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Artificial Intelligence & Machine Learning
12 Weeks Certification Training Course

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* No Prior Coding Experience Required

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KEY FEATURES

Live Classes

12 Weeks

Lab Support

For Practicals

Industry Expert

Learn From Top Industry Expert

Live Projects

Work On Capstone Projects

Overview of this Certification Course

This comprehensive training program is designed to equip participants with the foundational knowledge and practical skills required to understand and implement artificial intelligence (AI) and machine learning (ML) techniques. The course covers a wide range of topics, including the fundamentals of AI and ML, popular algorithms, hands-on coding exercises, and real-world applications. Participants will learn how to leverage AI and ML to solve complex problems, make data-driven decisions, and develop intelligent systems.

Prerequisites of this Course

Course objectives

Who can apply?

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Skills You Learn From this Course

Fundamental Cybersecurity Concepts

Cyber Threat Analysis

Network Security

Operating System Security

Secure Coding Practices

Data Protection and Encryption

Identity and Access Management (IAM)

Cybersecurity Compliance and Ethics

And Many More...

Course Curriculum

Chapter 1 Introduction to Artificial Intelligence and Machine Learning: 6 hours:

  • Overview of AI and ML

       

  • History and evolution of AI

     

  • Applications of AI and ML

Chapter 2 Python for Machine Learning 12 hours:

  • Introduction to Python programming

  • Essential libraries for ML: NumPy, Pandas, Matplotlib

  • Data manipulation and visualization

Chapter 3 Fundamentals of Machine Learning: 12 hours:

  • Supervised, unsupervised, and reinforcement learning

     

  • Model training, testing, and evaluation

     

  • Bias-variance tradeoff

Chapter 4 Regression Analysis: 6 hours:

  • Linear regression

  • Polynomial regression

  • Regularization techniques

Chapter 5 Classification Algorithms 6 hours:

  • Logistic regression

     

  • Decision trees and random forests

     

  • Support vector machines (SVM)

Chapter 6 Clustering Algorithms: 6 hours:

  • K-means clustering

     

  • Hierarchical clustering

     

  • Density-based clustering

Chapter 7 Dimensionality Reduction: 6 hours:

  • Principal Component Analysis (PCA)

     

  • t-Distributed Stochastic Neighbor Embedding (t-SNE)

     

  • Singular Value Decomposition (SVD)

Chapter 8 Neural Networks and Deep Learning 12 hours:

  • Introduction to neural networks

  • Deep learning frameworks: TensorFlow, Keras

  • Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs)

Chapter 9 Natural Language Processing (NLP) 6 hours:

  • Basics of NLP

       

  • Text preprocessing and tokenization

       

  • Sentiment analysis and text classification

Chapter 10 Reinforcement Learning: 6 hours:

  •  Introduction to reinforcement learning

     

  • Markov decision processes

     

  • Q-learning and Deep Q Networks (DQN)

Chapter 11 Real-world Applications of AI and ML: 6 hours:

  • Healthcare

     

  • Finance

     

  • Marketing and recommendation systems

  • Autonomous vehicles

Chapter 12 Ethical Considerations in AI and ML: 6 hours:

  • Bias and fairness

     

  • Privacy and security concerns

     

  • Responsible AI practices

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*Initial one-to-one consultation, Health & Fitness Assasments Bespoke training program planing, Custom Nutrition plan & recipes. Weekly Progress Reviews

*Initial one-to-one consultation, Health & Fitness Assasments Bespoke training program planing, Custom Nutrition plan & recipes. Weekly Progress Reviews

*Initial one-to-one consultation, Health & Fitness Assasments Bespoke training program planing, Custom Nutrition plan & recipes. Weekly Progress Reviews

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