What Is AI Engineer Course?

Datamites Team Datamites Team
May 16, 2024 Updated: May 16, 2024
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What Is AI Engineer Course?
What Is AI Engineer Course

AI Engineer Course blends the expertise of an Artificial Intelligence Expert and a Certified Data Scientist, equipping participants with advanced skills in machine learning, deep learning, and data science. Recognized by IABAC, the course offers hands-on experience, industry projects, and certifications. DataMites, a Platinum Partner of IABAC, offers a 9-month Artificial Intelligence Engineer Course with Internship and live projects.

In the Certified Data Engineer Training, participants can expect comprehensive job-oriented training. The course includes 12 courses in artificial intelligence engineer, covering;

  • Data Science Foundation
  • Python Foundation 
  • Statistics Essentials
  • Machine Learning Associate
  • Machine Learning Expert
  • Advanced Data Science
  • Database: SQL and MongoDB
  • Version Control with Git
  • Big Data Foundation
  • Certified BI Analyst
  • Artificial Intelligence Foundations
  • Artificial Intelligence Expert

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Let's delve deeply into the artificial intelligence engineer program;

  • Data Science Foundation - An introductory course tailored for beginners, covering the fundamental aspects of data science. It provides a comprehensive overview of essential concepts, techniques, and tools in the field.
  • What will be covered - Data science course introduction,Data Science Demo, Analytics Classification,  Data Science Essentials, Data and Related Fields, Machine Learning Introduction, and Data Science Industry Applications, Data Science Roles and Workflow.
  • Prerequisites - Python Foundation
  • Learning Hours - 60 Hours
  • Python Foundation -  A foundational Python programming course designed for novices, offering a thorough introduction to the language's basics.
  • What will be covered - Python Basics, Python Data Structures, Python Functions, Python Control Statements, Python Numpy Package, and Python Panda Package.
  • Prerequisites - None
  • Learning Hours - 40 Hours
  • Statistics Essentials  - Delve into crucial statistical concepts and techniques essential for data analysis and decision-making across various domains.
  • What will be covered - Overview of Statistics, Exploratory Data Analysis, Hypothesis Testing, Harnessing Data, and Correlation and Regression.
  • Prerequisites - None
  • Learning Hours - 20 Hours
  • Machine Learning Associate -  Gain a solid understanding of machine learning's fundamental principles and algorithms, along with insights into its applications and methodologies.
  • What will be covered - Machine Learning Introduction, Visualization with Python, ML Algo: Linear Regression, Python Numpy and Pandas Package, ML Algo: Logistics Regression, ML Algo: KNN, Principle Component Analysis (PCA), and ML Algo: K Means Clustering.
  • Prerequisites - Python Foundation, Data Science Foundation
  • Learning Hours - 40 Hours
  • Machine Learning Expert - An advanced course delving into intricate machine learning algorithms, model optimization, and practical implementation strategies for solving real-world problems.
  • What will be covered - Machine Learning Introduction, ML Algo: Logistics Regression, ML Algo: KNN, ML Algo: K Means Clustering, ML Algo: Linear Regression, ML Algo: Decision Tree, ML Algo: Naive Bayes, Gradient Boosting, XGBoost, ML Algo:  Support Vector Machine (SVM), Principal Component Analysis (PCA), Artificial Neural Network (ANN), and Advanced ML Concepts.
  • Prerequisites - Python Foundation, Data Science Foundation
  • Learning Hours - 80 Hours
  • Advanced Data Science - Explore advanced data science techniques, including deep learning, natural language processing, and predictive analytics.
  • What will be covered - Time Series Forecasting - ARIMA, Sentiment Analysis, Regular Expressions with Python, Feature Engineering,Advanced Data Analysis with MS Excel, AWS Cloud for Data Science, ML Model Deployment with Flask, and Azure for Data Science.
  • Prerequisites - Python Foundation, Machine Learning Foundation, Data Science Foundation
  • Learning Hours - 40 Hours
  • Database: SQL and MongoDB- Acquire proficiency in both SQL and NoSQL databases, covering query languages, database design, and management for structured and unstructured data.
  • What will be covered - DataBase Introduction, Data Types and Constraints, SQL Basics, DataBases and Tables (MySQL), SQL Commands and Clauses, SQL Joins, and Document DB/NO-SQL DB.
  • Prerequisites - None
  • Learning Hours - 15 Hours
  • Version Control with Git - Master the fundamentals of version control using Git, facilitating efficient collaboration, code management, and project tracking in software development.
  • What will be covered - GIT Introduction, Commits, Pull, Fetch, and Push, Tagging, Branching, and Merging, GIT Repository and GitHub, Undoing Changes, and GIT with GITHub and Bitbucket.
  • Prerequisites - None
  • Learning Hours - 10 Hours
  • Big Data Foundation- Learn about the principles, technologies, and tools essential for handling and analyzing large volumes of data, including frameworks like Hadoop and Spark.
  • What will be covered - Big Data Introduction, PySpark Foundation, Spark SQL and Hadoop Hive, HDFS and Map Reduce, Machine Learning with Spark ML, and KAFKA and Spark.
  • Prerequisites - Python Foundation
  • Learning Hours - 20 Hours
  • Certified BI Analyst - Focus on business intelligence concepts, data visualization techniques, and analytics tools to derive insights and support decision-making within organizations.
  • What will be covered - Business Intelligence Introduction, BI with Tableau: Introduction, Tableau: Business Insights, Dashboards, Stories, and Pages, Tableau: Connecting to Data Source, and BI with Power BI.
  • Prerequisites - None
  • Learning Hours - 15 Hours
  • Artificial Intelligence Foundations - This course teaches you all the basics of artificial intelligence, like how machines learn and understand language.
  • What Will Be Covered - Artificial Intelligence Overview, Tensorflow Foundation, Computer Vision Introduction, Deep Learning Introduction, Natural Language Processing (Nlp), and Ethical Issues And Concerns.
  • Prerequisites - Python Foundation, Data Science Foundation
  • Learning Hours - 20 Hours
  • Artificial Intelligence Expert - For those who want to become AI pros, this course dives deep into complex AI stuff, so you can solve tough problems and come up with new ideas in artificial intelligence.
  • What Will Be Covered - Neural Networks, Implementing Deep Neural Networks, Deep Computer Vision - CNN , Recurrent Neural Network, Reinforcement Learning, Deep Reinforcement Learning, Natural Language Processing (NLP) Generative Adversarial Network (GAN), And Deploying Dl Models In The Cloud (AWS).
  • Prerequisites - ML Knowledge, Python
  • Learning Hours - 80 Hours

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The scope for AI engineer careers is immense, with projections estimating a $13 trillion boost in global economic activity by 2030. Moreover, the creation of AI-related jobs has been on the rise since 2020, with an expected net gain of two million new positions by 2025. This surge in demand is reflected in lucrative salaries,

The average AI Engineer Salary in United States is $1,51,840 per year. (Glassdoor)

The average salary for AI skills is £58,000 per year in UK. (Payscale)

Average salary for an AI Engineer in India is 11.6 Lakhs per year. (AmbitionBox)

DataMites Institute offers a career-oriented program designed to upskill individuals into proficient AI engineers. With a vigorously updated curriculum and a total learning duration of 400 hours, participants can become certified AI engineers in just 9 months. 

Join DataMites' AI Engineer Program today to kickstart your journey into the dynamic field of artificial intelligence.