Data Science with ML & AI(GenAI /LLM)

Course Objectives:

Python Basics: Develop a solid foundation in Python programming for data science applications.
Data Analysis: Learn to process, clean, visualize, and analyze datasets using Python libraries.
Machine Learning: Understand and implement machine learning algorithms for predictive analysis.
Deep Learning: Explore neural networks, image processing, and natural language processing (NLP).
Generative AI with LLM: Gain insights into Large Language Models (LLMs) and implement Gen AI projects.
Project-Based Learning: Work on real-world projects focusing on prediction, classification, deep learning, and generative AI.

Course Content

Module 1: Introduction to Python for Data Science

Python Basics: Syntax, Data Types, Control Structures, and Functions.
Python Libraries: NumPy, Pandas, and Matplotlib.
Data Wrangling: Data Cleaning, Transformation, and Preprocessing
Data Visualization: Creating Charts and Graphs with Matplotlib and Seaborn.
Hands-on Project: Analyze Sales Data and Generate Insights.

Module 2: Data Analysis and Exploratory Data Analysis (EDA)

Module 3: Machine Learning

Module 4: Deep Learning

Module 5: Generative AI with LLMs

Module 6: Final Projects

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Price:
₹5,500.00 ~~₹8,000.00~~
Instructor:admin
Lessons:29
Students:0
Level:All Levels
Certifications:No

Data Science with ML & AI(GenAI /LLM)

Course Objectives:

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Course Content

Module 1: Introduction to Python for Data Science

Python Basics: Syntax, Data Types, Control Structures, and Functions.

Python Libraries: NumPy, Pandas, and Matplotlib.

Data Wrangling: Data Cleaning, Transformation, and Preprocessing

Data Visualization: Creating Charts and Graphs with Matplotlib and Seaborn.

Hands-on Project: Analyze Sales Data and Generate Insights.

Module 2: Data Analysis and Exploratory Data Analysis (EDA)

EDA Techniques: Summary Statistics and Data Distributions

Advanced Visualization: Heatmaps, Pairplots, and Boxplots

Handling Missing Data, Outliers, and Categorical Variables.

Case Study: Analyzing Customer Behavior for an E-commerce Dataset.

Module 3: Machine Learning

Introduction to ML: Supervised and Unsupervised Learning.

Regression Models: Linear Regression, Decision Trees, Random Forest.

Classification Models: Logistic Regression, SVM, KNN.

Clustering: K-Means, Hierarchical Clustering.

Model Evaluation: Accuracy, Precision, Recall, and F1 Score.

Hands-on Project: Predicting House Prices Using Machine Learning

Module 4: Deep Learning

Basics of Neural Networks: Perceptrons, Activation Functions.

Building Deep Neural Networks with TensorFlow/Keras

Image Processing: Convolutional Neural Networks (CNNs).

Natural Language Processing (NLP): Sentiment Analysis and Text Classification.

Hands-on Project: Image Classification Using CNN.

Module 5: Generative AI with LLMs

Introduction to Generative AI and Large Language Models (LLMs).

Pre-trained Models: GPT, BERT, and their Applications.

Fine-tuning LLMs for Specific Tasks.

Implementing Chatbots and Text Generators.

Hands-on Project: Building a Customer Support Chatbot Using GPT.

Module 6: Final Projects

Project 1: Develop a Machine Learning Model for Customer Segmentation.

Project 2: Create a Deep Learning Model for Object Detection

Project 3: Design a Generative AI Model for Automatic Text Summarization.

Project 4: Build and Deploy a Predictive Analytics Dashboard Using Python and Flask.

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