Team Datatechnophileprovides the below

  • • Industry 4.0 Live Training
  • • Assignments
  • • Mini Projects
  • • Industry level live projects
  • • Global Certification assistance
  • • Career Assistance & Mentorship




Introduction
To Data Science

Introduction to Data Science specializes in providing key foundational skills for a career in data science or further advanced learning in the field. This training will introduce you to what data science is and how to use the concepts in real time. This training also helps to discover the applicability of data science across fields, and learn data analysis can help you make data driven decisions. You will find that you can kick start your career in the field without prior knowledge of computer science or programming languages. Below major concepts will be covered in this training:

  • • Python Programming
  • • R Programming
  • • Statistics
  • • Probability
  • •SQL

Concepts like big data, statistical analysis, and relational databases, and gain familiarity with various open source tools and data science programs used by data scientists, like Jupyter notebooks, RStudio, Git Hub, and SQL. It also includes complete hands-on labs and projects to learn the methodology involved in tackling data science problems and apply the newly acquired skills and knowledge to real world data sets

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Machine Learning

Machine Learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine Learning is so pervasive today that you probably use it dozens of times a day knowing it. Many researchers also think it is the best way to make progress towards human-level AI. During this training we will teach the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. This training provides a broad introduction to machine learning with the below topics:

  • • Supervised Learning (decision trees, ensemble methods, KNN, PCA, parametric / non-parametric algorithms, support vector machines, kernels, neural networks and naïve bayes)
  • • Unsupervised Learning (HMM, K Means, clustering, dimensionality reduction, recommender systems, deep learning)
  • • Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI

This training will also draw from numerous case studies and applications, so that you’ll 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.

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Artificial Intelligence

Team Datatechnophile teaches Artificial Intelligence in the most practical way with the below concepts:

  • • Artificial Neural Networks
  • • Convolutional Neural Networks
  • • Recurrent Neural Networks
  • • Deep Learning Frameworks

We will ensure to expose to various issues and concerns surrounding Artificial Intelligence such as ethics and bias, & jobs, and get advice from or experts about learning and starting a career in AI. Live projects will be made available to get maximum exposure. Our course can also be applied to multiple specializations or professional certificate programs

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Big Data

Our big data training can provide through hands-on experience with the tools and systems used by big data scientists and engineers. Previous programming experience is not required and you will be guided through the basics of using Hadoop with mapreduce, spark, pig and hive. By following along with provided code, you will experience how one can perform predictive modeling and leverage graph analytics to model problems. We also prepare you how to ask the right questions about data, communicate effectively with data scientists, and do basic exploration of large, complex datasets. In one of our projects we will also teach how to apply the skills learned to do basic analysis of big data

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