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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.

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Overview

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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

What is Data Science ?

4 Lessons

Get familiar with the core concepts and distinctions of data science and its lifecycle.

Data Science vs. Data Analysis vs. Data Engineering

Descriptive and Predictive Analytics

Data Science Life Cycle

Structured vs. Semi-Structured vs. Unstructured Data

Applications of Data Science

2 Lessons

Get started with data science in healthcare, recommender systems, and image analysis applications.

Applications in Healthcare and Recommender Systems

Image Analysis

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.

Probability and Statistics

13 Lessons

Grasp the fundamentals of probability, statistics, distributions, skewness, sampling, and hypothesis testing.

Machine Learning Part-1

21 Lessons

Deepen your knowledge of machine learning basics, types, regression techniques, feature engineering, and model evaluation measures.

Machine Learning Part-2

15 Lessons

Follow the process of exploring classification algorithms and techniques to evaluate model performance.

Machine Learning Part-3

7 Lessons

Piece together the parts of unsupervised learning, clustering techniques, and semi-supervised learning applications.

Deep Learning

8 Lessons

Sharpen your skills in deep learning, neural networks, CNNs, RNNs, and LSTM networks.

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:

Jamshaid Sohail

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