Data Science Course

In this Module you will learn the fundamental mathematical concepts used to analyze and interpret data, where statistics focuses on collecting, organizing, and summarizing data, while probability deals with the likelihood of events occurring, allowing data scientists to make predictions and inferences based on their analysis

• Statistic and Probability

  5:44

• Quantitative Data

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• Basic Terminologies in Statistics

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• Stratified Sampling

  9:02

• Variance

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• Confusion Matrix

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• Probability

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• Quiz

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• Types of Events

  6:53

• Joint Probability

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• Bayes Theorem

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• Infrential Statistics

  10:56

• Hypothesis Testing

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• Quiz

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In this Module will understand different types of learning algorithms like supervised, unsupervised, and reinforcement learning, data cleaning and preprocessing, feature engineering, model selection, training, evaluation, and visualization, all aimed at building predictive models using data to make informed decisions.

• Basics of Maching Learning

  6:29

• What is Machine Learning

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• Machine Learning Process

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• Supervised Learning Algorithm

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• Quiz

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Linear regression is an algorithm that provides a linear relationship between an independent variable and a dependent variable

• Linear Regression

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• What is R-square

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• Quiz

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Logistic regression is a statistical algorithm which analyze the relationship between two data factors

• Logistic Regression

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• Logistic Regression Curve

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• Implement Logistic Regression

  4:31

• Analyzing Data

  6:24

• Data Wrangling

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• Train n Test Data

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• SUV Data analysis

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• Quiz

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Decision tree is a tree-like model that shows how decisions can lead to different outcomes. It can be used in data analysis, machine learning, and operations research.

• Decision Tree

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• Naive Bayes

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• What is Decision Tree?

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• What is Entropy

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• What is Entropy 2

  12:38

• Quiz

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Random forest is a technique used in modeling predictions and behavior analysis and is built on decision trees.

• Random Forest

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• Why Random Forest?

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• What is Random Forest

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• How Random Forest Works

  14:07

• Quiz

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(KNN) algorithm is a non-parametric, supervised learning classifier, which uses proximity to make classifications or predictions about the grouping of an individual data point

• What is KNN

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• How Knn Works

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Naive Bayes is a machine learning algorithm that uses probability to classify data.

• What is Naive Bayes

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• Naive Bayes Workings

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• Types of Naive Bayes

  14:31

(SVM)Support Vector Machine, a machine learning algorithm that classifies data by finding a hyperplane that separates different classes. SVMs are used in classification and regression analysis.

• Quiz

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• SVM

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• Unsupervised Learning

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• K-means Algorithm

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• Fuzzy C- means Clustering

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• Quiz

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• Aporiori Algorithm

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• What is Reinforcement Learning

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• Markov Decision Process

  22:03

• Q-Learning demo

  12:23

• The Bellman Equation

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• The Bellman Equation 2

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• Quiz

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Deep learning is a type of machine learning that uses artificial neural networks to process data. It's a subset of artificial intelligence (AI) that's inspired by the human brain.

• What is Deep Learning

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• Activation Function

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• Demo

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• What is A Computational Graph

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• What is A Computational Graph 2

  14:12

• Multi Layer Perception

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• Quiz

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In this section you'll learn a step by step guide on how to handle and execute a project.

• Customer churn Analysis intro

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• Customer churn Analysis Cleaning Data

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• Handling Imbalannce Data

  8:14

• Data Model 1

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• Data Modeel 2

  7:30

• Data Model 3

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• Deep Learning with Keras Neuro Network

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• Project 2: Introduction

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• Data Exploration

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• Data Exploration 2

  7:29

• Data Encoding

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• Data Model

  8:13

• Data Model 2

  8:48

This section covers the data science interview questions and Answer.

• Interview QA 2

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• Interview QA

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• Interview QA 3

  11:00

• Interview QA 4

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• Interview QA 5

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• Interview QA 6

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