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An Introductory Guide to Data Science and Machine Learning - AI-Powered Course
Beginner
6h
An Introductory Guide to Data Science and Machine Learning
Gain insights into data science fundamentals, explore machine learning and big data, and delve into real-time projects. Discover systematic approaches to data acquisition, wrangling, and solving diverse problems.
Join 2.8M developers at
Overview
Content
Reviews
There is a lot of dispersed, and somewhat conflicting information on the internet when it comes to data science, making it tough to know where to start. Don't worry.
This course will get you familiar with the state of data science and the related fields such as machine learning and big data. You will be going through the fundamental concepts and libraries which are essential to solve any problem in this field.
You will work on real-time projects from Kaggle while also honing your mathematical skills which will be used extensively in most problems you face. You will also be taken through a systematic approach to learning about data acquisition to data wrangling and everything in between. This is your all-in-one guide to becoming a confident data scientist.
There is a lot of dispersed, and somewhat conflicting information on the internet when it comes to data science, making it tough...Show More
Content
93 Lessons
1.
What is Data Science ?
4 Lessons
Get familiar with the core concepts and distinctions of data science and its lifecycle.
2.
Applications of Data Science
2 Lessons
Get started with data science in healthcare, recommender systems, and image analysis applications.
3.
Overview of Libraries
13 Lessons
Break apart essential data science libraries: Beautiful Soup for web scraping, Numpy for array operations, Pandas for data analysis, and Spacy for NLP.
4.
Probability and Statistics
13 Lessons
Grasp the fundamentals of probability, statistics, distributions, skewness, sampling, and hypothesis testing.
5.
Machine Learning Part-1
21 Lessons
Deepen your knowledge of machine learning basics, types, regression techniques, feature engineering, and model evaluation measures.
6.
Machine Learning Part-2
15 Lessons
Follow the process of exploring classification algorithms and techniques to evaluate model performance.
7.
Machine Learning Part-3
7 Lessons
Piece together the parts of unsupervised learning, clustering techniques, and semi-supervised learning applications.
8.
Deep Learning
8 Lessons
Sharpen your skills in deep learning, neural networks, CNNs, RNNs, and LSTM networks.
9.
Machine Learning Tools and Libraries
3 Lessons
Get started with essential tools and libraries for efficient machine learning.
10.
Big Data Tools and Technologies
4 Lessons
Examine Big Data characteristics, Hadoop, Map Reduce, Apache Spark, and their applications.
11.
Where to go next ?
3 Lessons
Apply your skills to Kaggle, courses from Educative, and essential references.
Certificate of Completion
Showcase your accomplishment by sharing your certificate of completion.
Course Author:
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