Course description

  • Enhanced Efficiency and Productivity
  • Data-Driven Decision Making
  • Improved Customer Experience
  • Innovation and Problem Solving
  • Healthcare Advancements
  • Enhanced Security
  • Accessibility and Inclusivity
  • Economic Growth

  • Artificial Intelligence (AI) is the simulation of human intelligence processes by machines, especially computer systems.
  • These processes include learning (acquiring information and rules for using the information), reasoning (using rules to reach approximate or definite conclusions), and self-correction.
  • AI has become an integral part of various industries, transforming how we live, work, and interact with technology.

  • Artificial Intelligence (AI) is the simulation of human intelligence processes by machines, especially computer systems.
  • These processes include learning (acquiring information and rules for using the information), reasoning (using rules to reach approximate or definite conclusions), and self-correction.
  • AI has become an integral part of various industries, transforming how we live, work, and interact with technology

  • Overview of Artificial Intelligence
  • History and Evolution of AI
  • AI Applications in Various Industries
  • Ethical Considerations in AI
  • Setting Up the Development Environment (Python, Jupyter Notebooks, Anaconda)

  • Introduction to Python Programming
  • Data Structures and Algorithms in Python
  • Essential Python Libraries for AI (NumPy, Pandas, Matplotlib, Seaborn)
  • Object-Oriented Programming in Python

  • Linear Algebra
    1. Vectors and Matrices
    2. Matrix Operations
    3. Eigenvalues and Eigenvectors
  • Calculus
    1. Derivatives and Integrals
    2. Partial Derivatives
    3. Chain Rule and Gradient Descent
  • Probability and Statistics
    1. Probability Theory
    2. Statistical Distributions
    3. Hypothesis Testing
    4. Bayesian Statistics

  • Supervised Learning
    1. Regression Algorithms (Linear Regression, Ridge, Lasso)
    2. Classification Algorithms (Logistic Regression, Decision Trees, Random Forests, SVM, KNN)
    3. Evaluation Metrics (MSE, RMSE, R², Accuracy, Precision, Recall, F1 Score, ROC-AUC)
  • Unsupervised Learning
    1. Clustering Algorithms (K-Means, Hierarchical Clustering, DBSCAN)
    2. Dimensionality Reduction (PCA, LDA, t-SNE)
  • Model Selection and Evaluation
    1. Cross-Validation Techniques
    2. Hyperparameter Tuning (Grid Search, Random Search)

  • Neural Networks Basics
    1. Introduction to Neural Networks
    2. Activation Functions
    3. Loss Functions and Optimizers
  • Deep Learning Frameworks
    1. TensorFlow Basics
    2. Keras Basics
  • Convolutional Neural Networks (CNNs)
    1. Understanding CNN Architecture
    2. Building and Training CNNs
    3. Applications of CNNs (Image Classification, Object Detection)
  • Recurrent Neural Networks (RNNs)
    1. Understanding RNN Architecture
    2. LSTM and GRU Networks
    3. Applications of RNNs (Time Series Prediction, Natural Language Processing)

  • Text Processing and Feature Extraction
    1. Tokenization, Lemmatization, and Stemming
    2. Bag-of-Words and TF-IDF
  • Text Classification
    1. Sentiment Analysis
    2. Spam Detection
  • Advanced NLP Techniques
    1. Word Embeddings (Word2Vec, GloVe)
    2. Transformers and BERT
    3. Sequence-to-Sequence Models

  • Introduction to Reinforcement Learning
  • Key Concepts: Agents, States, Actions, Rewards
  • Q-Learning and Deep Q-Networks (DQNs)
  • Applications of Reinforcement Learning (Game Playing, Robotics)

  • Image Processing Basics
    1. Image Preprocessing and Augmentation
    2. OpenCV Basics
  • Advanced Computer Vision Techniques
    1. Object Detection (YOLO, SSD)
    2. Image Segmentation (U-Net, Mask R-CNN)
    3. Generative Adversarial Networks (GANs)

  • AI Ethics and Fairness
    1. Understanding Bias in AI Models
    2. Ensuring Fairness and Transparency
    3. Legal and Ethical Implications
  • AI Deployment and Production
    1. Model Deployment Strategies
    2. Using Flask and FastAPI for Deployment
    3. Monitoring and Maintaining AI Models

Tool 1

TensorFlow

Tool 2

PyTorch

Tool 3

Keras

Tool 4

Scikit-learn

Tool 5

Apache Spark MLlib

Skill level

Expiry period

Lifetime
Annual Salary
Source: Glassdoor
Hiring Companies
American Express Honeywell Accenture Wipro Wipro
Source: Indeed
Annual Salary
Source: Glassdoor
Hiring Companies
American Express Honeywell Accenture Wipro Wipro
Source: Indeed
Annual Salary
Source: Glassdoor
Hiring Companies
American Express Honeywell Accenture Wipro Wipro
Source: Indeed
Annual Salary
Source: Glassdoor
Hiring Companies
American Express Honeywell Accenture Wipro Wipro
Source: Indeed

Why Stellaraa

Comprehensive Course
Expert Instruction
Live Interaction
Internship
Capstone Project
LMS Access
Corporate Certificate
One on One Interaction
Career Support

Projects

Image Classification Using CNNs

AI-Powered Chatbot

AI-Based Face Recognition System

AI for Medical Diagnosis

AI-Powered Virtual Personal Assistant

AI Model for Text Summarization

AI-Based Smart Surveillance System

AI-Driven Music Composition System

CERTIFICATIONS

Certificate 1
Certificate 2
Certificate 3
5999

Essential

  • 6+ Hrs of Live Sessions
  • Industrial Projects
  • Recorded videos
  • Certifications
  • Mentor Support
  • One On One Doubt Clearing Sessions
  • Placement Guidance
  • Interview Assistance
Select Plan
14999

Elite

  • 24+ Hrs of Live Sessions
  • Industrial Projects
  • Recorded videos
  • Certifications
  • Mentor Support
  • One On One Doubt Clearing Sessions
  • Placement Guidance
  • Interview Assistance
Select Plan

Discover Top Categories

Machine Learning
Deep Learning
Natural Language Processing (NLP)
Computer Vision
Robotics
Expert Systems
Reinforcement Learning
Speech Recognition
AI in Healthcare
AI Ethics and Policy

Frequently asked question

  • AI refers to the simulation of human intelligence in machines, enabling them to perform tasks, learn from experience, and make decisions without explicit programming. There are different levels of AI:

  • Narrow AI (Weak AI): Focuses on specific tasks like playing chess or recommending products. This is the most common type of AI we encounter today.
  • General AI (Strong AI): Hypothetical AI with human-like intelligence capable of performing any intellectual task. This level of AI doesn't currently exist.
  • Superintelligence: AI surpassing human capabilities in all aspects, still entirely theoretical.

  • AI is already integrated into many aspects of our daily lives, such as:
  • Smartphones: Facial recognition, voice assistants, and personalized recommendations.
  • Social Media: Content filtering, targeted advertising, and automated chatbots.
  • Transportation: Self-driving cars, traffic prediction, and route optimization.
  • Healthcare: Medical diagnosis, drug discovery, and personalized treatment plans.
  • Entertainment: Movie recommendations, music streaming suggestions, and video game AI opponents.

  • AI offers numerous benefits, including:

  • Increased efficiency and productivity: AI automates tasks, freeing humans for more complex work.
  • Improved decision-making: AI can analyze vast amounts of data to identify patterns and make informed decisions.
  • Enhanced innovation: AI can assist in scientific research, drug discovery, and new product development.
  • Personalized experiences: AI tailors experiences to individual needs and preferences.
  • Improved problem-solving: AI tackles complex challenges in fields like healthcare and climate change.

  • While AI offers significant potential, there are also challenges to consider:

  • Job displacement: Automation by AI could lead to job losses in some sectors.
  • Ethical considerations: Bias in AI algorithms can lead to unfair or discriminatory outcomes.
  • Explainability and transparency: Understanding how AI systems reach decisions can be difficult.
  • Security risks: AI systems could be vulnerable to hacking or misuse.
  • Regulation and governance: Developing ethical frameworks for AI development and deployment is crucial.

  • AI is a rapidly evolving field with the potential to significantly impact our lives. The future of AI likely involves:

  • Increased adoption of AI across various industries.
  • Development of more sophisticated AI algorithms.
  • Focus on ethical considerations and responsible AI development.
  • Greater collaboration between humans and AI.

Recognized By

Credential Platform Partners

WIPRO

Contact us

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Email

support@stellaraa.com,
collabs@stellaraa.com

Get in touch

+91 93648 79763
+91 93648 79767

Our address

59/1,4thmain, Dattathreya Nagar, Hoskerehalli, Banashankari Iii Stage, Bangalore, Bangalore South, Karnataka, India, 560085

Office hours

11:00 AM - 8:00 PM

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