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Discover exciting and fast-moving AI fields with online courses. Learn artificial intelligence by studying natural language processing, strengthening training, predictive analysis, deep neural networks, image processing, human brain, and more. Online Eduranz Artificial Intelligence Certification Course with TensorFlow is an industry-leading CNN certification training (Conversion Neural Network) for CNN Perceptron, TensorFlow, TensorFlow code, graphic visualization, transfer training and repetitive Deep Learning networks, Hard & TFLearn API, in-depth GPU training, Redistribution and hyperparameter through practical projects. Learn AI with this online artificial intelligence course using Tensorflow.
|Section 1: Introduction|
|Introduction to Startups Details||FREE||00:30:00|
|Introduction to entrepreneurial management Details||00:20:00|
|Section 2: The Ecosystem|
|Different types of entrepreneurship Details||00:30:00|
|Entrepreneurial Ecosystem and Legal Fundamentals Details||01:00:00|
|Section 3: Marketing Startups|
|Value proposition Details||00:40:00|
|Product development Details||00:25:00|
Artificial Intelligence and Machine Learning is taking over every other industry. From small companies to big tech-giants, all are implementing AI and ML to grow in their respective fields. On one hand, where AI and ML are so in demand, there is a shortage of skilled Artificial Intelligence Engineer and Machine Learning Engineer. Artificial Intelligence and Deep Learning Training Certification course by Eduranz is designed and structured by industry experts based on industry requirements and demands. This training program will help you master Python and its libraries for Artificial Intelligence, TensorFlow, and Keras. As part of this project-based training program, you will learn about Time Series Analysis, Predictive Analytics, Graphical Models, Reinforcement Learning, Convolutional Neural Networks, Recurrent Neural Networks, and many more. As part of Eduranz’s training program, you will get to work on real-time projects and assignments, which are also developed keeping in mind their implications in the real-world industry. The training program ends with tests, there will be a quiz that will perfectly reflect the type of questions asked in the job interviews, thus helping you score better marks.
Professionals who want to build a career in AI and Deep Learning Students aspiring to become AI Engineer and Deep Learning Engineer
The pre-requisites for Eduranz’s AIand Deep Learning Training Certification course are:
•Basic knowledge of programming and mathematics are beneficial
Artificial Intelligence and Deep Learning Training Certification course has been designed and curated by industry professionals that prepare you for the industry that demands skilled Artificial Intelligence Engineer. As part of Eduranz’s training program, you will get to work on real-time projects and assignments, which are also developed keeping in mind their implications in the real-world industry. Upon completion of the project work, which will be reviewed by a panel of industry experts, and upon scoring at least 60% marks in the quiz, you will be awarded the Artificial Intelligence and Deep Learning Training Certification by Eduranz.
The field of machine learning and its implications for the field of artificial intelligence, the advantages of machine learning over other conventional methods, the introduction of deep learning in machine learning, which is different from all other machine learning methods, machine learning training Training data systems et al. controlled and unsupported training, classification, and regression, controlled training, grouping, and association, without supervision, algorithms used in this type of training. Introduction to AI, Introduction to Artificial Neural Networks, Control of Learning with Artificial Neural Networks, Concepts of Machine Learning, Statistics Basics, Probability Distribution, Hypothesis Testing, Hidden Markov Model.
Introduction to multilayer networks, concepts of deep neural networks, regularization. Multilayer perceptron, capacity and retrofit, neural network hyperparameters, logic gates, various activation functions in neural networks such as Sigmoid, ReLu and Softmax, hyperbolic functions. Reproduction, convergence, forward propagation, conversion, hyperparameter.
Various techniques used in artificial neural network training, gradient descent rules, perceptual rules, training rate regulation, stochastic processes, optimization techniques, regularization techniques, regression techniques Lasso L1, Ridge L2, missing gradients, missing training, pre-support that is not supported -Training, initialization of Xavier, disappearing color gradient.
How Deep Learning, Activation Features, Perceptron Illustration, Perceptron Training, Important Perceptron Parameters, Multilayer Perceptron What is Tensorflow? TensorFlow library, AI accelerator Tensor Processing Unit (TPU) programmable AI, Tensorflow core code, graphics, constants, substitutes, variables, step-by-step implementation, Hard.
High-level neural networks to work with TensorFlow, define complex multi-output models, create models with Hard, consistent and functional compositions, batch normalization, provide Hard with TensorBoard, and adapt neural learning networks.
Providing neural networks using the TFLearn API, defining and creating models using TFLearn, and using TensorBoard with TFLearn.
Illustration of the human mind with deep neural networks, various building blocks of artificial neural networks, DNN architecture, building blocks, DNN reinforcement training concepts, various parameters, layers, activation functions, and optimization algorithms at DNN.
What is convolutional neural networks, understanding CNN architecture, use of CNN cases, what constitutes an aggregation layer, how CNN is visualized, how convolutional neural networks are refined, what is transmission training, and understanding of repetitive neural networks, Feature maps, kernel filters, joins, provides spasms in the TensorFlow neural network.
Introduction to the RNN Model, Use of RNN Usage Cases, Sequence Modeling, RNN Propagation Training, Short Term Memory (LSTM), Recursive Neural Tensor Theory, Repeated Neural Network Models, RNN Core Cells, Expanded RNN, RNN Training, Dynamic Series Prediction.
Introduction to GPUs and how they differ from processors, the importance of GPUs in deep learning, back and forth training techniques, GPUs with simpler nuclear and simultaneous hardware.
Introducing RBM and auto-encoders, providing deep neural networks, shared RBM filtering, auto-encoder capabilities, auto-encoder applications.
Natural language processing
An automated call bot uses one descriptive technique
Louis from Microsoft
Sequence to sequence model (LSTM).
Business Context/ Objective
To develop a Machine learning algorithm to identify the most optimal ratio/ aspects to allocate funds/ spending proportionately by organizations in different areas of expenses like
1- Research and Development 2- Marketing 3- Employee cost 3- HR and Administration cost 4- Insfrasture cost … etc
Identifying the optimal ratio of the amount of allocation of funds to various segments is of utmost importance. This would also help the Management team with below aspects
To increase the revenue and profitability To better design the Marketing strategies to allocate the internal resources better
The algorithm would help in identifying the relationship between profit and various types of expenses individually by an organization (as mentioned above)
Within this final project of Data Science using R -we will include all the essential steps which a typical Machine learning-based project should have with Multiple Regression The data will be having multiple sources and format mainly having details about various organizations, their profit, and their expenses in different areas We will use the bulky input data set to be imported from different sources, the combined rows will contain 2k+ rows (tentative) with all the required column
At the time of enrollment team Eduranz will assign one mentor for you and he will be guiding you in this lifetime Journey.
24/7 Tech Adviser Support
Lifetime 24/7 Technical and Non Technical Support from team Eduranz.
Get Lifetime opportunity to access and attend the live sessions multiple times.
Assignments & Quizzes
Every module will be followed by certification based assessment and quiz.
Become a Certified Professional.
Team Eduranz will update your Resume before forwarding it to our 60+ global Clients.
To run Python, your system must meet the following basic requirements: 32- or 64-bit operating system 1 GB RAM The statement uses Anaconda and Jupiter notebooks. The E-learning video contains detailed installation instructions.
All of our highly qualified trainers are industry experts with at least 10-12 years of relevant teaching experience. Each of them underwent a rigorous selection process that included screening profiles, technical assessments, and training demonstrations before being certified for training. We also ensure that only high-level graduates live in our faculty.
Eduranz offers a 24/7 request solution and you can pick up your tickets at any time from our dedicated support team. You can use email support for all your questions. If your request is not answered via email, we can also arrange one-on-one discussions with the faculty. You will be glad to know that you can switch to Eduranz support after completing training. We also don’t limit the number of tickets you can collect when solving questions and doubts.
Eduranz offers independent learning for those who want to learn at their own pace. This training also gives you the benefits of email questions, tutorial sessions, 24×7 support, and access to modules or LMS for lifelong learning. In addition, you will receive the latest version of learning material at no additional cost. Independent Eduranz training is 75% lower than teacher-led online training. If you experience problems while studying, we can arrange virtual courses directly with the trainer at any time.
Eduranz offers the most up-to-date, relevant and valuable projects in the real world as part of the training program. In this way, you can integrate what you have learned in the real industry. Each training is delivered with various projects where you can thoroughly test your skills, learning and practical knowledge so that you are well prepared for the industry. They work on very interesting projects in the fields of high technology, e-commerce, marketing, sales, networking, banking, insurance and more. After successfully completing your project, your skills will be counted as a result of six months intensive industry experience.
Eduranz actively supports all trainees who have successfully completed the training. That’s why we are involved in more than 80 top MNCs worldwide. This way you can be in exclusive organizations such as Sony, Ericsson, TCS, Mu Sigma, Standard Chartered, Cognizant, Cisco and other similar-sized companies. We also support you during job interviews and preparation of your CV.
In any case, you can switch from self-directed self-training to online training only by paying an additional amount and participating in the next set of training that will be specifically notified to you.
After completing the Eduranz Training Program along with all real projects, tests and assignments and achieving at least 60% points in the qualification exam; You received a certificate that was certified by Eduranz. This certification is recognized by Eduranz’s partner organizations, which includes a lot of top MNCs worldwide that are also part of the Fortune 500 list.
In our job support program, we help you start your dream job by sharing your resume with potential tenants, helping you make resumes, and preparing you for interview questions. Eduranz training should not be seen as an employment agency or employment guarantee, because the entire employment process is handled directly between the student and the employing company and the final choice is always left to the employer.