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Full Stack Data Science with AI Training

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Data Science and AI Full Stack

Full Stack DataScience & AI ₹ 35,000/-
Course Duration 120 hours classes
Other 40 hours fee LMS access
10 hours Soft Skills
Training Mode Classroom/Online

Real Time Expert

Full Stack DataScience & AI Trainer with 8+ Years of Experience.

Tek Ikons Full Stack Data Science with AI course is designed to equip participants with the skills and knowledge required to excel in the rapidly evolving field of data science and artificial intelligence (AI). The course covers a wide range of topics, including data analysis, machine learning, deep learning, natural language processing, and more.

 Participants will gain hands-on experience with various tools and technologies commonly used in the industry.

This course provides a deep dive into the fundamentals of data science and AI, with a focus on building a strong foundation in both theory and practical applications. Participants will learn how to collect, clean, analyze, and interpret data to extract valuable insights. They will also explore advanced machine learning algorithms and techniques for predictive modeling and pattern recognition. Additionally, the course covers the basics of neural networks, deep learning architectures, and techniques for training and fine-tuning models. Practical exercises and projects are integrated throughout the course to reinforce learning and allow participants to apply their skills in real-world scenarios.

Objectives of

Full Stack Data Science & AI Training

  • Understand the fundamental concepts and principles of data science and AI.
  • Gain proficiency in data collection, cleaning, and preprocessing techniques.
  • Learn various machine learning algorithms and their applications.
  • Explore advanced topics such as deep learning, natural language processing, and computer vision.
  • Develop practical skills through hands-on exercises and projects.
  • Prepare for a career in data science, AI, or related fields.
Who can learn

Full Stack Data Science AI Course

This course is suitable for:

  • Students and professionals seeking to enter or advance their careers in the field of data science and AI.
  • Anyone interested in learning about data analysis, machine learning, and artificial intelligence.
  • Individuals with a background in computer science, mathematics, statistics, or a related field.
  • Professionals working in roles such as data analysts, data scientists, machine learning engineers, and AI developers.
Course Curriculum Of

Full Stack Data Science with Generative AI

  • Basic Programming Skills:
    • Python fundamentals
    • Data structures and algorithms
  • Mathematics and Statistics:
    • Linear algebra
    • Calculus
    • Probability
    • Statistics
  • Introduction to Data Science:
    • Overview of data science
    • Data science lifecycle
  • Data Manipulation and Analysis:
    • Data cleaning
    • Exploratory data analysis (EDA)
    • Data visualization
  • Machine Learning Fundamentals:
    • Supervised learning
    • Unsupervised learning
    • Model evaluation and selection
  • Advanced Machine Learning:
    • Deep learning fundamentals
    • Neural networks
    • Convolutional neural networks (CNNs)
    • Recurrent neural networks (RNNs)
    • Transfer learning
  • Big Data Processing:
    • Hadoop ecosystem
    • Apache Spark
  • Natural Language Processing (NLP):
    • Text preprocessing
    • Text classification
    • Sentiment analysis
    • Named entity recognition (NER)
    • Word embeddings
  • Computer Vision:
    • Image preprocessing
    • Object detection
    • Image classification
    • Image segmentation
  • Time Series Analysis:
    • Time series forecasting
    • Seasonality and trend analysis
    • ARIMA models
    • LSTM networks for time series
  • Reinforcement Learning:
    • Markov decision processes
    • Q-learning
    • Deep Q-Networks (DQN)
    • Policy gradients
  • Database Management Systems:
    • SQL and NoSQL databases
    • Database design
  • Web Development for Data Science:
    • Flask or Django framework
    • API development
    • Front-end development (HTML, CSS, JavaScript)
  • Deployment and Scalability:
    • Docker containers
    • Kubernetes
    • Cloud platforms (AWS, Azure, Google Cloud)
  • Version Control and Collaboration:
    • Git and GitHub
    • Collaboration tools
  • Real-world project integrating various aspects of data science, AI, and full-stack development.

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