There is no doubt that Machine Learning is a tough subject, and in-depth knowledge, in particular, requires a lot of Mathematics and complex terminology and is very tough to master. How do you learn it better if the subject matter is that tough? Choose a course that can explain this complex topic in simple words. We are actually blessed that we have many excellent instructors like Andrew Ng, Jeremey Howard, and Kirill Eremenko on Udemy, who are not just experts in deep learning but also excellent instructors and teachers. I firmly believe that every programmer should learn about Cloud Computing and Artificial Intelligence, as these two will drive the world in the coming years.

If you are a software developer who wants to build scalable AI-powered algorithms, you need to understand how to use the tools to build them. This Specialization will teach you best practices for using TensorFlow, a popular open-source framework for machine learning. In this fourth course, you will learn how to build time series models in TensorFlow. You'll first implement best practices to prepare time series data. You'll also explore how RNNs and 1D ConvNets can be used for prediction.

Here is a compilation of numerous topics, organized from fundamental to advanced knowledge, that are required for deep learning specialization. If you don't feel capable enough to understand on your own, DM me on Linkedin and we'll see if we can work something out.

If you are a software developer who wants to build scalable AI-powered algorithms, you need to understand how to use the tools to build them. This course is part of the upcoming Machine Learning in Tensorflow Specialization and will teach you best practices for using TensorFlow, a popular open-source framework for machine learning. In Course 2 of the deeplearning.ai TensorFlow Specialization, you will learn advanced techniques to improve the computer vision model you built in Course 1. You will explore how to work with real-world images in different shapes and sizes, visualize the journey of an image through convolutions to understand how a computer "sees" information, plot loss and accuracy, and explore strategies to prevent overfitting, including augmentation and dropout.

If you are a software developer who wants to build scalable AI-powered algorithms, you need to understand how to use the tools to build them. This Specialization will teach you best practices for using TensorFlow, a popular open-source framework for machine learning. In Course 3 of the deeplearning.ai TensorFlow Specialization, you will build natural language processing systems using TensorFlow. You will learn to process text, including tokenizing and representing sentences as vectors, so that they can be input to a neural network.

This course will teach how to become a leader, diagnose machine learning errors, and understand complex ML settings. If you see the people reviews, some of them liked the course since it shows them in-depth how deep learning and AI works step by step with simple quizzes and the videos were good in production. Some of them didn't like the user experience, maybe because of the hard math implemented in this course, or they didn't like the videos production and the quizzes being very easy and simple, but 4.8 stars are enough to convince people to enroll in this course and start a new career in this field. And here is the link to join this course - Deep Learning Specialization by Andrew Ng. Artificial Intelligence is widespread in almost every product or service that we use in our daily life, and we can not imagine this new age to get developed more and more without this technology, so learning this field is really good if you are planning to have a career in this industry or just for fun and educational purposes.

You may already know this, but let me say this again, Deep learning is a field that has a vast range of applications in the current world. Anyone from any area can enter this ocean and gain their own insights into its application. From its multiple applications in Computer Vision to its applications in Speech processing, mostly anything can be achieved using this fantastic science. This is not an article to discuss the history of DL or the applications of DL. It summarizes the mistakes made in an idiot's 5–6 month journey into Deep Learning.

In this article, we've listed all Machine Learning and Deep Learning courses by Andrew Ng, an excellent teacher from Standford University, and a tech-entrepreneur. The Machine Learning and Deep Learning courses given below are all available on Coursera in case you are interested in enrolling in any one of them. The Machine Learning course by Stanford and popularized by Andrew Ng's teaching is the best certification course in Machine Learning you can go for. The course is 11 weeks long and covers almost everything that you need to know about Machine Learning with great examples and assignments. The course has a 4.9/5 average rating from over 160,000 student ratings.

In the third course of the Deep Learning Specialization, you will learn how to build a successful machine learning project and get to practice decision-making as a machine learning project leader. By the end, you will be able to diagnose errors in a machine learning system; prioritize strategies for reducing errors; understand complex ML settings, such as mismatched training/test sets, and comparing to and/or surpassing human-level performance; and apply end-to-end learning, transfer learning, and multi-task learning. This is also a standalone course for learners who have basic machine learning knowledge. This course draws on Andrew Ng's experience building and shipping many deep learning products. If you aspire to become a technical leader who can set the direction for an AI team, this course provides the "industry experience" that you might otherwise get only after years of ML work experience.

In the fifth course of the Deep Learning Specialization, you will become familiar with sequence models and their exciting applications such as speech recognition, music synthesis, chatbots, machine translation, natural language processing (NLP), and more. By the end, you will be able to build and train Recurrent Neural Networks (RNNs) and commonly-used variants such as GRUs and LSTMs; apply RNNs to Character-level Language Modeling; gain experience with natural language processing and Word Embeddings; and use HuggingFace tokenizers and transformer models to solve different NLP tasks such as NER and Question Answering. The Deep Learning Specialization is a foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology. It provides a pathway for you to take the definitive step in the world of AI by helping you gain the knowledge and skills to level up your career. About the Deep Learning Specialization The Deep Learning Specialization is a foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology.