Machine Learning(AI) Training

Machine learning is one of the liveliest areas of artificial intelligence. Machine learning algorithms allow computers to learn new things without being programmed. They use statistics as a way to better understand the massive amounts of data that we create every day. These newer algorithms help machines classify images, sounds, and videos. They can answer our questions, discover new drugs, and even write songs.

In this course, trainees can obtain an overview of the definition and types of machine learning: supervised, unsupervised, and reinforcement.

Machine learning is reaching the mainstream. With the new tools available to developers, it’s now possible to implement machine learning features—voice, and image recognition; personalized recommendations; and more

  Course Contents:

• Artificial Intelligence

• What is machine learning?

• Different Data Forms

• Application of Numerical Data

• What kind of problems can this help you solve?

• Main Parts in ML process

• ML Learning Tools & SW

• Python & R Basics

• Machine learning vs. Deep learning vs. Artificial intelligence

• Different Ways a Machine Learns

• Popular Machine Learning Algorithms

• Applying Algorithms

• Data Science

• Demos of machine learning in real life

• Common challenges

• Myths in ML

• Conclusion

ABOUT THE TRAINER:

Mani has over 10+ plus years’ Corporate experience in IT Engineering & Enterprise Business Development coupled with SAP, Cloud Systems & Digital 2.0 Program Management, with proven ability and core strength in transforming employees to be career-ready for Industry 4.0. Mani has delivered training across AI, ML, IOT, SAP, Blockchain & Cyber Security Technologies. Currently in a mission to train & transform 2 million Grads in India by 2025 to enhance digital employability skills & become Digital Career ready.

Mani has served in Fortune 500s like Shell, Halliburton, ABB, Hewlett Packard, Daelim (Korea), & Asia foundations across Education, IT & Engineering industries in Singapore, India, Canada & US