Artificial Intelligence Course & Certification
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Earn certification from EduCADD THINKWorks, India’s most trusted educational institution with over 25+ years of excellence, in collaboration with Mindforge and the National Skill Development Corporation (NSDC). Pursue a high-paying career in Artificial Intelligence with Mindforge Infotech’s new Artificial Intelligence Course and gain a competitive edge in the job market
The Artificial Intelligence (AI) Course provides a comprehensive introduction to the principles and techniques behind intelligent systems. Covering a wide range of topics, the course includes machine learning, neural networks, natural language processing, computer vision, and robotics. Participants will explore how AI algorithms mimic human intelligence to solve complex problems, from automating tasks to making predictions. Using tools like Python, TensorFlow, and Keras, students will gain hands-on experience in building AI models and deploying them in real-world applications. By the end of the course, participants will have a solid foundation in AI, enabling them to create intelligent systems and drive innovation.
In addition to technical skills, the course emphasizes the ethical considerations and societal impacts of AI, including data privacy, fairness, and the implications of automation. Students will also learn best practices for model evaluation, optimization, and deployment in industries like healthcare, finance, robotics, and autonomous systems. This course prepares individuals for in-demand careers such as AI Engineer, Data Scientist, Machine Learning Engineer, and Research Scientist, offering opportunities to contribute to cutting-edge advancements in technology and transform industries with AI-driven solutions.
Artificial Intelligence Curriculum
- Overview of AI: Definition, History, and Applications
- Types of AI: Narrow AI, General AI, and Superintelligent AI
- AI vs Machine Learning vs Deep Learning
- Real-World Applications of AI: Healthcare, Autonomous Vehicles, Finance, Robotics
- Linear Algebra: Vectors, Matrices, and Tensors
- Probability and Statistics: Distributions, Bayes Theorem
- Calculus for AI: Derivatives, Gradient Descent
- Optimization Techniques in AI
- Introduction to Python Programming
- Key Libraries for AI: NumPy, Pandas, Matplotlib
- Data Handling and Preprocessing
- Exploratory Data Analysis (EDA) for AI Projects
- Supervised Learning: Regression, Classification (Linear Regression, Logistic Regression)
- Unsupervised Learning: Clustering (K-Means, Hierarchical Clustering), Dimensionality Reduction (PCA)
- Reinforcement Learning: Markov Decision Process, Q-Learning
- Model Evaluation: Confusion Matrix, Precision, Recall, F1 Score, ROC-AUC
- Hands-On Project: Building a Predictive Model
- Introduction to Neural Networks: Perceptron, Activation Functions
- Forward and Backpropagation in Neural Networks
- Convolutional Neural Networks (CNNs) for Image Processing
- Recurrent Neural Networks (RNNs) and LSTMs for Time Series and Sequence Data
- Deep Learning Frameworks: TensorFlow and Keras
- Hands-On Projects: Image Classification, Sentiment Analysis
- Introduction to NLP: Text Preprocessing, Tokenization, Lemmatization
- Bag of Words (BoW), TF-IDF, and Word Embeddings (Word2Vec, GloVe)
- Language Models: RNNs and Transformer Models (BERT, GPT)
- Text Classification and Sentiment Analysis
- Hands-On Project: Building a Chatbot or Spam Detection System
- Introduction to Computer Vision
- Image Processing Techniques: Edge Detection, Filters, Histogram Equalization
- Object Detection and Recognition: YOLO, Faster R-CNN
- Image Segmentation and Generative Models (GANs)
- Hands-On Projects: Face Recognition System, Object Detection
- Introduction to Reinforcement Learning
- Concepts: States, Actions, Rewards, Policy
- Q-Learning and Deep Q-Networks (DQN)
- Applications in Gaming, Robotics, and Autonomous Systems
- Hands-On Project: Building a Simple Game AI
- Ethical Implications of AI: Bias, Fairness, and Transparency
- AI and Data Privacy: Handling Personal Data Responsibly
- Accountability in AI Decision-Making
- Regulation and Guidelines for Responsible AI
- Case Studies: Ethics in Autonomous Vehicles, Facial Recognition
- AI in Healthcare: Disease Detection, Personalized Treatment
- AI in Finance: Fraud Detection, Algorithmic Trading
- AI in Autonomous Systems: Robotics, Self-Driving Cars
- AI in Marketing: Customer Behavior Analysis, Recommendation Engines
- AI in Smart Cities: IoT, Predictive Maintenance
- Hands-On Projects: AI Application in Any Selected Industry
- Model Exporting and Deployment Techniques
- Integrating AI Models into Web Applications using Flask/Django
- Model Deployment on Cloud Platforms: AWS, Google Cloud, Microsoft Azure
- API Integration for AI Services
- Hands-On Project: Deploying a Full AI Solution
- Select a real-world problem to solve using AI
- Apply the entire AI pipeline: Data collection, preprocessing, model building, evaluation, and deployment
- Example Capstone Projects:
- AI-Powered Personal Assistant
- Real-Time Object Detection for Surveillance
- Predictive Analytics in Healthcare or Finance
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Career Opportunities After Artificial Intelligence Training
AI Engineers focus on creating intelligent systems and applications that mimic human learning and reasoning. They work on developing models for artificial intelligence (AI), including natural language processing (NLP), computer vision, and speech recognition. AI Engineers are instrumental in advancing technologies in sectors like robotics, autonomous vehicles, and smart assistants.
₹ 2L – ₹ 18.4LPer AnnumHiring Companies
As a Machine Learning Engineer, you’ll be responsible for designing and implementing machine learning models that can learn from data and make predictions or decisions. This role involves working with algorithms, big data, and deployment pipelines. Machine Learning Engineers are highly sought after in industries like technology, finance, healthcare, and e-commerce.
₹ 3L – ₹ 12LPer AnnumHiring Companies
AI Research Scientists focus on advancing the field of artificial intelligence by developing new algorithms, models, and techniques. They work on cutting-edge AI research, often pushing the boundaries of machine learning, deep learning, and natural language processing. Research Scientists are employed in both academic institutions and top tech companies like Google, Facebook, and Microsoft.
₹ 12.2L – ₹ 50LPer AnnumHiring Companies
AI Product Managers oversee the development and deployment of AI-powered products and solutions. They bridge the gap between technical teams and business stakeholders, ensuring AI initiatives align with business objectives. This role is crucial in guiding companies through the successful integration of AI into their product offerings, particularly in sectors like tech, finance, and e-commerce.
₹ 12.2L – ₹ 50LPer AnnumHiring Companies
Automation Engineers leverage AI to develop systems that can operate autonomously without human intervention. They design AI-driven automation processes that streamline operations, reduce costs, and improve efficiency. Industries like manufacturing, logistics, and IT heavily rely on automation, creating a strong demand for AI Automation Engineers.
₹ 12.2L – ₹ 50LPer AnnumHiring Companies
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