Requirements
- Basic understanding on Cyber Security
Features
- Cyber Security Zero to Hero with Realtime Exposure
Target audiences
- Switching Domains, Job Seekers, Freshers, Cyber Security Engineers, Employees, Technical Managers, Technical Leads, Team Leads
MFH Artificial Intelligence Realtime Program :
1. Business Client projects (Live Sessions)
2. Beginners to Advanced Labs covering all Topics
3. Access to Learning Management System.
4. Interview Preparation (Tips from Architects, Interview Questions, Mock Interviews)
Duration : 3 Months
WA to enquire : https://wa.me/917671801206
Join WA Grp : https://chat.whatsapp.com/IsdDmAkrgAMAKftWhv4Hso
What is Artificial Intelligence Program ?
Through this program you will get opportunity to work on Business Client Real-time / Live Projects.
You will also get trained through Cyber Security Training & Labs (Optional).
Labs will you practice every topic starting from Beginner to Advanced.
Other than Client Projects, What is Course Content for Labs & Theory ?
Whatsapp for course content.
How will you get Hands-on ?
MFH will share all the client projects with Real-time Program members.
You can join all or few client projects of your choice.
You can showcase these client projects in your profile.
Artificial Intelligence (AI) Curriculum
1. Foundations of AI
History and Evolution of AI
Definitions and Philosophical Underpinnings
Ethics in AI
2. Mathematics for AI
Linear Algebra
Probability and Statistics
Calculus
Optimization Techniques
3. Programming and Software
Python Programming
Data Structures and Algorithms
Software Engineering Principles
Version Control (e.g., Git)
4.Machine Learning
Supervised Learning
Linear Regression
Logistic Regression
Decision Trees
Support Vector Machines (SVM)
Neural Networks
Unsupervised Learning
Clustering (K-means, Hierarchical)
Dimensionality Reduction (PCA, t-SNE)
Reinforcement Learning
Markov Decision Processes (MDPs)
Q-Learning
Deep Reinforcement Learning
5. Deep Learning
Neural Network Architectures
Convolutional Neural Networks (CNNs)
Recurrent Neural Networks (RNNs) and LSTMs
Generative Adversarial Networks (GANs)
Transformers
Training Deep Networks
Backpropagation
Gradient Descent
Hyperparameter Tuning
Regularization Techniques (Dropout, Batch Normalization)
6. Natural Language Processing (NLP)
Text Preprocessing
Word Embeddings (Word2Vec, GloVe, BERT)
Sequence Models
Machine Translation
Sentiment Analysis
7. Computer Vision
Image Preprocessing
Object Detection
Image Classification
Image Segmentation
8. AI Frameworks and Tools
TensorFlow
PyTorch
Keras
Scikit-Learn
9. Data Science and Big Data
Data Wrangling and Preprocessing
Exploratory Data Analysis
Data Visualization
Big Data Technologies (Hadoop, Spark)
10.Specialized Topics
Robotics
AI in Healthcare
AI in Finance
AI in Autonomous Systems
11. AI Ethics and Policy
Bias and Fairness in AI
Privacy Issues
AI and the Law
Societal Impacts of AI
12. Practical Applications and Projects
Real-world Case Studies
AI Competitions (e.g., Kaggle)
Capstone Projects
By covering these topics, you’ll gain a comprehensive understanding of AI and be equipped to work on a wide range of AI problems and applications.
