An Introductory Guide to Data Science and Machine Learning
INTERACTIVE COURSE

An Introductory Guide to Data Science and Machine Learning

Beginner

93 Lessons

6h

Certificate of Completion

An Introductory Guide to Data Science and Machine Learning

This course includes:

63 Playgrounds
160 Illustrations

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

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
2

Applications of Data Science

Applications in Healthcare and Recommender SystemsImage Analysis
3

Overview of Libraries

Beautiful Soup (Scraping Data from Simple HTML)Beautiful Soup (Scraping Data from Html Table)ScrapyNumpy BasicsNumpy Array CreationNumpy Array ManipulationSorting Numpy ArraysBasic Statistics on Numpy ArraysBroadcasting in Numpy ArraysPandasSpacy Part 1Spacy Part 2Seaborn
4

Probability and Statistics

ProbabilityStatisticsJoint ProbabilityConditional Probability and Bayes TheoremMeasures of LocationsMeasures of VariabilityProbability Distributions (Binomial and Bernoulli Distributions)Gaussian DistributionPoisson DistributionSkewness and KurtosisSampling MethodsKey Concepts in StatisticsStatistical Hypothesis Testing
5

Machine Learning Part-1

Machine Learning and its TypesDeep Learning and Recommender SystemsWhat is Regression ?Univariate Linear RegressionMultivariate Linear RegressionFeature ScalingLinear Regression in Scikit LearnRegularization (Lasso, Ridge, and ElasticNet Regression)Support Vector RegressionNearest Neighbour RegressionDecision Tree RegressionFeature Engineering and Categorical Variables EncodingNumerical Variables TransformationFeature 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 RegressorsCross ValidationCase Study: House Prices Prediction using Advanced Regression
6

Machine Learning Part-2

15 Lessons

7

Machine Learning Part-3

7 Lessons

8

Deep Learning

8 Lessons

9

Machine Learning Tools and Libraries

3 Lessons

10

Big Data Tools and Technologies

4 Lessons

11

Where to go next ?

3 Lessons

COURSE AUTHOR
Jamshaid Sohail

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