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.

Beginner

93 Lessons

Certificate of Completion

AI-POWERED

Explanations

AI-POWERED

Explanations

This course includes

63 Playgrounds

This course includes

63 Playgrounds

Course Overview

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 whi...Show More

Course Content

What is Data Science ?

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

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

Applications in Healthcare and Recommender Systems Image Analysis

Overview of Libraries

Break apart essential data science libraries: Beautiful Soup for web scraping, Numpy for array operations, Pandas for data analysis, and Spacy for NLP.

Beautiful Soup (Scraping Data from Simple HTML)Beautiful Soup (Scraping Data from Html Table)Scrapy Numpy Basics Numpy Array Creation Numpy Array Manipulation Sorting Numpy Arrays Basic Statistics on Numpy Arrays Broadcasting in Numpy Arrays Pandas Spacy Part 1 Spacy Part 2 Seaborn

Probability and Statistics

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

Probability Statistics Joint Probability Conditional Probability and Bayes Theorem Measures of Locations Measures of Variability Probability Distributions (Binomial and Bernoulli Distributions)Gaussian Distribution Poisson Distribution Skewness and Kurtosis Sampling Methods Key Concepts in Statistics Statistical Hypothesis Testing

Machine Learning Part-1

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.

Course Author

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Anthony Walker

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Emma Bostian 🐞

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Evan Dunbar

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Carlos Matias La Borde

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Anthony Walker

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Emma Bostian 🐞

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