- The Applied AI course offers 150+ hours of content covering Python, Machine Learning, and Deep Learning with no prerequisites
- Top recruiters include Microsoft, Infosys, TCS, Intel, Flipkart and Samsung
- The course can be completed in 5-6 months at 12-15 hours per week.
Applied Artificial Intelligence (AI) with Deep Learning is part of the IBM Advanced Data Science Certification. It gives you access to valuable insights into Deep Learning applications used by professionals in natural language processing, computer vision, time series analysis, and many other disciplines.
You are going to learn about the basics of Linear Algebra and Neural Networks. The course implements the most common Deep Learning frameworks including Keras, TensorFlow, PyTorch, DeepLearning4J and Apache SystemML. Keras and TensorFlow are the biggest part of this course.
You study Anomaly Detection, Time Series Forecasting, Image Recognition, and Natural Language Processing by building models using Keras on real-life examples from IoT (Internet of Things), Financial Market Information, Literature and Image Databases. Finally, you learn to scale artificial intelligence using Kubernetes, Apache Spark, and GPUs.
Artificial Intelligence Courses Guide 2026Read →What Is the Applied AI Course?
The Applied AI Course covers all concepts from foundational to advanced levels, with practical implementation at every stage. This approach allows each course participant to master Machine Learning, Deep Learning and Data Science skills regardless of their educational background. No prior experience in programming or AI is required.
No pre-requisites
The Applied AI course has no mandatory prerequisites. Candidates from any background including software development, business analysis, data science or engineering can enrol and complete the course at their own pace.
Content
Applied AI / Machine Learning path has 150+ hours of industry-focused and simplified content with no pre-requisites, covering Python, Mathematics, Data Analysis, Machine Learning, and Deep Learning.
More than 70 hours of live sessions discuss topics based on student feedback and industry needs to better prepare students for real-world problem-solving. The length of the course is one year.
Applied Nature
The course covers the practical dimensions of artificial intelligence: 30 practical assessments to improve learning with simple, focused and actionable feedback. 13+ end-to-end case studies based on real-world business problems across different industries give students a taste of real-time experience.
Addressing Queries
Our group responds to most purchaser requests in much less than 24 hours. Student questions are addressed through our Innovative Question Resolution Program using Audio / Video Responses.
Portfolio Building
Mentors assist candidates to create a portfolio of real-world projects they have worked on, customised to their current profile and shared with businesses. Candidates are also encouraged to write posts on specific platforms about their solutions to real-world problems, which boosts their chances of being recruited.
Machine Learning Course: Skills and Career ScopeRead →Who can do Applied AI?
This course is designed for practitioners who intend to advance to the role of AI specialist. Applied AI is ideal for Software Developers, Project Managers, Business Analysts, Data Scientists, and Computer Engineers who want to build a strong foundation in Artificial Intelligence and Machine Learning.
Which Are the Top Recruiters for Applied AI?
Artificial Intelligence is widely used to solve complex real-world problems. From self-driving vehicles to virtual assistants, AI is projected to grow significantly in everyday life. Most enterprise leaders report that AI will fundamentally transform their business operations. Top recruiters actively seek professionals trained in Applied AI and Machine Learning.
- Microsoft
- Flipkart
- Qualcomm
- Infosys
- Intel
- Zoho
- HCL
- Visa
- JIO
- Samsung
- GE
- Tata Consultancy Services
What Are the Applied AI Course Outcomes?
Applied AI graduates learn the fundamentals of intelligent systems and apply search techniques, machine learning algorithms, and data aggregation methods to extract valuable insights. They can find efficient solutions to complex real-world constraints, reason over large datasets, and determine the best logical course of action to achieve their goal.