|Course or Certification Name||Category||Location||Mode of learning|
|Marketing Applications of Machine Learning and Artificial Intelligence||Machine Learning||Classroom|
|Machine Learning with Python Training||Machine Learning||Noida , Delhi , Gurgaon , Chandigarh , Bangalore , Hyderabad , Chennai , Ernakulam||Online Classroom|
|Learn Machine Learning By Building Projects||Machine Learning||Online self study|
|Applied Machine Learning With R||Machine Learning||Online self study|
|Machine Learning In The Cloud With Azure Machine Learning||Machine Learning||Online self study|
|Machine Learning With TensorFlow The Practical Guide||Machine Learning||Online self study|
|The Deep Learning Masterclass: Classify Images with Keras!||Machine Learning||Online self study|
|The Top 5 Machine Learning Libraries in Python||Machine Learning||Online self study|
|Become a Machine Learning Engineer||Machine Learning||Online self study|
|Post Graduate Certificate in Artificial Intelligence & Deep Learning||Machine Learning||Noida , Delhi , Gurgaon , Chandigarh , Bangalore , Hyderabad , Chennai , Ernakulam||Online Classroom|
|Become a Deep Reinforcement Learning Expert||Machine Learning||Online self study|
|Intro to Machine Learning||Machine Learning||Online self study|
|PG Certification in Machine Learning and Deep Learning||Machine Learning||Online self study|
|Machine Learning||Machine Learning||Online self study|
|Google Cloud Platform Big Data and Machine Learning Fundamentals||Machine Learning||Online self study|
This management development programme will introduce you to some of the key concepts behind machine learning and artificial intelligence, including the differences between Strong and weak AI. This MDP will explain the different approaches to AI, including machine learning, and the practical applications for new AI-enhanced technologies. With the help of business frameworks, understand how these prediction machines can be built in your business context.
Machine Learning with Python course discusses concepts of the Python language such as file operations, sequences, object-oriented concepts, etc. along with some of the most commonly leveraged Python libraries like Numpy, Pandas, Matplotlib, etc. The course will then move on to introduce learners to the detailed mechanisms of Machine Learning. Learners will understand in detail the significance of the implementation of Machine Learning in the Python programming language, and leverage this knowledge in their role as data scientists. | After completing the course, one would have learnt about tools to train machines based on real-world situations using Machine Learning algorithms, as well as to create complex algorithms based on concepts related to deep learning and neural networks. During the latter stage of the course, learners will be introduced to real-world use cases of Machine Learning with Python for a holistic learning experience which would prepare them to create applications efficiently.
The course focuses on breaking down the important concepts, algorithms, and functions of Machine Learning. The course starts at the very beginning with the building blocks of Machine Learning and then progresses onto more complicated concepts. Each project adds to the complexity of the concepts covered in the project before it.
Applied Machine Learning with R is a hands-on course Machine Learning and Artificial Intelligence course. This course covers the core concepts of machine learning, along with machine learning algorithms. You will also learn how to implement those machine learning algorithms with R and after completion of the course, you will be able to use them in your own projects.
In this course, we will discuss Azure Machine Learning in detail. You will learn what features it provides and how it is used. We will explore how to process some real-world datasets and find some patterns in that dataset.This course teaches you how to design, deploy, configure and manage your machine learning models with Azure Machine Learning. The course will start with an introduction to the Azure ML toolset and features provided by it and then dive deeper into building some machine learning models based on some real-world problems
This course gives an insight into the basics of Tensorflow covering topics like tensors, operators and variables. It is a good option to master machine learning, its types and various main algorithms including linear regression. Furthermore, this course also covers advanced machine learning like a neural network, convolution neural network and others. Here, you’ll also gain the practice by implementing it in a project on Deep Neural Network. | Machine Learning With TensorFlow is a professional programme. It provides a complete insight of Tensorflow, tensors, operators and variables. Machine Learning With TensorFlow also includes other topics like – Getting started with Tensorflow, Tensorflow Basics, Machine Learning Basics, Main Algorithms including linear regression, Advanced Machine Learning
The Deep Learning Masterclass: Classify Images with Keras is an interactive course that is designed to help a beginner turn into a data modeler. The course starts with basic concepts of Keras and goes to the advanced concepts of machine learning and neural networks. The participants will also get to build their image classifier model from scratch.
A machine learning engineer is a person who builds predictive models, scores them and then puts them into production so that others in the company can consume or use their model. They are usually skilled programmers that have a solid background in data mining or other data related professions and they have learned predictive modeling. In this course, we are going to take a look at what machine learning engineers do. We are going to learn about the process of building supervised predictive models and build several using the most widely used programming language for machine learning: Python. There are literally hundreds of libraries we can import into Python that are machine learning related. A library is simply a group of code that lives outside the core language. We “import it” into our work space when we need to use its functionality. We can mix and match these libraries like Lego blocks.
Learn advanced machine learning techniques and algorithms and how to package and deploy your models to a production environment. Gain practical experience using Amazon SageMaker to deploy trained models to a web application and evaluate the performance of your models. A/B test models and learn how to update the models as you gather more data, an important skill in industry. | This program is intended for students who already have knowledge of machine learning algorithms.
The PG Certificate Program in Artificial Intelligence & Deep Learning from Manipal Academy of Higher Education (MAHE) comes with rich online training pedagogy, hands-on labs, and instructor led webinars & Tech talks to help learners ramp up their AI/DL skills rapidly and be a part of this highly paid, in demand profession.
Learn cutting-edge deep reinforcement learning algorithms—from Deep Q-Networks (DQN) to Deep Deterministic Policy Gradients (DDPG). Apply these concepts to train agents to walk, drive, or perform other complex tasks, and build a robust portfolio of deep reinforcement learning projects.
Learn foundational machine learning algorithms, starting with data cleaning and supervised models. Then, move on to exploring deep and unsupervised learning. At each step, get practical experience by applying your skills to code exercises and projects. | This program is intended for students with experience in Python, who have not yet studied Machine Learning topics.
Complete all courses successfully and receive a Post-Graduate certificate. Become part of the upGrad community with the PG alumni status from IIIT Bangalore. Become an ML Engineer by learning how to recognise gestures, analyse X-ray images, predict customer churn across telecom providers, and lots more
This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: | (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). | (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). | (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). | The course will also draw from numerous case studies and applications, so that you'll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.
This 1-week accelerated on-demand course introduces participants to the Big Data and Machine Learning capabilities of Google Cloud Platform (GCP). It provides a quick overview of the Google Cloud Platform and a deeper dive of the data processing capabilities.