How to become AI-ML Engineer?
Machine learning (ML) and artificial intelligence (AI) are among the hottest and most exciting career fields today, as advances in technologies such as neural networks, cognitive computing, self-driving cars, and speech recognition turn science fiction into reality. AI-ML engineers combine programming skills with math and computer science knowledge to build computer systems that emulate human thought processes, including learning from experience to recognize patterns. If you’re considering becoming an AI-ML engineer, you’ve come to the right place! Most successful ML engineers have at least a basic understanding of linear algebra, probability theory, and statistics. Understanding these foundational mathematical topics will help you better understand how ML models work under the hood. A strong understanding of programming will also set you up for success as an ML engineer — many companies develop their own libraries and frameworks for implementing machine learning algorithms in production.
What job profiles are safe today?
According to futurists, it means that many jobs that humans currently hold will be replaced by machines in the future, with one study projecting that AI could potentially wipe out 47% of US jobs in just 15 years’ time. But which jobs are safe from the threat of AI? Join our demo session to know more.
Who should attend this Program?
Designed for students/ working professionals, fresh graduates, self-starters, and career transformers with essential work experience.
- Marketing & Sales Professionals
- Software & IT Professionals
- Introduction to AI, Applications
- Machine Learning, Types
- Deep Learning, ANN, Batch Learning
- Web Scrapping, Searching the Tree
- Python basics, Anaconda Setup
- NumPy, Scipy, Pandas, sklearn
- Additional Topic: Operating System
- Digital Image Processing Fundamentals
- Computer Vision concepts
- Introduction to OpenCv
- Image Classification
- Object Detection
- Pose Estimation
- Object Tracking
- Visual Saliency Estimation
- Additional Topic: Virtualization
- Natural Language Processing introduction
- Sentiment Analysis
- Machine Translation
- Document Search
- Parts of Speech Tagging and HMM
- Word Embeddings with NN
- Additional Topic: Networking
- Deep Neural Networks
- CNNs, RNNs, GANs
- Keras, Tensorflow, Pytorch
- YOLO, ResNets, GoogleNet
- Additional Topic: Cloud