Foundations Of Artificial Intelligence

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Published 8/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 8.77 GB | Duration: 18h 21m
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.
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