Advance your career with Artificial Intelligence programs in collaboration with IITs and partnered corporations. Access exclusive educational sessions & placement assistance. Gain comprehensive knowledge in Python, Numpy, Deep Learning, Supervised Learning, Regression Techniques, and Naive Bayes Classification. This course jumpstarts your AI journey with self-paced videos, hands-on projects, and guided sessions for mastery.
What Will I Learn ?
Introduction to Artificial Intelligence
✓ Definition and History of AI
✓ Types of AI (Narrow AI, General AI, Superintelligent AI)
✓ Key Applications of AI
✓ AI vs Machine Learning vs Data Science
✓ Ethical Considerations in AI
Data Science in Artificial Intelligence
✓ Role of Data Science in AI Development
✓ Data Collection, Cleaning, and Analysis
✓ Data Mining Techniques
✓ Predictive Modeling and Decision Making
✓ AI-driven Data Insights
Hierarchy in Industry
✓ AI Career Roles (Data Scientist, AI Engineer, ML Engineer, etc.)
✓ AI in Various Industries (Healthcare, Finance, Manufacturing)
✓ Organizational Structure in AI and Data Science Teams
✓ AI in Startups vs Large Enterprises
Supervised and Unsupervised Learning
✓ Overview of Supervised Learning (Classification, Regression)
✓ Overview of Unsupervised Learning (Clustering, Dimensionality Reduction)
✓ Key Algorithms and Use Cases
✓ Differences between Supervised and Unsupervised Learning
Machine Learning in Artificial Intelligence
✓ Overview of Machine Learning
✓ Types of Machine Learning (Supervised, Unsupervised, Reinforcement)
✓ Key ML Algorithms and Models
✓ AI and Machine Learning Integration
Deep Learning in Artificial Intelligence
✓ Introduction to Deep Learning
✓ Neural Networks (Artificial, Convolutional, Recurrent)
✓ Applications of Deep Learning (Computer Vision, Natural Language Processing)
✓ How Deep Learning Differs from Traditional ML
Machine Learning vs Deep Learning
✓ Key Differences in Approach and Use Cases
✓ Data Requirements and Computational Power
✓ Algorithm Complexity
✓ Examples of ML vs DL in Real-world Applications
Data Preprocessing
✓ Data Cleaning and Handling Missing Data
✓ Encoding Categorical Variables
✓ Feature Engineering and Selection
✓ Splitting Data into Training and Test Sets
Categorical Variables in Data
✓ Types of Categorical Variables (Nominal, Ordinal)
✓ One-Hot Encoding
✓ Label Encoding
✓ Handling Categorical Data in ML Models
Feature Scaling
✓ Importance of Feature Scaling
✓ Techniques (Normalization, Standardization)
✓ Impact on Machine Learning Algorithms
✓ Use Cases and Best Practices
Machine Learning in Detail
✓ Introduction to Key ML Algorithms
✓ Model Training and Evaluation
✓ Overfitting and Underfitting
✓ Hyperparameter Tuning
Multiple Linear Regression
✓ Concept of Linear Regression
✓ Assumptions of Linear Regression
✓ Multivariate Regression Model
✓ Interpretation of Coefficients
Logistic Regression
✓ Introduction to Logistic Regression
✓ Use in Binary Classification
✓ Sigmoid Function and Thresholds
✓ Evaluating Logistic Regression Models
Support Vector Machine (SVM)
✓ Concept of Support Vectors
✓ Hyperplane and Margin
✓ Linear vs Non-linear SVM
✓ Applications of SVM
Clustering
✓ Concept of Clustering and Use Cases
✓ K-Means Clustering
✓ Hierarchical Clustering
✓ Evaluating Clustering Models
Advance Internship Projects
1. Breast Cancer Prediction
✓ Dataset Overview
✓ Feature Engineering
✓ Model Selection (Logistic Regression, SVM)
✓ Model Evaluation Metrics (Accuracy, Precision, Recall)
2. Spam News Detection
✓ Natural Language Processing (NLP) in AI
✓ Text Preprocessing (Tokenization, Lemmatization)
✓ Classification Algorithms (Naive Bayes, Random Forest)
✓ Model Evaluation
3. Handwritten Digit Recognition
✓ Introduction to MNIST Dataset
✓ Image Preprocessing
✓ Neural Networks for Image Recognition
✓ Model Accuracy and Performance Tuning
4. Automatic License Plate Detection
✓ Object Detection Techniques
✓ Image Processing (OpenCV)
✓ CNNs for License Plate Recognition
✓ Real-time Application and Challenges
5. Covid-19 Detection using Neural Networks
✓ Dataset and Features (CT Scans, X-rays)
✓ Deep Learning Model (CNNs)
✓ Preprocessing Medical Images
✓ Accuracy, Sensitivity, and Specificity
Careers opportunity
✓ Machine Learning Engineer
✓ Data Scientist
✓ Business Intelligence Developer
✓ Research Scientist
✓ Big Data Engineer
✓ Big Data Architect
