Introduction to Data Science (ML and DL ) | IDS
Created By: Training Head
What you'll learn
- Foundations of Data Science
- Python Programming for Data Science
- Data Acquisition and Cleaning
- Exploratory Data Analysis (EDA)
- Statistical Analysis with Python
- Machine Learning Fundamentals
- Supervised Learning Algorithms
- Unsupervised Learning Algorithms
- Advanced Topics
- Capstone Project
Requirements
The basic requirements for the "Introduction to Data Science (ML & DL)" course include:
- Basic Excel skills: Participants should be familiar with basic Excel functions for data manipulation and analysis.
- SQL knowledge: Understanding basic SQL queries for data extraction, joining, and aggregating data sets is essential.
- Python basics: Participants should have a foundational understanding of Python programming, including variables, data types, loops, and conditional statements.
- No prior data science knowledge required: The course is designed for beginners with no previous experience in data science, making it accessible to anyone eager to learn.
These prerequisites ensure that participants have the necessary skills to engage with the course material effectively and derive maximum benefit from the learning experience.
Course Description
Embark on a transformative journey into the world of data with our "Introduction to Data Science (ML & DL)" course. Designed for beginners, this comprehensive program covers essential topics such as Python programming, data manipulation, exploratory data analysis, and machine learning fundamentals. Participants will learn to acquire, clean, and analyze data, gaining practical skills in Python libraries like NumPy, Pandas, and Matplotlib. Advanced topics include feature engineering, deep learning basics, and model deployment using TensorFlow or PyTorch. The course culminates in a Capstone Project where learners apply their skills to solve a real-world problem, ensuring hands-on experience. With additional resources like recommended readings and industry case studies, graduates will be well-prepared for diverse career opportunities in machine learning engineering, data science, and research. Prerequisites include basic Excel, SQL, and Python skills, making it accessible to anyone eager to explore the exciting field of data science.
Course Content
Module 1: Foundations of Data Science (2 weeks)
1 lectures, 01:00:00 hrs
Module 1
Module 2: Data Manipulation and Cleaning (3 weeks)
1 lectures, 01:00:00 hrs
Module 2
Module 3: Exploratory Data Analysis (2 weeks)
1 lectures, 01:00:00 hrs
Module 3
Module 4: Machine Learning Fundamentals (4 weeks)
1 lectures, 01:00:00 hrs
Module 4
Module 5: Advanced Topics (3 weeks)
1 lectures, 01:00:00 hrs
Module 5
Module 6: Capstone Project (4 weeks)
1 lectures, 01:00:00 hrs
Module 6
Live Projects
1 lectures, 01:00:00 hrs
Module 7
₹75,000.00₹106,000.00
This Course Includes:
Duration : 6 Months
Letures : 72
Offline & Online Mode