Pandas Python Library for EDA Analysis: A Comprehensive Guide

How and Why to Register

0/2

Introduction

15:56

Who is a Data Scientist?

03:20

Software installation and Important Details

0/5

Software installation and websites for this course

08:10

Website introduction and opportunity to learn and earn for Free

12:24

Complete Installation of Softwares

10:35

09:59

Questions answered live on 26 09 2023

06:41

Day-1

0/5

Welcome and 6 months plan

26:27

Laptop buying guide for this course

06:46

LLMs’ websites and logins to learn prompt Engineering

05:37

VScode themes and extensions for Killer look

05:47

Questions and Answers Day-1

06:52

Day-2

0/6

Artificial Intelligence Kya Hai?

27:07

AI vs Data Science vs ML vs DL

Benefits of AI in our Daily life

09:07

Registration Deadline for Paid Zoom Classes

01:04

Day-3

0/13

Data Science vs. AI

00:00

Data Science vs. Data Analytics

00:00

AI and Types of AI

00:00

Reading and Blogs for the course

04:51

History of AI

09:47

Find jobs in AI and Data Science

18:40

Kia AI meri job khaye ge?

11:30

Ideas for AI Developers

04:17

Main Goals of this Course

03:52

Why should I learn Python to learn AI?

05:46

Software Installation to learn Python for AI and Data Science

02:00

Conda Environments for coding in Python

06:14

Python IDEs and VScode

02:38

Day-4

0/1

Start with Python

01:04:08

Day-5

0/5

Conda Environments (A-Z)

36:48

First Line of code in python

Day-6

0/5

Indentations and if conditions in Python

06:40

User input program in python

16:57

Data structures and indexing in python

21:42

Control flow statements in python

21:11

Nested Loops in Python

15:53

Day-7

0/4

Functions & Lmbda funtions in Python

29:18

Modules and Libraries in Python

19:07

Types of Errors in Python

07:14

Jupyter Notebook and File Handling in Python

26:37

Day-8

0/2

Mastering Pandas Library and EDA (Part-1)

01:21:33

Practice on Pandas before next lectures

Day-9

0/2

Quiz No.1: Pandas Basics

15:00

Mastering Pandas Library and EDA (Part-2)

01:00:00

Day-10

0/3

Quiz No.2: Exploratory Data Analysis (EDA)

40:00

Mastering Pandas Library and EDA (Part-3)

01:24:41

Learn MarkDown Language in 72 minutes

72:00

Day-11

0/1

Mastering Pandas Library and EDA (Part-4)

01:05:00

Day-12

0/1

Mastering Pandas Library and EDA (Part-5)

00:00

Day-13

0/2

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

17:00

Automatic EDA with ydata-profiling

00:00

Day-14

0/1

EDA on Google Play Store Data (Part-1)

01:57:00

Day-15

0/2

Exploratory Data Analysis q zaroori hy?

00:00

Missing Values ka rola kia hy?

00:00

Day-16

0/1

Mastering Pandas Library and EDA (Part-8)

35:00

Day-17: Complete EDA on Google PlayStore Apps

0/1

Complete EDA on Google Play Store Apps

01:42:00

Day-18: Outliers

0/6

How to identify Outliers? Visual + IQR Methods

13:23

Z-score method for outliers detection

06:07

How to handle Outliers?

04:54

Outliers in one go (Quick Summary)

04:41

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

0/3

Use AI for Fast paced EDA

29:06

Big Datasets for EDA practice

04:16

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

01:23:19

Day-20

0/6

Pandas Python Library for EDA Analysis: A Comprehensive Guide

00:00

Quiz Time

00:00

Exploratory Data Analysis (EDA) Kyun Zaroori Hai?

00:00

Missing values k Rolay

Day-21: Data Visualization-1

0/3

What is Data Visualization?

42:42

Types of Plots/Charts

28:38

Making plots/charts in Python

29:14

Day-22: Data Science Portfolio

0/6

What is a Data Science Portfolio?

11:52

Elements of a great Data Science Portfolio

11:44

Steps to Create Data Science Portfolio

11:30

What do recruiters look for in Data science jobs?

06:29

How this course will build your portfolio?

11:15

Polish your LinkedIn Profile for Data Science Jobs

27:44

Day-23: Data Visualization-2

0/2

Data Visualization in Python (Blog and Code)

00:00

Data Visualization in Python

01:12:00

Day-24: Data Visualization-3

0/3

Plotting with Seaborn in Python | Data Visualization

44:44

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

45:52

15 important plots to make with plotly in python

49:57

Day-25: Quiz Time, Data Visualization-4

0/1

Data Visualization Quiz

05:00

Day-26: Data Visualization-5

0/5

Sunburst plot in Python using plotly

14:11

Animated Plots in Python

19:49

GEO Maps in Python using plotly: Interactive and Animated maps

32:02

Saving Plotly Plots in gif files

03:39

Python plotly Visualization: A complete Beginner guide

00:00

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

0/2

Feature Scaling/Normalization

49:39

Feature Encoding

19:14

Day-28: NumPy (Part-1)

0/1

Introduction to numpy (a python Library)

43:00

Day-29: NumPy (Part-2)

0/1

Mastering Numpy with extended functions of arrays

01:07:03

Day-30: NumPy (Part-3)

0/1

NumPy indexing and slicing

09:44

Day-31: NumPy (Part-4)

0/1

Creating NumPy arrays from Data

05:11

Day-32a: NumPy (Part-5)

0/1

Matrices in NumPy python library

26:14

Day-32b: Data Preprocessing / Data Wrangling

0/1

Mastering the art of Data preprocessing

00:00

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

0/4

What is Mathematics?

20:54

Branches of Mathematics

17:33

Number Theory

26:32

What did you learn Today?

00:47

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

0/5

Number Theory in few words

07:16

Applications of Number theory

13:13

Pros of learning Number Theory and Mathematics

08:48

Factors and Multiples

09:09

Divisibility Rules in Math

14:14

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

0/4

Numbers and their types in number theory

15:05

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

0/6

What is Algebra?

14:25

Why Algebra is important to learn?

02:39

How to learn Algebra?

Algebra and Data Science

11:26

Day-37: Algebra in Data Science

0/1

Algebra and Data Science (Reading Material)

00:00

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

0/3

Pre-Algebra: Basic to Advanced understanding

00:00

Basics of pre-algebra

11:24

Introduction to pre-algebra

08:14

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

0/4

Fractions

16:09

Fractions and Decimals

12:00

Ratios and proportions

15:19

Percentages and Percent Calculations

19:33

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

0/5

Solving equations

16:02

Measurements, Units and Geometry

09:32

Data Analysis and Algebra

05:45

Problem solving in algebra

03:52

Exercise Questions for pre-algebra

02:46

Day-41: Elementary Algebra

0/2

Day-42: Linear Algebra (Part-1)

0/3

Day-43: Linear Algebra (Part-2)

0/4

Linear Algebra Definitions

06:18

Cartesian coordinates Geometry and Linear Algebra

10:24

Unit Vectors

07:59

Scalars and Scaling a vector

05:14

Day-44: Linear Algebra (Part-3)

0/6

Vectors’ Addition

09:37

Vector span and Linear Dependence of vectors

17:45

Vector Space

04:01

x and y intercepts in linear algebra

21:08

Dot product of vectors

05:02

Cross product of two vectors

09:38

Day-45: Linear Algebra (Part-4)

0/8

Basis Vectors and Transformation

23:37

Linear Transformation and Matrices

24:09

Matrices are Linear Transformations

06:51

Shear Transformations

08:44

Why do we need transformations?

03:11

Matrix Multiplications as compositions

17:30

Matrices and Types (Part-1/2)

06:43

Matrices and Types (Part-2/2)

10:18

Day-46: Linear Algebra (Part-5)

0/5

Addition and Subtraction of matrices

03:21

Matrices Multiplication

05:27

Importance of Matrices Multiplication in Data Science and AI

01:05

Determinants of Matrices

10:14

Important Tasks

00:18

Day-47: Linear Algebra (part-6)

0/6

Types of Linear Equations

04:00

Writing System of Equations

18:39

Matrix form of System of Equations

11:17

Assignment Alert

01:57

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

0/7

Solving system of equations

Matrix inversion Method

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

0/5

Advance Methods to solve system of equations

02:47

Gaussian Elimination Method

08:50

Gauss-Jordan Elimination Method

08:16

LU Decomposition Method

11:07

Singular Value Decomposition (SVD)

38:51

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

0/4

Row Operations Permissible vs. non-permissible

04:59

Eigenvalues and Eigenvectors

16:15

SVD in Python

10:37

Important Tasks before upcoming lectures

01:83

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

0/9

Why statistics is important?

07:15

Scales or Levels of Measurements

17:26

Qualitative vs. Quantitative Data

09:38

Discrete vs. Continuous vs. Binary Data

06:56

What is Time Series Data?

03:03

What is spatial Data?

02:25

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

0/5

Categorical vs. Ordinal Categorical Data

04:43

Multivariate Data Type

03:02

Structured vs. Unstructured Data

09:34

Boolean Data Type

01:18

Operationalization and Proxy Measurements

08:25

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

0/8

Surrogate Endpoints in statistics

09:27

True and Error Score

10:30

Types of Errors in Data Collection

11:21

Type-I and Type-II errors

11:59

Examples from the Audience for Type I and Type II errors

03:08

Reliability and validity

15:39

Triangulation

15:31

Notes of All lectures on ABC of statistics for Data Science

04:06

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

0/6

Measurement and Data Bias

27:59

How to remove bias?

08:39

Statistics and types of Statistics

18:19

Why is statistics important to learn?

10:38

Data Analysis and Types of Data Analysis

15:43

Next Tasks to Learn: Sample and Population

03:07

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

0/1

Central Tendency: Mean, Median, and Mode

06:20

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

0/8

Central Tendency of the Data Complete Guide

10:33

Mean

24:42

Hormonic Mean understanding of the formula

01:19

Limitations of using mean in Data Science and statistics

Population vs. sample and their means

06:42

Variability | Dispersion | Spread of the Data

21:02

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

0/5

Range

09:28

Inter quartile Range (IQR)

17:39

Variance

12:41

Standard Deviation and Standard Error

24:03

Normal distribution and Standard Deviation

07:43

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

0/2

Data Distributions and types of data distributions

45:25

Skewness and Kurtosis

45:30

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

0/2

Primary vs. Secondary | Data

13:43

Next Tasks for upcoming lectures on sampling

01:19

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

0/5

Data Collection in the age of big data

16:24

Best Practices for Data Collection

10:00

Representative and Non-representative sample types

11:59

Details of each Sampling Techniques for Data Collection

10:27

Hybrid Sampling

00:33

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

0/4

Descriptive Statistics

16:00

EDA and Statistics

18:53

How to choose the right statistical method?

23:39

EDA and the Four Pillars of statistics

05:04

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

0/4

Dependent vs. independent variables

09:59

Inferential Statistics

07:17

Hypothesis Testing

21:03

Confidence Interval in statistics

10:06

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

0/6

Chi-Square Test in Python

12:38

Next Statistical tests

00:53

Test for Normal Distribution Shapiro-Wilk Test in Python

08:23

t-tests in Python

12:07

Levene’s test in Python

04:19

One-way ANOVA in python

04:57

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

0/3

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

27:43

MANOVA in python

05:56

Correlation in Python

15:40

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

0/6

Chi-squared test case study in python

18:52

t-test and case studies in python

16:49

One-way and Two-way ANOVA case studies

19:39

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

14:41

EDA and basic Pillars of Statistics

04:18

Book announcement for Statistics ABC of statistics

01:33

Day-67: Machine Learning (Part-1)

0/2

What is machine learning?

07:27

Types of Machine Learning

15:38

Day-68: Machine Learning (Part-2)

0/6

ML Model Building to Deployment | Steps A-Z

34:28

What is an algorithm?

08:25

Training and Testing Data | Features and Labels | Model

04:19

Overfitting vs. Underfitting

10:14

Important Python libraries for Machine Learning

07:29

Installation steps

14:45

Day-69: Machine Learning (Part-3)

0/1

Data Pre-processing before ML Model

30:29

Day-70: Machine Learning (Part-4)

0/2

Steps in Data Pre-processing

07:23

Dealing with Missing values in Python

45:52

Day-71: Machine Learning (Part-5)

0/3

Dealing with Data Inconsistencies / Anomalies

25:40

Outliers | Data Pre-processing

45:19

IQR Method to remove Outliers

17:39

Day-72: Machine Learning (Part-6)

0/5

Data Preprocessing Steps

02:07

Feature Scaling and Normalization

21:44

Standard Scaling or standardizing the data

10:55

Day-73: Machine Learning (Part-7)

0/7

Min-max, MaxAbs, and Robust Scalars in python

04:16

Most used Scaling methods for feature scaling

00:44

Normalization and Nonlinear Data Transformation of data

14:24

L2 and L1 Normalization in python

12:37

Feature Scaling vs. Normalization

09:03

Tips about Scaling and normalization

02:51

Assignment Alert

01:55

Day-74: Machine Learning (Part-8)

0/7

What is feature encoding?

13:06

Benefits of feature encoding

12:44

Feature Encoding in Python using scikit-learn

20:49

Feature-encoding with pandas python

04:46

Data Discretization | Data Binning

17:48

Data preprocessing steps completed

03:12

Scikit-learn base Jupyter notebook

20:04

Day-75: Machine Learning (Part-9)

0/1

Linear Regression A-Z

01:03:55

Day-76: Machine Learning (Part-10)

0/3

Logistic Regression (Theory)

08:08

Logistic Regression and example in Python A-Z

29:10

Evaluation Metrics for Regression & Classification Models

08:13

Day-77: Machine Learning (Part-11)

0/5

Train test split matters

20:32

Support Vector Machines (SVM) Theory

23:49

Support Vector Machines (SVM) in Python

18:59

Assignment Alert about SVM

00:29

Upload Notebooks on Kaggle

17:32

Day-78: Machine Learning (Part-12)

0/7

K-Nearest Neighbors (KNN) Theory

Why Minkowski Distance is Important?

03:44

Hamming Distance

04:55

K-Nearest Neighbors (KNN) in Python

20:26

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

0/6

Algorithms we have learned so far

07:46

What is Decision tree Algorithm?

09:33

Elements of Decision Tree

12:52

Entropy, Gini impurity and information gain theory

22:09

Entropy, Gini impurity and information gain in python

16:20

Decision Tree Classifier in Python

23:02

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

0/3

Ensemble Algorithms

21:22

Random Forest (Theory)

18:55

Random Forest in Python for Classification and Regression

17:25

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

0/2

Evaluation Metrics in Machine Learning

04:31

Regression Metrics

19:23

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

0/1

Evaluation Metrics for Classification Algorithms

31:00

Day-83: Machine Learning (Part-17)

0/2

Ensemble Algorithms Family

08:00

Boosting in Ensemble Methods

21:29

Day-84: Machine Learning (Part-18)

0/3

Boosting algorithms and their pros. and cons.

11:48

Booting algorithms vs. Neural Networks

13:30

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

23:13

Day-85: Machine Learning (Part-19)

0/1

catBoost algorithm in Python

16:13

Day-86: Machine Learning (Part-20)

0/3

Hyperparameter Tuning and Cross Validation

18:57

Cross Validation in machine learning (Part-1)

12:23

Cross Validation in machine learning (Part-2)

02:24

Day-87: Machine Learning (Part-21)

0/2

pipeline in Machine Learning using Scikit-learn

18:57

Best Model Selection in Python using Scikit-learn

09:53

Day-88: Machine Learning (Part-22)

0/4

Naive Bayes Algorithm (Part-1)

16:18

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

06:18

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

07:17

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

01:06

Day-89: Machine Learning (Part-23)

0/1

Best hyperparameter tuned Model Selection

24:09

Day-90: Machine Learning (Part-24)

0/3

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

24:11

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

26:04

Question and Answers session on 90th day of this course

38:31

Day-91: Machine Learning (Part-25)

0/1

Encoding and Inverse Transform the data

26:47

Day-92: Machine Learning (Part-26)

0/4

Basic Method | Missing Values Imputation (Part-1)

11:11

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

13:07

Advance Methods | Missing Values Imputation (Part-3)

04:01

Last lecture of 2023 for AI and Data Science

00:15

Day-93: Machine Learning (Part-27)

0/1

Supervised Machine Learning and Assumptions

08:10

Day-94: Machine Learning (Part-28)

0/2

Mathematics and Assumptions of Linear Regression

09:35

Multilinear Regression and Mathematics with Assumptions

03:08

Day-95: Machine Learning (Part-29)

0/2

Polynomial Regression Theory and Assumptions

14:10

Polynomial Regression in Python with coding

08:18

Day-96: Machine Learning (Part-30)

0/2

Ridge Regression in Python L2 Regularization

23:46

Lasso Regression | L1 Regularization

13:04

Day-97: Machine Learning (Part-31)

0/3

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

45:08

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

01:17:16

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

21:27

Day-98: Machine Learning (Part-32)

0/2

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

11:04

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

08:26

Day-99: Machine Learning (Part-33)

0/2

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

17:07

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

30:24

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

0/4

Un-supervised Machine Learning – Introduction

40:04

What is Clustering?

16:16

k-Means clustering – Theory

01:08:43

k-Mean clustering in python

01:12:07

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

0/6

Hierarchical Clustering | Theory

36:28

Hierarchical Clustering | Coding in python

Gaussian Mixture Models (GMMs) | Theory

27:58

Gaussian Mixture Models (GMMs) | Metrics

10:44

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

0/6

Feature Engineering (Part-1)

01:01:34

Feature Engineering (Part-2)

28:27

PCA | Principal Component Analysis | Theory

01:12:44

PCA | Principal Component Analysis | Case Study in Python

30:39

SVD | Singular Value Decomposition

27:43

t-SNE | t-distributed Stochastic Neighbor Embedding

01:13:41

Day-103: Deep Learning (Part-1)

0/3

Introduction to Deep Learning

05:00

Machine Learning vs. Deep Learning

13:58

Small Data vs. Big Data for Deep Learning

02:30

Day-104: Deep Learning (Part-2)

0/2

What is a Neural Network?

21:17

Types of Neural Network

07:07

Day-105: Deep Learning (Part-3)

0/2

Architecture of Neural Networks

04:34

Single Layer vs. Multilayer Neural Network

03:16

Day-106: Deep Learning (Part-4)

0/3

Multi Layer Perceptron

16:25

Types of Multi Layer Perceptron

09:49

Applications of Multi layer perceptron

04:29

Day-107: Deep Learning (Part-5)

0/3

Python Libraries for Deep Learning

17:03

Ten steps to create a neural Network

10:52

Build Neural Networks in Python with TensorFlow

24:01

Day-108: Deep Learning (Part-6)

0/2

GPU for Deep Learning with TensorFlow

03:21

Build a Multilayer Perceptron in Python with TensorFlow

13:25

Day-109: Deep Learning (Part-7)

0/2

Call Back Function for Early Stopping of epochs

07:47

How many neurons should be in each layer?

08:36

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

0/4

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

54:10

tanH, ReLu, Leaky Relu, Paramteric ReLu activation functions

25:53

Softmax activation function | Multiclass classification

10:31

How to choose an activation function for neural network?

17:29

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

0/5

Case Study on UCI Heart Disease Data Project 1

24:51

Case Study on UCI Heart Disease Data Project 2

34:39

Case Study on UCI Heart Disease Data Project 3

23:11

Case Study on UCI Heart Disease Data Project 4

10:23

Competitions and Assignments for the course

10:56

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

0/2

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

01:04:07

What is random seeding or Random State in ML?

02:29

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

0/3

Case Study on Bank Churn Dataset

01:05:05

Important Assignment for Kaggle Competitions

00:57

Announcement about the AI Job Ready Course on Codanics

03:04

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

0/3

Convolutional Neural Network (CNN) | Part-1

15:03

Convolutional Neural Network (CNN) | Part-2

35:06

Convolutional Neural Network (CNN) | Part-3

36:53

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

0/3

Convolutional Neural Network (CNN) | Part-4

26:57

Convolutional Neural Network (CNN) | Part-5

01:01:29

Convolutional Neural Network (CNN) | Part-6

31:10

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

0/3

What is Computer Vision?

30:19

Python & Computer vision

22:05

Assignment alert

01:06

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

0/1

Paddy Rice Disease Detection Project

54:43

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

0/3

Introduction to RNN

14:05

RNN In depth Understanding

58:41

RNN and Python for Project making

17:33

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

0/2

Introduction to Natural Language Processing

01:01:46

Sentiment Analysis and NLP

46:29

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

0/3

LSTM (Long Short Term Memory) for NLP

27:05

History of ANN, CNN, RNN, LSTM, GRU

50:16

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

24:41

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

0/1

Introduction to Time Series Analaysis

01:09:47

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

0/3

Data Analysis Types for Time Series Data

32:04

Types of Data about Time

05:09

Time series analysis and plotting in python

28:51

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

0/1

Time Series Project understanding and development

51:25

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

0/3

Time series Weather forecasting project (Part-1)

01:12:31

Time series Weather forecasting project (Part-2)

01:21:36

Time series Weather forecasting project (Part-2)

17:14

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

0/2

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

01:07:57

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

26:21

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

0/3

What is web scrapping?

32:30

Web scrapping in python

26:21

Stock market data scrapping in python

23:04

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

0/3

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

43:34

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

08:45

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

17:35

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

0/1

ARIMA, SARIMA, SARIMAX in python complete project

01:04:15

Day-129: git & GitHub Crash Course

0/1

git and GitHub crash course

41:54

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

0/3

Underfitting in a Machine/Deep Learning Model

07:50

Overfitting in a Machine/Deep Learning Model

06:38

Good Fit or Robust Model in Machine/Deep Learning

04:37

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

0/2

How to Improve Model’s Fitness?

08:38

Project Based Improving Model’s Fitness

23:39

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

0/3

Introduction to Transfer learning

19:55

Transfer Learning in Python using TensorFlow

17:05

Assignment Alert for Transfer Learning

01:02

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

0/1

Use of TensorFlow for Image Classification – A complete Project

44:37

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

0/1

MobileNetV2 pre-trained Mode: A complete Project

30:27

Day-135: Generative AI (Part-1)

0/2

What is Generative AI?

13:08

Important Key Terms used in Generative AI

33:04

Day-136: Generative AI (Part-2)

0/2

How does Generative AI work?

07:14

Generative AI in 2024

15:01

Day-137: Generative AI (Part-3)

0/1

Generative AI | Kia meri job kha jaye ge?

05:44

Day-138: Generative AI (Part-4)

0/2

Progressive growing Generative Adversarial Networks (GANs)

51:05

Assignment Alert

02:33

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

0/1

TensorBoard: a crash course

38:47

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

0/2

Introduction to Diffusion Models

17:14

Stable Diffusion Models in python

21:38

Day-141: Generative AI (Part-7)

0/2

SORA: An openAI’s model for AI video generation

10:17

Important Logins to create before next lecture

03:45

Day-142: Prompt Engineering

0/5

Introduction to prompt Engineering

24:36

Types of Prompt Enginnering

08:06

Prompt Designing Strategies to write best prompt

11:48

Generate Prompts from LLMs

08:09

LLMs and prompt engineering live demo

44:03

Day-143: Huggingface

0/1

Use pre trained models from Huggingface

33:08

Day-144: LangChain and APIs

0/4

Introduction to API (Application Programming Interface)

25:30

LangChain workflow for LLMs

09:20

Applications of LangChain for LLMs

07:47

LangChain + OpenAI API + HuggingFace API

15:43

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

0/3

Introduction and installation

16:01

My first app in streamlit

20:30

streamlit cheat sheet

01:06

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

0/1

Streamlit Data Science application

26:14

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

0/1

Streamlit webapp without coding but prompting

45:09

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

0/2

Creating a word Cloud app on streamlit python

17:00

requirements.txt file for streamlit apps

03:43

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

0/1

Streamlit app deployment on free servers

16:29

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

0/1

Create an app only in 14 lines of codes

06:30

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

0/1

Manage Secrets in streamlit deployment with python

13:48

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

0/1

ask the pdf and other docx application

22:33

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

0/1

Streamlit project read pdfs in a folder

27:47

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

0/1

Stock Market Analysis and Prediction app

18:59

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

0/1

Dall-e-3 Image generation app in python

12:45

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

0/1

Speech to text Streamlit Application development

17:44

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

0/1

Text to speech Streamlit application

32:16

How to Earn using Data Science and AI skills

0/1

How to earn online using Data Science and AI

51:04

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

0/2

is FLASK better than streamlit?

09:07

Installation and basics of web app development in flask

24:13

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

0/5

flask Installation and basic application development

17:02

Open Conda environments inside vscode

06:51

Flask app for basic EDA and plotting

09:21

Image Generation app development in flask python

10:58

Assignment

00:50

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

0/1

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

12:32

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

0/1

End to End machine Learning project in flask | Tip prediction

07:54

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

0/1

Diabetes prediction app development

24:51

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

0/1

Text to speech web app development

15:19

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

0/2

WordCloud app development

05:55

Speech to text app using openAI API

09:44

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

0/1

Web application deployment on Amazon AWS EC2 Instance

53:06

Day-166: FastAPI (Part-1)

0/2

Intro to FastAPI | is FastAPI really fast?

07:52

Installing FastAPI using conda

12:33

Day-167: FastAPI (Part-2)

0/1

Book Store app in FastAPI in python

10:52

Day-168: FastAPI (Part-3)

0/1

FastAPI basics and running server

07:26

Day-169: FastAPI (Part-4)

0/1

Swagger docs in FastAPI

04:35

Day-170: FastAPI (Part-5)

0/1

Path parameters in FastAPI

09:46

Day-171: FastAPI (Part-6)

0/1

Users path parameters in FastAPI

05:53

Day-172: FastAPI (Part-7)

0/1

Titanic app backend development with FastAPI

05:47

Day-173: FastAPI (Part-8)

0/2

Add static plots to FastAPI app

05:10

Add interactive plots in FastAPI app using plotly

07:25

Day-174: FastAPI (Part-9)

0/1

Different port numbers for FastAPI

07:18

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

0/8

What is Tableau?

03:24

Tableau Learning Resources

01:14

Tableau Public Installation Free

05:20

How Tableau Public Looks Like? Interface

03:14

Load Data in Tableau Public

04:10

Load and handle Dataset in Tableau Public

07:08

Data Types check in Tableau Public

05:49

Data Filtering and sorting in Tableau Public

05:57

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

0/6

Data Visualization in Tableau Public

11:12

Scatter Plots in Tableau Public

07:51

Dynamic Scatter Plot in Tableau Public

05:30

Flip your charts in tableau public

00:55

Bar plots and sorting in Tableau public

08:29

GEO Maps in Tableau public

05:15

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

0/2

Your Own Project Visualization in Tableau Public

05:03

Saving your plots with Tableau Public

05:44

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

0/4

Area Charts in Tableau Public

06:01

Group charts in Tableau

02:25

Tableau Interactive Dashboard Development

11:37

Tableau Dashboard Deployment for Community

07:19

Day-179: Power BI (Part-1)

0/3

Introduction to Data Visualization and Power BI

07:54

Power BI vs. Tableau vs. Python?

08:55

Top 11 benefits of using Power BI

11:05

Day-180: Power BI (Part-2)

0/3

Install Power BI Desktop

06:45

Update Power BI features and Updates

04:25

Learning Resources for Power BI

03:52

Day-181: Power BI (Part-3)

0/4

Business Intelligence with Power BI

11:08

Seven Steps Workflow to use Power BI

04:40

Power BI interface and Features

12:20

Connect Data in Power BI

14:13

Day-182: Power BI (Part-4)

0/4

Data Visualization and Plotting in Power BI

03:33

Column/Bar charts in Power BI

07:57

Create a bar plot like a professional

16:37

Pro tips to make a stunning plot in Power BI

14:54

Day-183: Power BI (Part-5)

0/4

Pie Charts in power BI

04:30

Donut Charts in Power BI

03:52

Tree Map in Power BI

03:24

Line Chats in Power BI

06:24

Six months of AI and Data Science Mentorship Program

About Lesson

Pandas Python Library for EDA Analysis: A Comprehensive Guide

Join the conversation

Hammad Kamran Faruki 11 months ago

I think its help me a lot to clear some of my doubt. Great Blogs

Shahid Umar 11 months ago

We can load/import from 40 file formats through PANDAS libraries and write/export in 46 file formats.

Sana Shah 11 months ago

Pandas are so interesting and it's fun while coding just because of Sir Ammar, his way of teaching method is directly and clearly understandable

komal Baloch 11 months ago

done

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