Foundations Of Artificial Intelligence

Foundations of Artificial Intelligence

What you'll learn

Gain hands-on experience in data analysis and modeling using Python within Jupyter Notebooks.

Learn to manage and query databases using MySQL and its graphical interface, MySQL Workbench.

Develop interactive web applications for data visualization and machine learning model deployment.

Create dynamic and interactive dashboards to visualize complex datasets.

Requirements

Basic Understanding of Programming: Familiarity with any programming language, preferably Python.

Fundamental Knowledge of Databases: Basic understanding of database concepts and SQL.

Proficiency in Excel: Basic skills in using Excel for data manipulation and analysis.

Description

Welcome to the "Hands-on Data Science Projects" course! This comprehensive program is designed to equip you with practical skills and experience in data science through a series of real-world projects. Throughout this course, you will work with a variety of powerful tools and technologies, including Jupyter Notebook, Streamlit, MySQL Workbench, Power BI, and Excel. These tools will help you analyze and visualize data, build predictive models, and create interactive dashboards, giving you a robust and practical understanding of the data science workflow.You will start with an introduction to data analysis techniques and progress through various projects, each designed to provide hands-on experience with different aspects of data science.Tailored for students, aspiring data scientists, and professionals looking to enhance their data science skills, this course provides practical experience in solving real-world problems using industry-standard tools. You will learn to collect, clean, and analyze data, build and evaluate predictive models, and create interactive visualizations and dashboards. By the end of this course, you will be well-prepared to apply your data science skills in professional settings, equipped with a comprehensive portfolio of projects demonstrating your expertise. Embark on your journey with us and unlock boundless opportunities in data science and technology

Overview

Section 1: Introduction to Python IDEs

Lecture 1 Google Colab - Part 1

Lecture 2 Google Colab - Part 2

Lecture 3 Anaconda Installation

Lecture 4 Jupyter notebook install

Section 2: Python and its importance in Modern day

Lecture 5 Understanding Programming

Lecture 6 Python properties and applications

Section 3: Data Types

Lecture 7 Variables and Values

Lecture 8 Data Types-Integer

Lecture 9 Data Types-Float

Lecture 10 Data Types-Boolean

Lecture 11 Data Types- String

Section 4: Operators

Lecture 12 Conditionals

Lecture 13 Arithmetic operators

Lecture 14 Logical operations in conditionals

Lecture 15 Expression Evaluation

Section 5: Simple If, If-Else, Nested If-Else, If-Elif-Else

Lecture 16 If statements

Lecture 17 Else & Elif Statement

Lecture 18 Nested If statement

Section 6: Control structures: Iterative control structures (For and while Loop)

Lecture 19 Loops

Lecture 20 For loop with range

Lecture 21 For loop with variables

Lecture 22 While

Section 7: String indexing, Accessing and strings using For loop

Lecture 23 Indexing & Slicing in Strings

Lecture 24 String access using for loop

Section 8: Break and Continue statements

Lecture 25 Break and continue statements

Section 9: Fuctions and Types of Arguments

Lecture 26 Functions

Lecture 27 Functions without Arguments

Lecture 28 Functions with Arguments

Lecture 29 Functions with multiple arguments

Lecture 30 Functions with multiple keyword arguments

Lecture 31 Scope of a function

Section 10: Recursion

Lecture 32 Recursion Introduction

Lecture 33 Recursion summation function part 1

Lecture 34 Recursion base case

Lecture 35 Recursion summation function part 2

Section 11: Collections: Lists List Functions

Lecture 36 Lists - Introduction

Lecture 37 Creating Lists

Lecture 38 Accessing Lists

Lecture 39 Methods in List - 1

Lecture 40 Methods in List - 2

Section 12: Collections:Dictionary

Lecture 41 Dictionaries - Introduction

Lecture 42 Creating Dictionaries

Lecture 43 Accessing Dictionaries

Lecture 44 Methods in Dictionaries

Section 13: Collections:Tuples and Sets

Lecture 45 Sets - Intro

Lecture 46 Creating Sets

Lecture 47 Methods in Sets - 1

Lecture 48 Methods in Sets - 2

Lecture 49 Tuples - Intro

Lecture 50 Creating Tuples

Lecture 51 Accessing & Methods in Tuples

Section 14: Assignment

Lecture 52 Assignment

Section 15: Quiz

Section 16: Data and Statistics

Lecture 53 What statistics is and what data are?

Lecture 54 Qualitative data (nominal and ordinal)

Lecture 55 Quantitative data (discrete and continuous)

Section 17: Sample & Population

Lecture 56 Sample and Population

Section 18: Sampling Techniques

Lecture 57 Sampling techniques

Section 19: Numerical (continuous and discrete) and categorical

Lecture 58 Data Types

Section 20: Measures of central tendency

Lecture 59 Measures of Central Tendency (Mean, median and mode)

Section 21: Measures of dispersion

Lecture 60 Measures of Dispersion (variance, sd and IQR) and skewness

Section 22: Important terminology

Lecture 61 Bar plot

Lecture 62 Pie chart

Lecture 63 Histograms

Lecture 64 Box whiskers-Plot

Lecture 65 Scatter plots

Section 23: Normal Distribution & Central Limit Theorem

Lecture 66 Normal distribution

Section 24: Correlation

Lecture 67 Correlation

Section 25: Z,test,T test, Anova and chi-squared test

Lecture 68 T distribution and degree of freedom

Lecture 69 One sample T test

Lecture 70 z-test

Lecture 71 Independent sample T test

Lecture 72 Paired T test

Lecture 73 One way Anova

Lecture 74 Two way Anova

Lecture 75 Chi-square Test

Section 26: Statistical Model using Python

Lecture 76 Statistical Model using Python

Section 27: Probability

Lecture 77 Intro to probability

Section 28: Quiz

Section 29: Supervised Learning

Lecture 78 Supervised Learning: Regression

Lecture 79 Supervised Learning : Classification

Section 30: Decision Tree

Lecture 80 What is a Decision Tree

Lecture 81 Decision Tree in Brief

Lecture 82 Terminologies used

Lecture 83 Case study - ML

Section 31: Random Forest

Lecture 84 What is Random Forest?

Lecture 85 Working Philosophy

Lecture 86 Terminologies & Real-life examples

Lecture 87 Case Study - ML

Section 32: KNN

Lecture 88 What is K-nearest-neighbour?

Lecture 89 How does this work?

Lecture 90 Walk through Sci-kit website

Lecture 91 Case study - ML

Section 33: SVM

Lecture 92 Basics of Support Vector Machine

Lecture 93 Why the name

Lecture 94 Kernel, Gamma and C value

Lecture 95 Case Study - ML

Section 34: Neural Networks

Lecture 96 Neural Networks

Section 35: Ensemble Methods

Lecture 97 Ensemble Methods

Section 36: Unsupervised Learning

Lecture 98 What is unsupervised learning?

Section 37: Clustering

Lecture 99 What is k-means & clustering

Lecture 100 Case Study - ML

Section 38: Dimensionality Reduction using PCA

Lecture 101 Understanding PCA

Lecture 102 Case study - ML

Section 39: Association Rules

Lecture 103 What is Market Basket Analysis

Lecture 104 How does it work

Lecture 105 Case study - ML

Section 40: Quiz

Section 41: Introduction to DataBases

Lecture 106 Introduction to SQL

Section 42: Installation

Lecture 107 MySQL Workbench Installation for Windows

Lecture 108 MYSQL Workbench Installation For MAC

Section 43: Database schema

Lecture 109 Create Database

Lecture 110 Insert

Lecture 111 Alter

Lecture 112 Select

Section 44: String Functions

Lecture 113 String Functions

Section 45: Numeric and Temporal functions

Lecture 114 Numeric and Temporal functions

Section 46: SQL Functions

Lecture 115 SQL functions- Order by, Limit

Lecture 116 Like and ILike (wildcards)

Lecture 117 Aggregate functions in SQL

Lecture 118 Group By

Lecture 119 Having

Section 47: SQL Joins

Lecture 120 SQL Joins

Lecture 121 Inner Join

Lecture 122 Full outer join

Lecture 123 Left Outer Join

Lecture 124 Right Outer Join

Section 48: Union

Lecture 125 Union

Section 49: Database normalization

Lecture 126 Database normalization

Lecture 127 Types of normal forms-1

Lecture 128 Types of Normal forms-2

Section 50: Clustered and non clustered index

Lecture 129 Clustered and non clustered index

Section 51: SQL views

Lecture 130 Temporary Tables

Lecture 131 SQL views

Lecture 132 Subqueries

Section 52: Quiz

Aspiring Data Scientists,Data Analysts,Software Developers,Business Analysts,Students,Career Changers,Anyone Interested in Data Science Projects.