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| Item Details | Price | ||
|---|---|---|---|
Basics of Data Science and Machine Learning
| Instructor: Aishwariya Mathpati |
Language: English |
Course Description
The Data Science with Machine Learning course is designed to equip students with the skills and knowledge required to analyze and interpret complex data using advanced statistical and machine-learning techniques. This course covers the data science workflow from data acquisition and pre-processing to model selection, optimization, and deployment.
The course begins with an overview of the fundamental concepts and techniques used in data science, including data types, data structures, and statistical analysis.
Throughout the course, students will work on several hands-on projects that involve solving real-world data science problems. By the end of the course, students will have gained the skills and knowledge required to tackle a wide range of data science challenges and make data-driven decisions using advanced statistical and machine learning techniques.
What you will learn:-
- Statistic
- Linear Reggration
- Logistic Reggration
- SVM
- KNN
- Error Mertix
- K-Means
- And Practical implications of them.ENROLL NOW AND START YOUR JOURNEY !!
Syllabus
What do we offer
Lifetime Access
Get life long access to all the courses with unlimited learning.
Structured learning
Our curriculum is designed by experts to make sure you get the best learning experience.
Community & Networking
Interact and network with like-minded folks from various backgrounds in exclusive chat groups.
Learn with the best
Stuck on something? Discuss it with your peers and the instructors in the inbuilt chat groups.
Practice tests
With the quizzes and live tests practice what you learned, and track your class performance.
Get certified
Flaunt your skills with course certificates. You can showcase the certificates on LinkedIn with a click.
LEARNING RESOURCES
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Learning Path
Week 1 : Data Science Fundamentals & Python Basics
What is data science and its lifecycle Introduction to Jupyter Notebook or Google Colab pandas
Week 2 : Data Pre-processing & Exploratory Data Analysis
Handling missing values Encoding categorical variables Scaling and normalization
Week 3 : Statistical Analysis & Intro to Machine Learning
Mean, median, variance, standard deviation Correlation and covariance Train/test split
Week 4 : Supervised Learning: Regression & Classification
Linear Regression Logistic Regression K-Nearest Neighbors (KNN)
Week 5 : Unsupervised Learning & Model Tuning
K-Means clustering Elbow method for selecting clusters Dimensionality reduction basics (e.g., PCA overview)
Week 6 : Final Project: End-to-End ML Workflow
Problem Selection: Choose a dataset & business question Data Prep: Clean and preprocess the data
OUR OTHER COURSES
Applied IoT, Cloud Computing
Data Science & Machine Learning With Python
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