k-Means clustering - Theory - Codanics

How and Why to Register

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Who is a Data Scientist?

Software installation and Important Details

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Software installation and websites for this course

Website introduction and opportunity to learn and earn for Free

Complete Installation of Softwares

Login to made for the course

Questions answered live on 26 09 2023

Day-1

0/5

Welcome and 6 months plan

Laptop buying guide for this course

LLMs’ websites and logins to learn prompt Engineering

VScode themes and extensions for Killer look

Questions and Answers Day-1

Day-2

0/6

Artificial Intelligence Kya Hai?

AI vs Data Science vs ML vs DL

Big Players in AI

Benefits of AI in our Daily life

Registration Deadline for Paid Zoom Classes

Day-3

0/13

Data Science vs. AI

Data Science vs. Data Analytics

AI and Types of AI

Reading and Blogs for the course

Find jobs in AI and Data Science

Kia AI meri job khaye ge?

Ideas for AI Developers

Main Goals of this Course

Why should I learn Python to learn AI?

Software Installation to learn Python for AI and Data Science

Conda Environments for coding in Python

Python IDEs and VScode

Day-4

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Start with Python

Day-5

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Conda Environments (A-Z)

First Line of code in python

Variables in Python

Operator in python

Data Types in Python

Day-6

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Indentations and if conditions in Python

User input program in python

Data structures and indexing in python

Control flow statements in python

Nested Loops in Python

Day-7

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Functions & Lmbda funtions in Python

Modules and Libraries in Python

Types of Errors in Python

Jupyter Notebook and File Handling in Python

Day-8

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Mastering Pandas Library and EDA (Part-1)

Practice on Pandas before next lectures

Day-9

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Quiz No.1: Pandas Basics

Mastering Pandas Library and EDA (Part-2)

Day-10

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Quiz No.2: Exploratory Data Analysis (EDA)

Mastering Pandas Library and EDA (Part-3)

Learn MarkDown Language in 72 minutes

Day-11

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Mastering Pandas Library and EDA (Part-4)

Day-12

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Mastering Pandas Library and EDA (Part-5)

Day-13

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Quiz No.3: Master Pandas and EDA with Interactive Quiz

Automatic EDA with ydata-profiling

Day-14

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EDA on Google Play Store Data (Part-1)

Day-15

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Exploratory Data Analysis q zaroori hy?

Missing Values ka rola kia hy?

Day-16

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Mastering Pandas Library and EDA (Part-8)

Day-17: Complete EDA on Google PlayStore Apps

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Complete EDA on Google Play Store Apps

Day-18: Outliers

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What are outliers?

Types of Outliers

How to identify Outliers? Visual + IQR Methods

Z-score method for outliers detection

How to handle Outliers?

Outliers in one go (Quick Summary)

Day-19: Complete EDA project on Apple APP store apps Dataset

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Use AI for Fast paced EDA

Big Datasets for EDA practice

Complete EDA on Apple APP store Data (A-Z)

Day-20

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Pandas Python Library for EDA Analysis: A Comprehensive Guide

Exploratory Data Analysis (EDA) Kyun Zaroori Hai?

Missing values k Rolay

Day-21: Data Visualization-1

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What is Data Visualization?

Types of Plots/Charts

Making plots/charts in Python

Day-22: Data Science Portfolio

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What is a Data Science Portfolio?

Elements of a great Data Science Portfolio

Steps to Create Data Science Portfolio

What do recruiters look for in Data science jobs?

How this course will build your portfolio?

Polish your LinkedIn Profile for Data Science Jobs

Day-23: Data Visualization-2

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Data Visualization in Python (Blog and Code)

Data Visualization in Python

Day-24: Data Visualization-3

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Plotting with Seaborn in Python | Data Visualization

Plotly and Data Visualization for 2D and 3D Interactive plots

15 important plots to make with plotly in python

Day-25: Quiz Time, Data Visualization-4

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Data Visualization Quiz

Day-26: Data Visualization-5

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Sunburst plot in Python using plotly

Animated Plots in Python

GEO Maps in Python using plotly: Interactive and Animated maps

Saving Plotly Plots in gif files

Python plotly Visualization: A complete Beginner guide

Day-27: Data Scaling/Normalization/standardization and Encoding

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Feature Scaling/Normalization

Feature Encoding

Day-28: NumPy (Part-1)

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Introduction to numpy (a python Library)

Day-29: NumPy (Part-2)

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Mastering Numpy with extended functions of arrays

Day-30: NumPy (Part-3)

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NumPy indexing and slicing

Day-31: NumPy (Part-4)

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Creating NumPy arrays from Data

Day-32a: NumPy (Part-5)

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Matrices in NumPy python library

Day-32b: Data Preprocessing / Data Wrangling

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Mastering the art of Data preprocessing

Day-33: Zero-to-Math for Data Science (Part-1)

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What is Mathematics?

Branches of Mathematics

What did you learn Today?

Day-34: Zero-to-Math for Data Science (Part-2)

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Number Theory in few words

Applications of Number theory

Pros of learning Number Theory and Mathematics

Factors and Multiples

Divisibility Rules in Math

Day-35: Zero-to-Math for Data Science (Part-3)

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GCD and LCM in math

Modular Arithmetic

What is Modulus?

Numbers and their types in number theory

Day-36: Zero-to-Math for Data Science (Part-4)

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What is Algebra?

Why Algebra is important to learn?

How to learn Algebra?

Types of Algebra

History of Algebra

Algebra and Data Science

Day-37: Algebra in Data Science

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Algebra and Data Science (Reading Material)

Day-38: Pre-Algebra (Part-1)

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Pre-Algebra: Basic to Advanced understanding

Basics of pre-algebra

Introduction to pre-algebra

Day-39: Pre-Algebra (Part-2)

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Fractions and Decimals

Ratios and proportions

Percentages and Percent Calculations

Day-40: Pre-Algebra (Part-3)

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Solving equations

Measurements, Units and Geometry

Data Analysis and Algebra

Problem solving in algebra

Exercise Questions for pre-algebra

Day-41: Elementary Algebra

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Elementary Algebra

Important Tasks

Day-42: Linear Algebra (Part-1)

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What is a vector?

What are Matrices?

Day-43: Linear Algebra (Part-2)

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Linear Algebra Definitions

Cartesian coordinates Geometry and Linear Algebra

Scalars and Scaling a vector

Day-44: Linear Algebra (Part-3)

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Vectors’ Addition

Vector span and Linear Dependence of vectors

x and y intercepts in linear algebra

Dot product of vectors

Cross product of two vectors

Day-45: Linear Algebra (Part-4)

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Basis Vectors and Transformation

Linear Transformation and Matrices

Matrices are Linear Transformations

Shear Transformations

Why do we need transformations?

Matrix Multiplications as compositions

Matrices and Types (Part-1/2)

Matrices and Types (Part-2/2)

Day-46: Linear Algebra (Part-5)

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Addition and Subtraction of matrices

Matrices Multiplication

Importance of Matrices Multiplication in Data Science and AI

Determinants of Matrices

Important Tasks

Day-47: Linear Algebra (part-6)

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Inverse of a Matrix

System of Equations

Types of Linear Equations

Writing System of Equations

Matrix form of System of Equations

Assignment Alert

Day-48: Linear Algebra (Part-7)- Solving System of Equations

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Solving system of equations

Graphical Method

Substitution Method

Elimination Method

Matrix inversion Method

Advance Methods

Assignment Alert

Day-49: Linear Algebra (Part-8)- Solving System of Equations

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Advance Methods to solve system of equations

Gaussian Elimination Method

Gauss-Jordan Elimination Method

LU Decomposition Method

Singular Value Decomposition (SVD)

Day-50: Linear Algebra (Part-9)- Solving System of Equations

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Row Operations Permissible vs. non-permissible

Eigenvalues and Eigenvectors

Important Tasks before upcoming lectures

Day-52: Statistics for Data Science (Part-1)

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What is Statistics?

What will we learn?

Why statistics is important?

Scales or Levels of Measurements

Qualitative vs. Quantitative Data

Discrete vs. Continuous vs. Binary Data

What is Time Series Data?

What is spatial Data?

Day-53: Statistics for Data Science (Part-2)

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Categorical vs. Ordinal Categorical Data

Multivariate Data Type

Structured vs. Unstructured Data

Boolean Data Type

Operationalization and Proxy Measurements

Day-54: Statistics for Data Science (Part-3)

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Surrogate Endpoints in statistics

True and Error Score

Types of Errors in Data Collection

Type-I and Type-II errors

Examples from the Audience for Type I and Type II errors

Reliability and validity

Notes of All lectures on ABC of statistics for Data Science

Day-55: Statistics for Data Science (Part-4)

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Measurement and Data Bias

How to remove bias?

Statistics and types of Statistics

Why is statistics important to learn?

Data Analysis and Types of Data Analysis

Next Tasks to Learn: Sample and Population

Day-56: Statistics for Data Science (Part-5)

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Central Tendency: Mean, Median, and Mode

Day-57: Statistics for Data Science (Part-6)

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Central Tendency of the Data Complete Guide

Hormonic Mean understanding of the formula

Limitations of using mean in Data Science and statistics

Median in statistics

Mode in Statistics

Population vs. sample and their means

Variability | Dispersion | Spread of the Data

Day-58: Statistics for Data Science (Part-7)

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Inter quartile Range (IQR)

Standard Deviation and Standard Error

Normal distribution and Standard Deviation

Day-59: Statistics for Data Science (Part-8)

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Data Distributions and types of data distributions

Skewness and Kurtosis

Day-60: Statistics for Data Science (Part-9)

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Primary vs. Secondary | Data

Next Tasks for upcoming lectures on sampling

Day-61: Statistics for Data Science (Part-10)

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Data Collection in the age of big data

Best Practices for Data Collection

Representative and Non-representative sample types

Details of each Sampling Techniques for Data Collection

Hybrid Sampling

Day-62: Statistics for Data Science (Part-11)

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Descriptive Statistics

EDA and Statistics

How to choose the right statistical method?

EDA and the Four Pillars of statistics

Day-63: Statistics for Data Science (Part-12)

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Dependent vs. independent variables

Inferential Statistics

Hypothesis Testing

Confidence Interval in statistics

Day-64: Statistics for Data Science (Part-13)

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Chi-Square Test in Python

Next Statistical tests

Test for Normal Distribution Shapiro-Wilk Test in Python

t-tests in Python

Levene’s test in Python

One-way ANOVA in python

Day-65: Statistics for Data Science (Part-14)

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ANOVA | one-way, 2-way and N-way in Python

MANOVA in python

Correlation in Python

Day-66: Statistics for Data Science (Part-15)

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Chi-squared test case study in python

t-test and case studies in python

One-way and Two-way ANOVA case studies

Correlation | Pearson’s and Spearman’s | Case studies in python

EDA and basic Pillars of Statistics

Book announcement for Statistics ABC of statistics

Day-67: Machine Learning (Part-1)

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What is machine learning?

Types of Machine Learning

Day-68: Machine Learning (Part-2)

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ML Model Building to Deployment | Steps A-Z

What is an algorithm?

Training and Testing Data | Features and Labels | Model

Overfitting vs. Underfitting

Important Python libraries for Machine Learning

Installation steps

Day-69: Machine Learning (Part-3)

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Data Pre-processing before ML Model

Day-70: Machine Learning (Part-4)

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Steps in Data Pre-processing

Dealing with Missing values in Python

Day-71: Machine Learning (Part-5)

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Dealing with Data Inconsistencies / Anomalies

Outliers | Data Pre-processing

IQR Method to remove Outliers

Day-72: Machine Learning (Part-6)

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Data concatenation

Data Preprocessing Steps

Feature Scaling and Normalization

Standard Scaling or standardizing the data

Day-73: Machine Learning (Part-7)

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Min-max, MaxAbs, and Robust Scalars in python

Most used Scaling methods for feature scaling

Normalization and Nonlinear Data Transformation of data

L2 and L1 Normalization in python

Feature Scaling vs. Normalization

Tips about Scaling and normalization

Assignment Alert

Day-74: Machine Learning (Part-8)

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What is feature encoding?

Benefits of feature encoding

Feature Encoding in Python using scikit-learn

Feature-encoding with pandas python

Data Discretization | Data Binning

Data preprocessing steps completed

Scikit-learn base Jupyter notebook

Day-75: Machine Learning (Part-9)

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Linear Regression A-Z

Day-76: Machine Learning (Part-10)

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Logistic Regression (Theory)

Logistic Regression and example in Python A-Z

Evaluation Metrics for Regression & Classification Models

Day-77: Machine Learning (Part-11)

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Train test split matters

Support Vector Machines (SVM) Theory

Support Vector Machines (SVM) in Python

Assignment Alert about SVM

Upload Notebooks on Kaggle

Day-78: Machine Learning (Part-12)

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K-Nearest Neighbors (KNN) Theory

Euclidean Distance

Manhattan Distance

Minkowski Distance

Why Minkowski Distance is Important?

Hamming Distance

K-Nearest Neighbors (KNN) in Python

Day-79: Machine Learning (Part-13) Decision Tree

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Algorithms we have learned so far

What is Decision tree Algorithm?

Elements of Decision Tree

Entropy, Gini impurity and information gain theory

Entropy, Gini impurity and information gain in python

Decision Tree Classifier in Python

Day-80: Machine Learning (Part-14)-Ensemble Algorithms

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Ensemble Algorithms

Random Forest (Theory)

Random Forest in Python for Classification and Regression

Day-81: Machine Learning (Part-15)-Evaluation Metrics

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Evaluation Metrics in Machine Learning

Regression Metrics

Day-82: Machine Learning (Part-16)-Metrics for Classification

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Evaluation Metrics for Classification Algorithms

Day-83: Machine Learning (Part-17)

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Ensemble Algorithms Family

Boosting in Ensemble Methods

Day-84: Machine Learning (Part-18)

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Boosting algorithms and their pros. and cons.

Booting algorithms vs. Neural Networks

xgBoost vs. Random forest vs. Decision Tree | in python

Day-85: Machine Learning (Part-19)

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catBoost algorithm in Python

Day-86: Machine Learning (Part-20)

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Hyperparameter Tuning and Cross Validation

Cross Validation in machine learning (Part-1)

Cross Validation in machine learning (Part-2)

Day-87: Machine Learning (Part-21)

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pipeline in Machine Learning using Scikit-learn

Best Model Selection in Python using Scikit-learn

Day-88: Machine Learning (Part-22)

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Naive Bayes Algorithm (Part-1)

Types of Naive Bayes Algorithm | NB (Part-2)

NAIVE Bayes in Python | Naive Bayes Algorithm (Part-3)

Questions and Answers | Naive Bayes Algorithm (Part-4)

Day-89: Machine Learning (Part-23)

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Best hyperparameter tuned Model Selection

Day-90: Machine Learning (Part-24)

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How to select the best model (Presentation-1) and Feedback

How to select the best model (Presentation-2) and Feedback

Question and Answers session on 90th day of this course

Day-91: Machine Learning (Part-25)

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Encoding and Inverse Transform the data

Day-92: Machine Learning (Part-26)

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Basic Method | Missing Values Imputation (Part-1)

Machine Learning models | Missing Values Imputation (Part-2)

Advance Methods | Missing Values Imputation (Part-3)

Last lecture of 2023 for AI and Data Science

Day-93: Machine Learning (Part-27)

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Supervised Machine Learning and Assumptions

Day-94: Machine Learning (Part-28)

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Mathematics and Assumptions of Linear Regression

Multilinear Regression and Mathematics with Assumptions

Day-95: Machine Learning (Part-29)

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Polynomial Regression Theory and Assumptions

Polynomial Regression in Python with coding

Day-96: Machine Learning (Part-30)

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Ridge Regression in Python L2 Regularization

Lasso Regression | L1 Regularization

Day-97: Machine Learning (Part-31)

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Heart Disease prediction | A complete ML Project (Part-1)

Heart Disease prediction | A complete ML Project (Part-2)

Heart Disease prediction | A complete ML Project (Part-3)

Day-98: Machine Learning (Part-32)

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Heart Disease prediction | A complete ML Project (Part-4)

Heart Disease prediction | A complete ML Project (Part-5)

Day-99: Machine Learning (Part-33)

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Heart Disease prediction | A complete ML Project (Part-6)

Heart Disease prediction | A complete ML Project (Part-7)

Day-100: Un-Supervised Machine Learning (Part-1)

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Un-supervised Machine Learning – Introduction

What is Clustering?

k-Means clustering – Theory

k-Mean clustering in python

Day-101: Un-Supervised Machine Learning (Part-2)

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Hierarchical Clustering | Theory

Hierarchical Clustering | Coding in python

Gaussian Mixture Models (GMMs) | Theory

Gaussian Mixture Models (GMMs) | Metrics

Day-102: Un-Supervised Machine Learning (Part-3)

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Feature Engineering (Part-1)

Feature Engineering (Part-2)

PCA | Principal Component Analysis | Theory

PCA | Principal Component Analysis | Case Study in Python

SVD | Singular Value Decomposition

t-SNE | t-distributed Stochastic Neighbor Embedding

Day-103: Deep Learning (Part-1)

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Introduction to Deep Learning

Machine Learning vs. Deep Learning

Small Data vs. Big Data for Deep Learning

Day-104: Deep Learning (Part-2)

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What is a Neural Network?

Types of Neural Network

Day-105: Deep Learning (Part-3)

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Architecture of Neural Networks

Single Layer vs. Multilayer Neural Network

Day-106: Deep Learning (Part-4)

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Multi Layer Perceptron

Types of Multi Layer Perceptron

Applications of Multi layer perceptron

Day-107: Deep Learning (Part-5)

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Python Libraries for Deep Learning

Ten steps to create a neural Network

Build Neural Networks in Python with TensorFlow

Day-108: Deep Learning (Part-6)

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GPU for Deep Learning with TensorFlow

Build a Multilayer Perceptron in Python with TensorFlow

Day-109: Deep Learning (Part-7)

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Call Back Function for Early Stopping of epochs

How many neurons should be in each layer?

Day-110: Deep Learning (Part-8)-Activation Functions

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Activation functions in Neural Network | Linear vs. Step vs. Sigmoid

tanH, ReLu, Leaky Relu, Paramteric ReLu activation functions

Softmax activation function | Multiclass classification

How to choose an activation function for neural network?

Day-111: Case Studies (Complete Machine/Deep Learning Projects)

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Case Study on UCI Heart Disease Data Project 1

Case Study on UCI Heart Disease Data Project 2

Case Study on UCI Heart Disease Data Project 3

Case Study on UCI Heart Disease Data Project 4

Competitions and Assignments for the course

Day-112: Deep Learning (Part-9)-Complete Project

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Case Study for Kaggle Competition-(Complete Deep Learning Project)

What is random seeding or Random State in ML?

Day-113: Deep Learning (Part-10)-Complete Project

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Case Study on Bank Churn Dataset

Important Assignment for Kaggle Competitions

Announcement about the AI Job Ready Course on Codanics

Day-114: Deep Learning (Part-11)-Convolutional Neural Network (CNN)

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Convolutional Neural Network (CNN) | Part-1

Convolutional Neural Network (CNN) | Part-2

Convolutional Neural Network (CNN) | Part-3

Day-115: Deep Learning (Part-12)-Convolutional Neural Network (CNN)

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Convolutional Neural Network (CNN) | Part-4

Convolutional Neural Network (CNN) | Part-5

Convolutional Neural Network (CNN) | Part-6

Day-116: Deep Learning (Part-13)-Computer Vision

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What is Computer Vision?

Python & Computer vision

Assignment alert

Day-117: Deep Learning (Part-14)-Complete CNN Project

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Paddy Rice Disease Detection Project

Day-118: Deep Learning (Part-15)-Recurrent Neural Network (RNN)

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Introduction to RNN

RNN In depth Understanding

RNN and Python for Project making

Day-119: Deep Learning (Part-16)-Natural Language Processing (NLP)

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Introduction to Natural Language Processing

Sentiment Analysis and NLP

Day-120: Deep Learning (Part-17)-Natural Language Processing (NLP)

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LSTM (Long Short Term Memory) for NLP

History of ANN, CNN, RNN, LSTM, GRU

LSTM vs. GRU types of RNN for Deep learning in NLP

Day-121: Time Series Analysis (Part-1)

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Introduction to Time Series Analaysis

Day-122: Time Series Analysis (Part-2)

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Data Analysis Types for Time Series Data

Types of Data about Time

Time series analysis and plotting in python

Day-123: Time Series Analysis (Part-3)

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Time Series Project understanding and development

Day-124: Time Series Analysis (Part-4)

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Time series Weather forecasting project (Part-1)

Time series Weather forecasting project (Part-2)

Time series Weather forecasting project (Part-2)

Day-125: Time Series Analysis (Part-5)

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prophet model from FB (meta) for time series analysis (1/2)

prophet model from FB (meta) for time series analysis (1/2)

Day-126: Time Series Analysis (Part-6): Web-scrapping

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What is web scrapping?

Web scrapping in python

Stock market data scrapping in python

Day-127: Time Series Analysis (Part-7): ARIMA models for time series

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ARIMA (Time series analysis) in python (Part-1)

ARIMA (Time series analysis) in python (Part-2)

ARIMA (Time series analysis) in python (Part-3)

Day-128: Time Series Analysis (Part-8): Complete Project

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ARIMA, SARIMA, SARIMAX in python complete project

Day-129: git & GitHub Crash Course

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git and GitHub crash course

Day-130: Over-fitting and Under-fitting in a Model

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Underfitting in a Machine/Deep Learning Model

Overfitting in a Machine/Deep Learning Model

Good Fit or Robust Model in Machine/Deep Learning

Day-131: Improving Machine/Deep Learning Model’s Performance

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How to Improve Model’s Fitness?

Project Based Improving Model’s Fitness

Day-132: Transfer Learning and Pre-trained Models (Part-1)

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Introduction to Transfer learning

Transfer Learning in Python using TensorFlow

Assignment Alert for Transfer Learning

Day-133: Transfer Learning and Pre-trained Models (Part-2)

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Use of TensorFlow for Image Classification – A complete Project

Day-134 Transfer Learning and Pre-trained Models (Part-3)

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MobileNetV2 pre-trained Mode: A complete Project

Day-135: Generative AI (Part-1)

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What is Generative AI?

Important Key Terms used in Generative AI

Day-136: Generative AI (Part-2)

0/2

How does Generative AI work?

Generative AI in 2024

Day-137: Generative AI (Part-3)

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Generative AI | Kia meri job kha jaye ge?

Day-138: Generative AI (Part-4)

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Progressive growing Generative Adversarial Networks (GANs)

Assignment Alert

Day-139: Generative AI (Part-5)-Tensorboard

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TensorBoard: a crash course

Day-140: Generative AI (Part-6)-Diffusion Models

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Introduction to Diffusion Models

Stable Diffusion Models in python

Day-141: Generative AI (Part-7)

0/2

SORA: An openAI’s model for AI video generation

Important Logins to create before next lecture

Day-142: Prompt Engineering

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Introduction to prompt Engineering

Types of Prompt Enginnering

Prompt Designing Strategies to write best prompt

Generate Prompts from LLMs

LLMs and prompt engineering live demo

Day-143: Huggingface

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Use pre trained models from Huggingface

Day-144: LangChain and APIs

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Introduction to API (Application Programming Interface)

LangChain workflow for LLMs

Applications of LangChain for LLMs

LangChain + OpenAI API + HuggingFace API

Day-145: Streamlit for webapp development and deployment (Part-1)

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Introduction and installation

My first app in streamlit

streamlit cheat sheet

Day-146: Streamlit for webapp development and deployment (Part-2)

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Streamlit Data Science application

Day-147: Streamlit for webapp development and deployment (Part-3)

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Streamlit webapp without coding but prompting

Day-148: Streamlit for webapp development and deployment (Part-4)

0/2

Creating a word Cloud app on streamlit python

requirements.txt file for streamlit apps

Day-149: Streamlit for webapp development and deployment (Part-5)

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Streamlit app deployment on free servers

Day-150: Streamlit for webapp development and deployment (Part-6)

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Create an app only in 14 lines of codes

Day-151: Streamlit for webapp development and deployment (Part-7)

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Manage Secrets in streamlit deployment with python

Day-152: Streamlit for webapp development and deployment (Part-8)

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ask the pdf and other docx application

Day-153: Streamlit for webapp development and deployment (Part-9)

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Streamlit project read pdfs in a folder

Day-154: Streamlit for webapp development and deployment (Part-10)

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Stock Market Analysis and Prediction app

Day-155: Streamlit for webapp development and deployment (Part-11)

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Dall-e-3 Image generation app in python

Day-156: Streamlit for webapp development and deployment (Part-12)

0/1

Speech to text Streamlit Application development

Day-157: Streamlit for webapp development and deployment (Part-13)

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Text to speech Streamlit application

How to Earn using Data Science and AI skills

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How to earn online using Data Science and AI

Day-158: Flask for web app development (Part-1)

0/2

is FLASK better than streamlit?

Installation and basics of web app development in flask

Day-159: Flask for web app development (Part-2)

0/5

flask Installation and basic application development

Open Conda environments inside vscode

Flask app for basic EDA and plotting

Image Generation app development in flask python

Day-160: Flask for web app development (Part-3)

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End to End machine Learning project in flask | house price prediction

Day-161: Flask for web app development (Part-4)

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End to End machine Learning project in flask | Tip prediction

Day-162: Flask for web app development (Part-5)

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Diabetes prediction app development

Day-163: Flask for web app development (Part-6)

0/1

Text to speech web app development

Day-164: Flask for web app development (Part-7)

0/2

WordCloud app development

Speech to text app using openAI API

Day-165: Flask for web app deployment (Part-8)

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Web application deployment on Amazon AWS EC2 Instance

Day-166: FastAPI (Part-1)

0/2

Intro to FastAPI | is FastAPI really fast?

Installing FastAPI using conda

Day-167: FastAPI (Part-2)

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Book Store app in FastAPI in python

Day-168: FastAPI (Part-3)

0/1

FastAPI basics and running server

Day-169: FastAPI (Part-4)

0/1

Swagger docs in FastAPI

Day-170: FastAPI (Part-5)

0/1

Path parameters in FastAPI

Day-171: FastAPI (Part-6)

0/1

Users path parameters in FastAPI

Day-172: FastAPI (Part-7)

0/1

Titanic app backend development with FastAPI

Day-173: FastAPI (Part-8)

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Add static plots to FastAPI app

Add interactive plots in FastAPI app using plotly

Day-174: FastAPI (Part-9)

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Different port numbers for FastAPI

Day-175: Tableau Complete Course (Part-1)

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What is Tableau?

Tableau Learning Resources

Tableau Public Installation Free

How Tableau Public Looks Like? Interface

Load Data in Tableau Public

Load and handle Dataset in Tableau Public

Data Types check in Tableau Public

Data Filtering and sorting in Tableau Public

Day-176: Tableau Complete Course (Part-2)

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Data Visualization in Tableau Public

Scatter Plots in Tableau Public

Dynamic Scatter Plot in Tableau Public

Flip your charts in tableau public

Bar plots and sorting in Tableau public

GEO Maps in Tableau public

Day-177: Tableau Complete Course (Part-3)

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Your Own Project Visualization in Tableau Public

Saving your plots with Tableau Public

Day-178: Tableau Complete Course (Part-4)

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Area Charts in Tableau Public

Group charts in Tableau

Tableau Interactive Dashboard Development

Tableau Dashboard Deployment for Community

Day-179: Power BI (Part-1)

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Introduction to Data Visualization and Power BI

Power BI vs. Tableau vs. Python?

Top 11 benefits of using Power BI

Day-180: Power BI (Part-2)

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Install Power BI Desktop

Update Power BI features and Updates

Learning Resources for Power BI

Day-181: Power BI (Part-3)

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Business Intelligence with Power BI

Seven Steps Workflow to use Power BI

Power BI interface and Features

Connect Data in Power BI

Day-182: Power BI (Part-4)

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Data Visualization and Plotting in Power BI

Column/Bar charts in Power BI

Create a bar plot like a professional

Pro tips to make a stunning plot in Power BI

Day-183: Power BI (Part-5)

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Pie Charts in power BI

Donut Charts in Power BI

Tree Map in Power BI

Line Chats in Power BI

Six months of AI and Data Science Mentorship Program

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Rana Anjum Sharif 1 week ago

Done

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Shahid Umar 5 months ago

The complete theory of K-Means clustering. I think we should make a maximum of 20 clusters.

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Saman Fatima 5 months ago

Silhoutte Score : ak cluster me majood ak point apny jesy qareebee points sy kitna milta julta ha

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Saman Fatima 5 months ago

Other Method for Optimization of K value is Gap statistic

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Saman Fatima 5 months ago

the "++" in "k++" indicates a smart or improved way to pick the initial points of any cluster. EXAMPLE "Imagine you're trying to group a bunch of points on a graph into, let's say, three groups. The 'k++' thing is a smart way to decide where to start looking for these groups. Instead of just picking starting points randomly, it picks them in a way that makes sure each group gets a good head start and they're not too close to each other. It helps the computer find these groups more efficiently.

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Mehak Iftikhar 5 months ago

What is Global Optima?Uniqueness: In some cases, the objective function might have multiple global optima with the same best value. These are called multimodal optima.Importance: Finding the global optimum is often the ultimate goal of optimization problems, as it represents the best possible solution.Challenges: Finding the global optimum can be challenging, especially in high-dimensional search spaces with complex functions. Many optimization algorithms are only guaranteed to find a local optimum, which might not be the global one.Approaches: Several algorithms and techniques exist for global optimization, each with its own strengths and weaknesses. These include evolutionary algorithms, genetic algorithms, simulated annealing, and particle swarm optimization.

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Danish Ammar 5 months ago

Done

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