Qualitative vs. Quantitative Data

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

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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

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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)

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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

Categorical vs. Numerical Data

Join the conversation

kashan malik 8 months ago

DONE

Shahid Umar 10 months ago

A deep understanding of "Qualitative Data" and "Quantitative Data" is very important when analyzing data in Python.

Sibtain Ali 10 months ago

I learned qualitative, and quantitative.

tayyab Ali 10 months ago

I learned qualitative, and quantitative.

Javed Ali 10 months ago

AOA, I learned in this lecture about categorical and numerical data.

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