNorm, Xavier/He initialization, and more.
This is an introductory machine learning course which "which gives an overview of many concepts, techniques, and algorithms in machine learning, beginning with topics such as classification and linear regression and ending up with more recent topics such as boosting, support vector machines, hidden Markov models, and Bayesian networks. The course will give the student the basic ideas and intuition behind modern machine learning methods
If you want a high-level overview of machine learning and AI in general, then this is the course for you: "This computer science course provides an overview of AI, and explains how it can be used to build smart apps that help organizations be more efficient and enrich people's lives. It uses a mix of engaging lectures and hands-on activities to help you take your first steps in the exciting field of AI." It applies basic concepts in AI for building simple models, creating chatbots, and for design speech and vision recognition/detection systems.
Harnessing the full potential of artificial intelligence requires adaptive learning systems. Learn how Reinforcement Learning (RL) solutions help solve real-world problems through trial-and-error interaction by implementing a complete RL solution from beginning to end. By the end of this Specialization, learners will understand the foundations of much of modern probabilistic artificial intelligence (AI) and be prepared to take more advanced courses or to apply AI tools and ideas to real-world problems.
In the first half of the course we will cover supervised learning techniques for regression and classification. In this framework, we possess an output or response that we wish to predict based on a set of inputs. We will discuss several fundamental methods for performing this task and algorithms for their optimization. Our approach will be more practically motivated, meaning we will fully develop a mathematical understanding of the respective algorithms, but we will only briefly touch on abstract learning theory.
This course will introduce the learner to applied machine learning, focusing more on the techniques and methods than on the statistics behind these methods. The course will start with a discussion of how machine learning is different than descriptive statistics, and introduce the scikit learn toolkit through a tutorial. The issue of dimensionality of data will be discussed, and the task of clustering data, as well as evaluating those clusters, will be tackled. Supervised approaches for creating predictive models will be described, and learners will be able to apply the scikit learn predictive modelling methods while understanding process issues related to data generalizability