An Introductory Guide to Data Science and Machine Learning

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 b... See More

How You'll Learn

Hands-on coding environments

You don’t get better at swimming by watching others. Coding is no different. Practice as you learn with live code environments inside your browser.

Faster than videos

Videos are holding you back. The average video tutorial is spoken at 150 words per minute, while you can read at 250. That‘s why our courses are text-based.

No set-up required

Start learning immediately instead of fiddling with SDKs and IDEs. It‘s all on the cloud.

Progress you can show

Built in assessments let you test your skills. Completion certificates let you show them off.

Hands-on coding environments

You don’t get better at swimming by watching others. Coding is no different. Practice as you learn with live code environments inside your browser.

Faster than videos

Videos are holding you back. The average video tutorial is spoken at 150 words per minute, while you can read at 250. That‘s why our courses are text-based.

No set-up required

Start learning immediately instead of fiddling with SDKs and IDEs. It‘s all on the cloud.

Progress you can show

Built in assessments let you test your skills. Completion certificates let you show them off.

Course Contents

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Show All Lessons

1. What is Data Science ?

Data Science vs. Data Analysis vs. Data Engineering

Descriptive and Predictive Analytics

Data Science Life Cycle

Structured vs. Semi-Structured vs. Unstructured Data

The Good Traits of a Data Scientist

2. Applications of Data Science

Applications in Healthcare and Recommender Systems

Image Analysis

3. Overview of Libraries

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

4. Probability and Statistics

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

5. Machine Learning Part-1

Machine Learning and its Types

Deep Learning and Recommender Systems

What is Regression ?

Univariate Linear Regression

Multivariate Linear Regression

Feature Scaling

Linear Regression in Scikit Learn

Regularization (Lasso, Ridge, and ElasticNet Regression)

Support Vector Regression

Nearest Neighbour Regression

Decision Tree Regression

Feature Engineering and Categorical Variables Encoding

Numerical Variables Transformation

Feature Selection (Filter Methods)

Feature Selection (Wrapper Methods)

Feature Selection (Intrinsic Methods)

Model Evaluation Measures (Explained Variance Score, MAE, MSE)

Model Evaluation Measures (Median Absolute Error, R^2 Score)

Dummy Regressors

Cross Validation

Case Study: House Prices Prediction using Advanced Regression

6. Machine Learning Part-2

Types of Classification Problems

Logistic Regression

Support Vector Machines

Decision Trees

Naive bayes Part-1

Naive bayes Part-2

K-Nearest Neighbors

Ensemble Learning Part 1

Ensemble Learning Part 2

XGBoost, Light GBM and CatBoost

Learning Curves

Model Evaluation Part 1

Model Evaluation Part 2

Dummy Estimators and Handling Imbalance Class Problem

Hyper-Parameter Optimization and Kaggle Competition

7. Machine Learning Part-3

Unsupervised Learning

K-Means Clustering

Hierarchical Clustering

DBSCAN Clustering and Customer Segmentation

Apriori Algorithm and Association Rules

Principal Component Analysis for Dimensionality Reduction

Semi-Supervised Learning Techniques

8. Deep Learning

What is Deep Learning?

Neural Networks

Feedforward Neural Networks

Backpropagation Part 1

Backpropagation Part 2

Convolutional Neural Network

Recurrent Neural Network

LSTM Networks

9. Machine Learning Tools and Libraries

Automated Machine Learning

Pandas Profiling and PyCaret

RAPIDS (Using GPU for Fast Computations)

10. Big Data Tools and Technologies

What is Big Data ?

Hadoop Ecosystem

Map Reduce Framework

Apache Spark and it's Components

11. Where to go next ?

Starting Career on Kaggle (Tips)

Recommended Courses from Educative

References and Acknowledgements