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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.

Beginner

93 Lessons

Certificate of Completion

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Explanations

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Explanations

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Course Overview

Course Content

Apply Your Skills

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

There is a lot of dispersed, and somewhat conflicting information on the internet when it comes to data science, making it tough...Show More

Course Content

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.

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

Probability and Statistics

13 Lessons

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

21 Lessons

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

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

Machine Learning Part-2

15 Lessons

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

Types of Classification Problems

Logistic Regression

Support Vector Machines

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

Machine Learning Part-3

7 Lessons

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

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

Deep Learning

8 Lessons

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

What is Deep Learning?

Neural Networks

Feedforward Neural Networks

Backpropagation Part 1

Backpropagation Part 2

Convolutional Neural Network

Recurrent Neural Network

LSTM Networks

Machine Learning Tools and Libraries

3 Lessons

Get started with essential tools and libraries for efficient machine learning.

Automated Machine Learning

Pandas Profiling and PyCaret

RAPIDS (Using GPU for Fast Computations)

10.

Big Data Tools and Technologies

4 Lessons

Examine Big Data characteristics, Hadoop, Map Reduce, Apache Spark, and their applications.

What is Big Data ?

Hadoop Ecosystem

Map Reduce Framework

Apache Spark and it's Components

11.

Where to go next ?

3 Lessons

Apply your skills to Kaggle, courses from Educative, and essential references.

Starting Career on Kaggle (Tips)

Recommended Courses from Educative

References and Acknowledgements

Course Author

Jamshaid Sohail

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