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AI for Data Scientists Training

Live Online & Classroom Enterprise Training

AI for Data Scientists focuses on applying advanced AI and machine learning techniques to analyze data, build predictive models, and solve complex real-world problems.

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What is AI for Data Scientists Course about?

This course equips data scientists with the tools, techniques, and frameworks needed to apply Artificial Intelligence (AI) and Machine Learning (ML) in real-world scenarios. It focuses on building and deploying AI models, enhancing data science workflows with deep learning, NLP, computer vision, and MLOps best practices. Learners will also explore how to scale AI solutions using cloud platforms and modern infrastructure.

What are the objectives of AI for Data Scientists Course ?

  • Apply advanced AI techniques in real-world data science projects
  • Use deep learning frameworks (TensorFlow, PyTorch) for image, text, and tabular data
  • Build and deploy NLP models (BERT, Transformers)
  • Implement computer vision pipelines (object detection, classification)
  • Manage end-to-end ML workflows using MLOps principles
  • Use pre-trained models and APIs to accelerate AI solution delivery
  • Optimize and scale AI models using cloud platforms (AWS, GCP, Azure)
  • Evaluate AI model fairness, interpretability, and ethical considerations

Who is AI for Data Scientists Course for?

  • Data Scientists
  • Machine Learning Engineers
  • AI/ML Researchers
  • Applied Scientists
  • Developers with data science background
  • Product Analysts working on AI-enabled products

What are the prerequisites for AI for Data Scientists Course?

  • Strong understanding of Python
  • Experience with data analysis and ML basics
  • Familiarity with libraries like Pandas, NumPy, and Scikit-learn
  • Basic knowledge of statistics and linear algebra
  • Experience with Jupyter notebooks and data visualization

Available Training Modes

Live Online Training

4 Days

Self-Paced Training

40 Hours

Course Outline Expand All

Expand All

  • AI vs. Machine Learning vs. Deep Learning
  • AI use cases in various industries
  • The AI lifecycle and where data scientists fit in
  • Overview of neural networks
  • Implementing feedforward, CNNs, RNNs
  • Using TensorFlow/Keras and PyTorch for rapid prototyping
  • Text preprocessing and vectorization
  • Building and fine-tuning transformer models (e.g., BERT)
  • Sentiment analysis, summarization, and entity extraction
  • Image classification, object detection, segmentation
  • Pre-trained CNNs (ResNet, MobileNet, YOLO)
  • Real-time applications and model deployment
  • Ensemble methods (XGBoost, LightGBM)
  • Feature engineering with AI tools
  • Time series forecasting using AI models
  • Model versioning and tracking (MLflow, DVC)
  • Containerization with Docker and deployment via FastAPI
  • Monitoring and retraining in production environments
  • Using AWS SageMaker / GCP Vertex AI / Azure ML Studio
  • AutoML and serverless AI workflows
  • Managing scalability and compute efficiency
  • Bias detection and fairness metrics
  • Explainability with SHAP, LIME
  • Ethical and legal considerations in AI applications

Who is the instructor for this training?

The trainer for this AI for Data Scientists Training has extensive experience in this domain, including years of experience training & mentoring professionals.

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