Artificial Intelligence & Machine Learning 12 Weeks Certification Training Course

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Artificial Intelligence & Machine Learning
12 Weeks Certification Training Course

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KEY FEATURES

Live Classes

12 Weeks

Virtual 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

Gain a solid understanding of the principles and concepts underlying AI and ML.
Learn popular ML algorithms and techniques for data analysis, pattern recognition, and predictive modeling.
Develop hands-on experience with coding ML algorithms using Python and popular libraries such as TensorFlow and scikit-learn.
Explore real-world applications of AI and ML across various domains, including healthcare, finance, and marketing.
Acquire practical skills for designing, training, and evaluating ML models.
Understand ethical considerations and best practices in AI and ML implementation.

Who can apply?

Software Developers / Engineers interested in expanding their skill set to include AI and ML.
Data Analysts / Scientists seeking to enhance their understanding of ML algorithms and techniques.
Business Professionalsinterested in leveraging AI and ML for data-driven decision-making.
Students or Researchers looking to explore the field of AI and ML.

Our Hiring Partners

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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