About AI and Data Science Course
AI is the simulation of human intelligence in machines that are programmed to think, learn, and make decisions like humans. It uses algorithms and data to perform tasks such as natural language processing, image recognition, and decision-making. AI powers applications like chatbots, autonomous vehicles, recommendation systems, and virtual assistants. Data Science is the field of analyzing and interpreting large datasets to extract insights, solve problems, and make data-driven decisions. It combines mathematics, statistics, and programming. Data scientists use tools like Python, R, and SQL, along with machine learning techniques, to uncover patterns and trends in data.
Course Fee
₹34999
Available Seats
10
Schedule
6.00 pm - 8.30 pm
Industry Mentorship
Network with industry experts and be mentored by them
Project Portfolio
Build job-ready profile with dynamic portfolio
Interview Opportunities
Get interviews for desired roles in the top companies
Career Growth
Get opportunities to elevate and fast track your career
Certification
Attain industry renowned certificates for internship and course completion
Job Assistance Program
Month Duration
Delivery Mode
Average Salary
Skills you'll Learn
Tools you'll Learn
Course Curriculum
Introduction to Data Science and AI
- Definitions and Real-World Applications
- Mathematics for AI and Data Science
- Linear Algebra: Vectors, Matrices, Eigenvalues, Eigenvectors.
Data Analysis and Visualization
- Data Cleaning: Handling missing data, duplicates, and anomalies
- Exploratory Data Analysis (EDA): Identifying trends, patterns, and anomalies.
- Tools: Matplotlib, Seaborn, and Plotly.
- Creating Dashboards: Using Tableau or Power BI.
Machine Learning
- Algorithms: Linear Regression, Logistic Regression, Decision Trees, Random Forests.
- Clustering: K-means, DBSCAN, and Hierarchical Clustering.
- Dimensionality Reduction: PCA (Principal Component Analysis).
- Metrics: Accuracy, Precision, Recall, F1-Score, ROC-AUC.
Deep Learning
- Architecture: Input, Hidden, and Output Layers.
- Training Techniques: Backpropagation, Gradient Descent.
- Hands-on: Building Neural Networks for classification and regression.
- Convolutional Neural Networks (CNNs): For image processing.
Natural Language Processing (NLP)
- Tokenization, Stemming, and Lemmatization.
- Transformers (BERT, GPT).
- Sentiment Analysis, Text Summarization, and Chatbot Development.
Big Data and Cloud Computing
- Hadoop and Spark Basics.
- Managing large datasets.
- AWS, Google Cloud, or Azure for data storage and AI model deployment.
Projects
Chatbot for Customer Support
Develop an AI-powered chatbot that can handle customer queries, provide recommendations, and simulate human-like conversations.
Predicting House Prices
Build a machine learning model to predict house prices based on features like location, size, and amenities.
Salary Comparison Data
