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Machine Learning

Machine Learning and AI Fundamentals

Original price was: INR ₹14,998.00.Current price is: INR ₹7,499.00.

The Machine Learning and AI Fundamentals course offers a comprehensive introduction to the core principles of machine learning and artificial intelligence. Designed for AI professionals, this course covers supervised and unsupervised learning techniques, neural networks, deep learning, and natural language processing. Through a blend of engaging video lectures, interactive coding sessions, and real-world projects, participants will gain hands-on experience and practical skills, preparing them to excel in the AI industry.

Course Structure

Introduction to Machine Learning and AI

  • Overview of Machine Learning and AI: Get an understanding of what machine learning and AI are all about.
  • Historical Context and Evolution: Dive into the origins and growth of these fields.
  • Key Terminologies and Concepts: Familiarize yourself with the essential vocabulary and ideas.

Supervised Learning

  • Linear Regression and Classification: Explore the basics of making predictions and categorizing data.
  • Decision Trees and Random Forests: Learn how these models split data into more understandable chunks.
  • Support Vector Machines (SVM): Understand how SVMs separate data with maximum margin.
  • Model Evaluation and Performance Metrics: Assess the accuracy and effectiveness of your models.

Unsupervised Learning

  • Clustering Algorithms (K-means, Hierarchical): Group data points without predefined labels.
  • Dimensionality Reduction Techniques (PCA, LDA): Simplify data while retaining its essential characteristics.
  • Anomaly Detection: Spot unusual data points that could indicate important trends or errors.

Neural Networks and Deep Learning

  • Introduction to Neural Networks: Discover how artificial neurons mimic the brain’s workings.
  • Deep Learning Fundamentals: Learn the building blocks of deep learning and how they stack up.
  • Convolutional Neural Networks (CNN): Get to grips with CNNs, especially for image processing.
  • Recurrent Neural Networks (RNN): Explore how RNNs handle sequences and time-series data.
  • Transfer Learning: Learn how to apply pre-trained models to new tasks.

Natural Language Processing (NLP)

  • Text Preprocessing and Tokenization: Break down text data into manageable pieces.
  • Sentiment Analysis: Analyze and determine the mood behind a piece of text.
  • Topic Modeling: Uncover the hidden themes in a body of text.
  • Sequence Models and LSTM: Work with Long Short-Term Memory networks to handle sequential data.
  • Transformer Models and BERT: Dive into the latest advancements in NLP with transformers and BERT.

Practical Machine Learning

  • Working with Python and Jupyter Notebooks: Set up your environment and start coding.
  • Using TensorFlow and Keras for Model Building: Build and train models using powerful libraries.
  • Implementing PyTorch for Advanced Deep Learning: Tackle more complex models with PyTorch.
  • Utilizing scikit-learn for Machine Learning Algorithms: Apply various machine learning techniques using scikit-learn.

Participant’s Eligibility

  • Senior undergraduates and graduate students in Computer Science and related fields.
  • Professionals in IT, data science, and software development who are eager to enhance their AI expertise.

Course Outcomes

  • Solid Foundation: Gain a thorough understanding of machine learning and AI basics.
  • Proficiency in Learning Techniques: Become skilled in both supervised and unsupervised learning methods.
  • Hands-On Experience: Get practical experience with neural networks and deep learning models.
  • NLP Applications: Learn to implement real-world NLP applications.
  • Master Key Tools: Gain expertise in TensorFlow, Keras, PyTorch, and scikit-learn.
  • Real-World Projects: Complete projects that showcase your ability to apply what you’ve learned.
  • Certificate of Completion: Earn a certificate recognized by industry leaders, bolstering your professional profile.

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Certification

  • Upon successful completion of the workshop, participants will be awarded a Certificate of Completion, validating their skills and knowledge in advanced AI ethics and regulatory frameworks. This certification can be added to your LinkedIn profile or shared with employers to demonstrate your commitment to ethical AI practices.

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