AI Beginners

Section 1: Course Description

Master AI engineering with DreamsPlus’ comprehensive training. Learn the latest AI technologies, frameworks, and best practices to design, develop, and deploy intelligent systems. Achieve industry-recognized certification and enhance your career prospects.

Section 2: Key Points/Training Highlights

AI Engineer Associate Program Highlights

• Pre-requisites: Basic programming knowledge
• Eligibility: AI enthusiasts, software engineers, and data scientists
• Certification Body: DreamsPlus
• Validity: 3 years
• Certification Level: Associate
• Exam Format: Project-based, multiple-choice
• Passing Score: 85%
  
  

Section 3: Course Lessons

Month 1: Introduction to AI and Machine Learning

• Week 1: Learn the fundamentals of AI and Machine Learning, including its history, evolution, and various types like Supervised, Unsupervised, and Reinforcement Learning.
• Week 2: Master the necessary mathematical and statistical foundations, including Linear Algebra, Calculus, Probability, and optimization techniques.
• Week 3: Get started with Python programming and essential libraries like NumPy, SciPy, and Pandas. Dive into Machine Learning libraries such as Scikit-learn and TensorFlow.
• Week 4: Explore the basics of Machine Learning, including Supervised and Unsupervised Learning, and learn how to evaluate models effectively.

Month 2: Machine Learning

• Week 1: Delve into Regression and Classification techniques, including Linear Regression, Logistic Regression, Decision Trees, Random Forests, and SVMs.
• Week 2: Understand Clustering and Dimensionality Reduction with K-Means, Hierarchical Clustering, and Principal Component Analysis (PCA).
• Week 3: Learn about Model Selection, Cross-Validation, and Hyperparameter Tuning, with practical exercises.
• Week 4: Explore Ensemble Methods like Bagging and Boosting, and gain insights into Model Deployment and serving.

Month 3: Deep Learning

• Week 1: Introduction to Deep Learning, its history, evolution, and key types like CNNs, RNNs, and LSTMs.
• Week 2: Study Convolutional Neural Networks (CNNs), their architecture, components, and applications with hands-on practice.
• Week 3: Dive into Recurrent Neural Networks (RNNs) and LSTM Networks, focusing on their architecture and practical applications.
• Week 4: Develop skills in TensorFlow and Keras, culminating in a Deep Learning project.

Month 4: Advanced Deep Learning Topics

• Week 1: Master Transfer Learning and Fine-Tuning techniques with practical applications.
• Week 2: Explore Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), learning their architecture and hands-on practice.
• Week 3: Understand Deep Reinforcement Learning and its applications, with practical exercises.
• Week 4: Delve into Deep Learning for Natural Language Processing (NLP), focusing on real-world applications.

Month 5: Specialized AI Topics

• Week 1: Introduction to Computer Vision, including hands-on practice and applications.
• Week 2: Deep dive into Natural Language Processing (NLP) with practical exercises and applications.
• Week 3: Study Robotics and Autonomous Systems, exploring their practical applications.
• Week 4: Learn about Ethics and Fairness in AI, ensuring responsible AI development.

Month 6: Capstone Project

• Weeks 1-4: Apply your skills in a comprehensive capstone project, integrating AI, Machine Learning, and Deep Learning concepts. Present your project to demonstrate your expertise.

Section 4: AI-Based Industry Jobs

AI Career Opportunities

1. AI Engineer
2. Machine Learning Engineer
3. Data Scientist
4. Natural Language Processing Specialist
5. Computer Vision Engineer
6. Robotics Engineer
7. AI Researcher
8. Business Intelligence Developer
9. Predictive Modeling Specialist
10. Automation Specialist