Complete Data Science , Machine Learning and AI Guide

00:00:00 - Introduction 00:08:11 - Excel Basics Knowledge 00:26:21 - How to convert data in uppercase and lowercase? 00:37:21 - How to Group/ungroup data? 00:40:15 - How to delete blank rows? 00:46:41 - How to convert numbers in words? 00:51:31 - How to combine data in one column? 00:54:08 - How to add date in excel? 01:09:52 - How to calculate age? 01:12:33 - How to Compare columns? 01:19:45 - How to change date formats? 01:35:42 - How to calculate Time Difference? 01:38:23 - DAX in Excel 01:44:04 - Checkbox in Excel 01:49:42 - How to insert image in Excel? 01:52:49 - How to insert PDF/PPT in excel? 01:55:45 - How to convert PDF to Excel? 02:08:08 - How to set cell size? 02:12:20 - How to create barcodes in excel? 02:24:14 - How to calculate standard deviation in Excel? 02:30:35 - How to sort by date in excel? 02:37:18 - Slicers and Filters in Excel 02:40:10 - How to add filters in excel? 02:53:49 - Data Validation in Excel 03:12:44 - How to Lock(Protect)cells in excel? 03:17:15 - Excel Print Page Setup 03:26:55 - Conditional Formatting In Excel 03:31:37 - How to remove duplicates in excel? 03:34:28 - SUMIFS Formula in excel 03:42:48 - How to calculate Percentage in Excel 03:49:34 - How to Highlight Duplicates in Excel? 03:53:20 - Charts in Excel 04:03:31 - Progress Tracker in excel 04:06:09 - Excel Gantt Chart 04:09:24 - How to use mail merge in Excel? 04:14:55 - Excel LOOKUP 04:55:15 - Indirect Function In Excel 05:00:00 - Web Query in Excel 05:04:51 - Userforms in Excel 05:16:06 - How to recover unsaved file in excel? 05:21:19 - Project Planning in Excel 05:30:28 - Microsoft Excel Templets 05:34:06 - MIS Reports in Excel 05:45:37 - Time Series Analysis 06:19:53 - Excel Dashboard Designs

00:00:00 - Why tableau? 00:03:25 - Learning Resources 00:04:41 - Installation 00:09:56 - Interface Overview 00:13:08 - Loading Data 00:17:18 - Loading and Viewing excel data 00:24:27 - Data types 00:30:14 - Filtering & Sorting 00:36:12 - Data Visualization 00:47:22 - Scatter plot 00:55:12 - Dynamic Scatter plot 01:00:00 - Flip Charts 01:01:38 - Bar plots 01:10:08 - Geo Maps 01:15:23 - Creating your projects 01:20:25 - Saving the plots & projects 01:26:10 - Area Charts 01:32:11 - Grouping in charts 01:34:36 - Dashboard Development 01:46:07 - Dashboard deployment

00:00:00 - What is Mathematics? 00:20:49 - Branches of Mathematics 00:38:24 - Number Theory 01:04:53 - What did you learn? 01:05:41 - Number Theory in Details 01:12:56 - Application of Number Theory 01:26:11 - Pros. of Number Theory 01:34:57 - Factors and Multiples 01:44:04 - Divisibility Rules 01:58:18 - GCD and LCM 02:05:33 - Modular Arithmetic 02:19:52 - Modulus 02:20:27 - Types of Numbers 02:35:33 - What is Algebra? 02:49:56 - Importance of Algebra 02:52:34 - How to learn Algebra? 02:55:38 - Types of Algebra 03:05:59 - History of Algebra 03:12:32 - Algebra and Data Science 03:23:57 - Pre-algebra Introduction 03:32:13 - Basics of Pre-algebra 03:43:34 - Fractions and Decimals I 03:59:45 - Fractions and Decimals II 04:12:01 - Ratios and Proportions 04:27:20 - Percentages 04:46:52 - Solving Equations 05:02:53 - Units and Geometry 05:12:23 - Data Analysis Basics in Pre-algebra 05:18:09 - Problem Solving in pre-algebra 05:22:00 - Exercises for Pre-algebra 05:24:46 - Elementary Algebra 05:33:49 - Mathematics Playlist an additional resource 05:38:51 - Tasks for Linear Algebra 05:40:58 - Vector 06:02:42 - Matrices in Machine Learning 06:12:25 - Next Tasks assignment 06:15:15 - Linear Algebra Introduction 06:21:33 - Cartesian coordinates 06:31:57 - Unit Vectors 06:39:56 - Scalars and Scaling Vector 06:45:09 - Vector Addition 06:54:44 - Vector Spans and Linear Dependence 07:12:31 - x and y intercepts 07:33:37 - Dot product of vectors 07:38:37 - cross product of vectors 07:48:14 - Vector spaces 07:52:56 - Vector Transformation 08:16:30 - Linear Transformation and matrices 08:40:38 - Matrices 08:47:30 - Shear transformation 08:56:12 - Why do we need transformations? 08:59:22 - Matrix multiplication and composition 09:16:55 - Matrix and Types 09:23:36 - Other types of Matrices 09:33:51 - Addition and Subtraction of Matrices 09:37:12 - Multiplication of Matrices 09:42:40 - Multiplication is important to learn 09:43:43 - Determinant of Matrix 09:53:59 - Inverse of a Matrix 10:17:09 - System of Equations 10:25:45 - Types of linear equations 10:29:47 - How to represent System of Equations 10:48:25 - Matrix form for system of equations 10:59:42 - Assignment Alert 11:01:38 - Solving system of equations 11:07:27 - Graphical Method of solving equations 11:31:21 - Substitution method of solving equations 11:45:24 - Elimination method of solving equations 11:54:46 - Matrix Inversion method of solving equations 12:05:33 - Advance methods of solving equations 12:21:58 - Assignment Alert 12:23:09 - System of Equations and advance methods 12:25:59 - Gaussian Elimination Method I 12:52:05 - Gaussian Elimination Method II 13:00:52 - Gaus Jordan Elimination Method 13:09:09 - LU Decomposition Method 13:20:16 - Singular Value Decomposition 13:59:06 - Gaussian Elimination method and Row Operations 14:04:04 - Eigenvalues and Eigenvectors 14:20:20 - Singular Value Decomposition in Python 14:30:58 - Linear Algebra Notes 14:36:39 - Solving System of Equations in Python 14:45:10 - Solving System of Complex Equations in Python 14:50:12 - Draw a vector in Python 14:54:49 - Linear Transformation on vectors in 2D 15:02:02 - Shear Transformation in Python 15:06:05 - SVD in python 15:15:03 - Learn Python from this course 15:15:41 - Register for Python ka Chilla Latest Course

00:00:00 - Introduction to statistics for Data Science 00:08:03 - What is statistics? 00:14:06 - Content of this course 00:22:15 - Why statistics is important? 00:29:29 - Scales of measurement 00:46:55 - Qualitative vs. Quantitative data 00:56:32 - Discrete, Continuous or Binary Data 01:03:28 - Time series Data 01:06:31 - Spatial Data 01:08:55 - Categorical vs. Ordinal Data 01:13:37 - Multivariate Data 01:16:38 - Structured vs. Unstructured Data 01:26:12 - Boolean Data 01:27:29 - Operationalization and Proxy measurements 01:35:53 - True vs. Error Score 01:46:23 - Types of Errors 01:57:44 - Type-I vs. Type-II errors 02:09:42 - Reliability and Validity 02:25:21 - Triangulation 02:40:52 - Surrogate Endpoints 02:50:19 - Measurement and Data Bias 03:18:17 - How to remove Bias? 03:26:56 - Statistics and Types of Statistics 03:45:14 - Why statistics is important to learn? 03:55:52 - Types of Data Analysis 04:11:34 - Assignment Alert 04:14:40 - Central Tendency 04:25:13 - Mean, types and limitations of means 04:54:36 - Median 05:07:30 - Mode 05:22:47 - Population vs. Sample means 05:29:29 - Variation, spread or dispersion in data 05:50:30 - Data variability and Range 05:59:58 - Interquartile Range (IQR) 06:17:37 - Variance 06:30:17 - Standard Deviation vs. Standard Error 06:54:19 - Normal Distribution and Standard Deviation 07:01:05 - Data Distribution and its types 07:46:29 - Skewness vs. Kurtosis 08:31:59 - Primary vs. Secondary Data 08:45:42 - Data Collection and Sampling 09:02:05 - Best practices for Data Collection 09:12:04 - Sampling Types 09:24:02 - All sampling Techniques 09:34:29 - Hybrid Sampling 09:35:01 - Descriptive Statistics 09:51:01 - Descriptive statistics with t-test 10:09:53 - How to choose right statistical method? 10:33:32 - Exploratory Data Analysis (EDA) 10:38:35 - Dependent vs. Independent Variables 10:48:34 - Inferential Statistics 10:55:51 - Hypothesis and Hypothesis Testing 11:16:53 - Confidence Intervals 11:26:59 - Chi-squared test and Python code 11:40:28 - Shapiro Wilk Test in python 11:48:51 - t-tests in python 12:00:58 - Leven's test for homogeneity 12:05:17 - One-way ANOVA 12:10:13 - ANOVA in Python 12:37:56 - MANOVA in python 12:43:51 - Correlation in python 12:59:31 - Case Study-I (Chi-squared test) 13:18:22 - Case Study-II (t-tests) 13:35:10 - Case Study-II (ANOVA) 13:54:49 - Case Study-IV (Correlation) 14:09:29 - Basic Pillars of EDA 14:13:47 - Free Book as Bonus Resource 14:14:38 - Python ka Chilla 2024-25

00:00:00 - What is Python? 00:03:23 - Installation process 00:16:06 - VScode profile and themes 00:22:36 - File and Folder Management 00:32:12 - Miniconda Environment Guide 00:54:18 - GitBash and Miniconda connect 01:01:53 - Jupyter Notebooks 01:09:31 - Python for Data Science 01:23:52 - Important logins for students 01:37:01 - GitHub student developer pack 01:37:56 - Naming Conventions for variables 01:54:15 - Jupyter notebook vs. py files 02:07:42 - Variables in Python 02:27:19 - Types of Variables 02:51:05 - Type casting 03:14:46 - Dynamic Type casting 03:23:49 - Comments in Python 03:28:47 - gitbash in VScode 03:32:30 - Screencast Mode 03:34:59 - Google your errors 03:41:44 - How to google? 03:51:43 - Alternatives to github co-pilots 03:54:47 - Activate github co-pilot in VScode 03:56:03 - Snipping tool for questions 03:59:53 - When to use variables and data structures? 04:10:42 - Data Structures 04:46:31 - Sequences 05:08:28 - Indexing 05:33:38 - Slicing 05:39:40 - Mutable Elements 05:45:44 - Range Functions 05:48:09 - gitbash as default terminal in vscode 05:49:52 - control flow statements 06:09:58 - Audience examples of control flow statements 06:13:06 - if else or elif statements 06:30:40 - conditional and relational operators 06:52:36 - for and while loops 07:07:50 - break continue and pass statements 07:16:35 - infinite loops 07:26:35 - try except and finally 07:31:51 - summary of control flow statements 07:34:24 - .ipynb to pdf file conversion 07:57:53 - functions 08:24:15 - lambda function 08:46:47 - user input functions 09:11:18 - gitbash in vscode 09:13:01 - nine methods for print functions 09:54:55 - tips to improve communciation 09:56:19 - generators 10:09:53 - libraries in python 10:32:04 - social media appearance 10:38:16 - installing important libraries 10:54:49 - PyPI and pip 10:59:13 - Top 5 libraries 11:07:30 - Seventeen important libraries 11:18:42 - use libraries as professionals 11:35:15 - Loading and saving data in python

00:00:00 - Installation of Python 00:02:49 - Python from Command Line 00:03:31 - Running Python from Python Software 00:04:00 - Talking to Python 00:07:50 - Notepad for Python 00:13:20 - Installing an IDE (VS Code) 00:14:23 - Basic Commands in CMD 00:18:17 - Working in VS Code 00:19:52 - Chapter 1: First Program 00:22:14 - Chapter - 2: Operators 00:33:03 - Chapter - 3: Strings 00:38:48 - Chapter - 4: Comments 00:43:40 - Chapter - 5: Variables 00:57:12 - Chapter - 6: Input Variables 01:07:33 - Chapter - 7: Conditional Logic 01:19:13 - Chapter - 8: Type Conversion 01:29:33 - Chapter - 9: If, Else & Elif 01:40:43 - Chapter - 10: Functions 01:57:16 - Chapter - 11: loops 02:05:10 - Chapter - 12: Import Libraries 02:09:52 - Chapter -13: Trouble Shooting 02:13:59 - Anaconda Installation 02:19:03 - Jupyter Notebook First Start 02:29:54 - First Program in Jupyter 02:36:53 - Chapter 2 in Jupyter 02:39:42 - Chapter 3 in Jupyter 02:41:09 - Chapter 4 in Jupyter 02:45:30 - Indexing 02:55:13 - String Methods 03:01:18 - Finding an Index Number in string 03:04:43 - Basic Data Structures in Python - Tuples 03:16:18 - Basic Data Structures - List 03:26:56 - Dictionaries 03:39:15 - Sets 03:45:15 - Numpy (Numerical Python) 03:53:32 - What is an Array? 04:11:16 - Array Attributes 04:14:04 - How to create an Array? 04:19:40 - Array Creation (Hands-on) 04:36:00 - Array Functions 04:39:39 - 2D Arrays 04:59:52 - NumPy Documentation 05:00:43 - Jupyter Notebook in VS Code 05:23:40 - Pandas Library 05:27:53 - Data Manipulation in PANDAS 05:38:59 - Data Analysis in PANDAS 05:46:51 - Types of Data Analysis 05:57:58 - PANDAS vs MS Excel 06:02:07 - PANDAS Exercise in VS Code 06:47:39 - Import Save and Analyze Data using PANDAS (Kashti Case Study) 07:04:43 - Basics of Statistics for Data Science 07:16:45 - Types of Data 07:27:35 - Measures of Central Tendency (Mean, Median, and Mode) 07:49:36 - Mean, Median and Mode (Hands-on) 08:03:19 - Fundamentals of Visualization 08:06:13 - Chart Suggestions By Dr. Andrew Abela 08:06:55 - Choosing a Statistical Method 08:10:00 - Step-1 Normality Test 08:11:01 - Step-2 Homogeneity Test 08:11:36 - Step-3 Purpose 08:18:30 - Step-3 Data Type 08:21:46 - Statistical Tests 08:35:50 - Non-Parametric Alternatives 08:40:45 - Research Designs (CRD vs RCBD vs LSD) 08:48:37 - EDA (Exploratory Data Analysis) 09:45:21 - Data Wrangling

00:00:00 - Introduction 00:00:48 - Chapter-1 First Program 00:03:23 - Chapter-2 Operators 00:14:09 - Chapter-3 Strings 00:19:57 - Chapter-4 Comments in Python 00:24:48 - Chapter-5 Variables 00:38:20 - Chapter-6 Input Variables 00:48:41 - Chapter-7 Conditional logics 01:00:22 - Chapter-8 Type Conversion 01:10:42 - Chapter-9 if, else & elif 01:21:51 - Chapter-10 Functions 01:38:25 - Chapter-11 Loops 01:46:17 - Chapter-12 Import Libraries 01:51:00 - Chapter-13 Trouble Shooting

00:00:00 - Introduction 00:01:56 - Data Visualization hy kia? 01:01:37 - matplotlib and seaborn library 02:09:05 - plot customization 02:28:33 - plolty crash course 03:21:09 - 3D plots 03:59:27 - Marginal Plots plotly 04:06:62 - Animated plots 04:23:50 - Multiple plots 04:34:51 - Geospatial maps plots in plotly 05:09:15 - Save plotly animation

00:00:00 - What is Data Pre-processing? 00:30:27 - Steps in Data Pre-processing 00:37:49 - What are Outliers? 01:00:13 - Types of Outliers 01:10:01 - How to Identify Outliers? 01:23:25 - Z-score methods for Outliers 01:29:30 - Handling Outliers 01:34:23 - Outliers, Final Words 01:39:04 - Removing Outliers in Dataset Example 01:55:29 - Missing Values k Rolay 02:12:03 - Imputing Missing Values Basic to Advance Methods 03:07:04 - Data Scaling and Normalization 03:28:46 - Feature Scaling 03:27:48 - Tips about Feature Scaling 03:40:40 - Data Scaling and Pre-processing in Python 03:59:20 - Data Transformation in Python 04:04:16 - Data Normalization in Python 04:15:22 - Most Used Scalar Types 04:16:06 - What is Feature Encoding? 04:29:11 - Why feature encoding is needed? 04:41:55 - Feature Encoding in Python

00:00:00 - Introduction 00:05:33 - World Bank Dataset 00:25:45 - All crash courses are here 00:27:28 - FAOSTAT dataset 00:45:36 - EUROSTAT dataset 01:01:09 - yahoo finance dataset 01:13:27 - Crash courses | freemium

00:00:00 - Introduction to the Crash Course 00:02:07 - What is Machine Learning? 00:21:45 - Types of Machine Learning 00:31:45 - Supervised Machine Learning 00:50:05 - Unsupervised Machine Learning 00:58:19 - Semi-supervised Machine Learning 00:59:18 - Reinforcement Learning 01:07:07 - Application of ML 01:13:58 - Data is Important for ML 01:19:04 - Scikit-learn for ML 01:24:59 - Scikit-learn in Python 01:51:02 - Intermediate ML in Python 02:27:32 - Metrics for Classification and Regression 02:57:26 - ML model building and deployment 03:31:54 - What is an algorithm? 03:40:19 - Training and Testing data, Features, Labels 03:44:37 - Overfitting vs. Underfitting 03:54:51 - Data Pre-processing 04:32:42 - Imputing Missing Values Methods in python 05:27:43 - Data Scaling and Normalization Theory 05:49:27 - Feature Scaling vs. Normalization 06:01:20 - What is Feature Encoding? 06:14:25 - Why do we need feature encoding? 06:27:09 - Regression in one go Theory 07:08:37 - Logistic Regression 07:16:45 - Regression vs. Classification Metrics 07:24:57 - Testing Data Matters 07:45:29 - Support Vector Machines (SVMs) Theory 08:09:17 - K-nearest Neighbours (KNNs) Theory 08:31:05 - Euclidean Distance in ML 08:50:05 - Manhattan Distance in ML 08:59:15 - Minkowski Distance in ML 09:11:54 - Hamming Distance in ML 09:16:49 - Algorithms we learned so far 09:24:34 - Decision Tree Algorithms Theory 09:34:07 - Elements of Decision Tree Algorithm 09:46:58 - Entropy, gini impurity and information gain 10:09:07 - Ensemble Algorithms in ML 10:30:28 - Random Forest Algorithm Theory 10:49:22 - Ensemble Algorithms Family 10:57:22 - Boosting in Ensemble Algorithm 11:30:38 - Boosting Algorithm vs. Neural Network 11:44:08 - Part-2 of Machine Learning Crash Course

00:00:00 - Part-1 of this Lecture is here 00:00:15 - Evaluation Metrics in ML 00:04:45 - Regression Metrics in ML 00:24:07 - Classification Metrics in ML 00:55:06 - Complete Previous tasks 00:55:46 - Removing Outliers in Python 01:12:09 - Data Scaling and Preprocessing 01:30:49 - Data Transformation in Python 01:35:45 - Data Normalization in Python 01:46:52 - Pipeline in ML 01:54:09 - Pipeline in Python using scikit-learn 02:05:05 - Feature Encoding in Python 02:17:25 - Intermediate use of sk-learn for ML 02:42:38 - Improving ML model performance 02:57:18 - Polynomial Regression in Python 03:05:30 - Kaggle is important for ML 03:14:39 - Ridge Regression in Python 03:40:13 - Lasso Regression in Python 04:09:50 - Logistic Regression and Classification metrics in Python 04:29:04 - KNN in Python 04:41:54 - SVM in Python 04:52:04 - Decision Trees Algorithm in Python 05:07:32 - Random Forest Algorithm in Python 05:19:12 - CatBoost Algorithm in python 05:31:55 - Naive Bayes Algorithm 05:48:10 - Types of Naive Bayes Algorithm 05:54:27 - Naive Bayes Algorithms in python for ML 05:58:15 - Cross validation methods 06:10:49 - Hyperparameter Tuning in python for ML 06:27:02 - Best model selection 06:45:53 - All ML models so far 06:48:10 - PyCaret for Automatic Machine Learning 07:27:54 - PyCaret for Regression Tasks 07:44:36 - Like Share and Subscribe

00:00:00 - Part 1 00:00:03 - What will you learn? 00:02:29 - What is Deep Learning? 00:06:49 - AI vs ML vs DL 00:20:42 - Small vs Big Data 00:23:11 - What is a Neural Network? 00:44:26 - Types of Neural Networks 00:51:32 - Architecture of Neural Network 00:56:05 - Single Layer vs Multi Layer Neural Network 00:59:20 - Multilayer Perceptron 01:15:43 - Types of Multilayer Perceptron 01:25:32 - Applications of Multilayer Perceptron 01:30:01 - Python Libraries and Installations for DL 01:46:58 - Ten Step guide to create an ANN 01:57:49 - Creating ANN with TensorFlow in Python 01:58:06 - Simple Neural Network in TensorFlow 02:21:14 - Using GPU for DL in TensorFlow 02:24:33 - MLP in TensorFlow with Python 02:37:57 - Call Back Function and Early Stopping 02:45:39 - How many number of Neurons? 02:54:08 - Activation Function 03:27:16 - Linear Activation Function 03:30:48 - Non-linear Activation Functions 03:33:06 - Binary Step Activation Function 03:37:25 - Sigmoid or Logistic Activation Function 03:48:04 - tanH Activation Function 03:52:30 - ReLu Activation Function 04:04:43 - Leaky ReLu Activation Function 04:09:50 - Parametric ReLu Activation Function 04:13:47 - Softmax activation function 04:23:26 - How to choose an Activation Function? 04:38:59 - Computer Vision Basics 05:09:17 - Computer Vision in Python 05:31:21 - Convolutional Neural Network (CNN) Intro 05:45:24 - CNN Advancement 06:20:30 - CNN Coding in Python TF 06:57:22 - CNN Key Concepts 07:24:16 - CNN Image Classification Case Study 08:56:13 - CNN Key Terms 09:09:23 - CNN Project Fasion MNIST 09:41:22 - CNN Project Rice Disease Detection 10:35:56 - Summary 10:36:30 - Crash Course Part2 Coming Soon

00:00:00 - Deep Learning Part-2 00:00:05 - Deep Learning Part-1 is here 00:00:08 - Recurrent Neural Network (RNN) 00:11:08 - Deep Insights to RNN 01:09:31 - RNN in Python with TensorFlow 01:24:01 - Key Term in NLP 02:25:47 - Sentiment Analysis in Python 03:12:16 - LSTM in TensorFlow with Python 03:38:32 - History of ANN, CNN, RNN, LSTM, GRU 04:28:45 - LSTM vs GRU 04:53:24 - Underfitting of a DL Model 05:01:13 - Overfitting of a DL Model 05:07:51 - Good Fit or Robust Model 05:12:27 - How to Improve a Model fitness? 05:21:05 - Reducing Overfitting of a DL Model 05:44:44 - Transfer Learning 06:04:38 - Transfer Learning in Python using TensorFlow 06:21:43 - Generative AI 06:34:50 - Key Terms in Generative AI 07:07:54 - GenAI working in Python 07:15:07 - GenAI is Now 07:30:08 - AI meri job kha gaye 07:35:51 - Transfer Learning for Image Classification 08:20:28 - Python for Transfer Learning (A Case Study) 08:50:54 - Progressive Growing GANs in python 09:41:58 - TensorBoard for Hyperparameter Tuning 10:20:44 - Diffusion Models 10:37:58 - Stable Diffusion Model in Python 10:59:36 - Transformers from Huggingface 11:32:38 - Extro 11:33:12 - Deep Learning Crash Course Part-1

0:00 - Introduction to MySQL 00:40 - What is Data? / What is Database Management System? 04:42 - Types of Data 06:59 - What is MySQL? 10:54 - Table Related Query 13:26 - Creating First Database 20:24 - Creating First Table 29:01 - SQL Datatypes 34:48 - Data Types W.R.T Signs 36:29 - Types of SQL Commands 39:37 - Data Base Related Queries 43:57 - Table Related Queries 50:41 - Keys in SQL 53:49 - Constraints in SQL 1:07:53 - SELECT Command In SQL 1:09:45 - WHERE clause in SQL 1:12:11 - Operators In SQL 1:17:22 - Limit Clause in SQL 1:20:57 - Aggregate Functions 1:22:47 - Group By clause 1:25:02 - Having Clause 1:27:19 - General Order of Commands 1:28:37 - Update commands 1:32:36 - Delete Command in SQL 1:34:08 - Revisiting Foreign Keys 1:37:51 - Cascading for foreign keys in SQL 1:42:28 - ALTER command in SQL 1:48:17 - Truncate in SQL 1:49:51 - Joins in SQL

00:00:00 - Intro and Importance 00:01:52 - Who am I? and Registration in Community 00:10:53 - Scientific Writing and Old Era 00:23:46 - Keyword Practice 00:37:28 - Keyword Science 00:58:04 - KeyWords and Google Scholar 01:12:46 - AI Databases 01:32:09 - Research vs. Review Paper 01:51:03 - Finding the Gap in Research 02:22:27 - AI Tools 100X writing Speed 02:38:51 - ResearchRabbit for Literature Review 02:45:22 - Join our community