Course Content
How and Why to Register
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Introduction
15:56
Who is a Data Scientist?
03:20
Software installation and Important Details
In this section you will learn which software are important to install and learn from.
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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
Login to made for the course
09:59
Questions answered live on 26 09 2023
06:40
Day-1
Introduction and details about 6 months plan
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Welcome and 6 months plan
26:26
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
What is AI and How is it Different From Data Science?
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Artificial Intelligence Kya Hai?
27:07
AI vs Data Science vs ML vs DL
06:25
Assignments
01:00
Big Players in AI
07:42
Benefits of AI in our Daily life
09:07
Registration Deadline for Paid Zoom Classes
01:04
Day-3
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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
04:50
History of AI
09:46
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?
05:45
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
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Start with Python
01:04:08
Day-5
Learn Python from Zero
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Conda Environments (A-Z)
36:48
First Line of code in python
29:43
Variables in Python
13:03
Operator in python
23:59
Data Types in Python
11:33
Day-6
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Indentations and if conditions in Python
06:40
User input program in python
16:56
Data structures and indexing in python
21:42
Control flow statements in python
21:11
Nested Loops in Python
15:53
Day-7
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Functions & Lmbda funtions in Python
29:19
Modules and Libraries in Python
19:06
Types of Errors in Python
07:15
Jupyter Notebook and File Handling in Python
26:36
Day-8
Master Pandas Library
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Mastering Pandas Library and EDA (Part-1)
01:21:33
Practice on Pandas before next lectures
Day-9
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Quiz No.1: Pandas Basics
15:00
Mastering Pandas Library and EDA (Part-2)
01:00:00
Day-10
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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
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Mastering Pandas Library and EDA (Part-4)
01:05:00
Day-12
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Mastering Pandas Library and EDA (Part-5)
00:00
Day-13
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Quiz No.3: Master Pandas and EDA with Interactive Quiz
17:00
Automatic EDA with ydata-profiling
00:00
Day-14
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EDA on Google Play Store Data (Part-1)
01:57:00
Day-15
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Exploratory Data Analysis q zaroori hy?
00:00
Missing Values ka rola kia hy?
00:00
Day-16
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Mastering Pandas Library and EDA (Part-8)
35:00
Day-17: Complete EDA on Google PlayStore Apps
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Complete EDA on Google Play Store Apps
01:42:00
Day-18: Outliers
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What are outliers?
22:23
Types of Outliers
09:48
How to identify Outliers? Visual + IQR Methods
13:23
Z-score method for outliers detection
06:06
How to handle Outliers?
04:53
Outliers in one go (Quick Summary)
04:41
Day-19: Complete EDA project on Apple APP store apps Dataset
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Use AI for Fast paced EDA
29:12
Big Datasets for EDA practice
04:16
Complete EDA on Apple APP store Data (A-Z)
01:23:20
Day-20
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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
00:00
Outlier-1
00:00
Outlier-2
00:00
Day-21: Data Visualization-1
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What is Data Visualization?
42:42
Types of Plots/Charts
28:38
Making plots/charts in Python
39:14
Day-22: Data Science Portfolio
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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
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Data Visualization in Python (Blog and Code)
00:00
Data Visualization in Python
01:12:00
Day-24: Data Visualization-3
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Plotting with Seaborn in Python | Data Visualization
44:45
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
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Data Visualization Quiz
05:00
Day-26: Data Visualization-5
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Sunburst plot in Python using plotly
14:11
Animated Plots in Python
19:48
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
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Feature Scaling/Normalization
49:39
Feature Encoding
19:14
Day-28: NumPy (Part-1)
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Introduction to numpy (a python Library)
43:00
Day-29: NumPy (Part-2)
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Mastering Numpy with extended functions of arrays
01:07:03
Day-30: NumPy (Part-3)
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NumPy indexing and slicing
09:44
Day-31: NumPy (Part-4)
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Creating NumPy arrays from Data
05:11
Day-32a: NumPy (Part-5)
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Matrices in NumPy python library
26:14
Day-32b: Data Preprocessing / Data Wrangling
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Mastering the art of Data preprocessing
00:00
Day-33: Zero-to-Math for Data Science (Part-1)
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What is Mathematics?
20:54
Branches of Mathematics
17:33
Number Theory
26:31
What did you learn Today?
00:47
Day-34: Zero-to-Math for Data Science (Part-2)
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Number Theory in few words
07:16
Applications of Number theory
13:12
Pros of learning Number Theory and Mathematics
08:48
Factors and Multiples
09:09
Divisibility Rules in Math
14:13
Day-35: Zero-to-Math for Data Science (Part-3)
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GCD and LCM in math
07:15
Modular Arithmetic
14:20
What is Modulus?
00:34
Numbers and their types in number theory
15:05
Day-36: Zero-to-Math for Data Science (Part-4)
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What is Algebra?
14:25
Why Algebra is important to learn?
02:39
How to learn Algebra?
03:03
Types of Algebra
10:22
History of Algebra
06:33
Algebra and Data Science
11:25
Day-37: Algebra in Data Science
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Algebra and Data Science (Reading Material)
00:00
Day-38: Pre-Algebra (Part-1)
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Pre-Algebra: Basic to Advanced understanding
00:00
Basics of pre-algebra
11:24
Introduction to pre-algebra
08:13
Day-39: Pre-Algebra (Part-2)
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Fractions
16:09
Fractions and Decimals
12:18
Ratios and proportions
15:19
Percentages and Percent Calculations
19:32
Day-40: Pre-Algebra (Part-3)
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Solving equations
16:01
Measurements, Units and Geometry
09:31
Data Analysis and Algebra
05:45
Problem solving in algebra
03:52
Exercise Questions for pre-algebra
02:45
Day-41: Elementary Algebra
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Elementary Algebra
19:11
Important Tasks
02:05
Day-42: Linear Algebra (Part-1)
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What is a vector?
21:45
What are Matrices?
09:43
Next Tasks
02:50
Day-43: Linear Algebra (Part-2)
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Linear Algebra Definitions
06:18
Cartesian coordinates Geometry and Linear Algebra
10:23
Unit Vectors
07:59
Scalars and Scaling a vector
05:14
Day-44: Linear Algebra (Part-3)
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Vectors’ Addition
09:37
Vector span and Linear Dependence of vectors
17:45
Vector Space
04:38
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)
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Basis Vectors and Transformation
23:37
Linear Transformation and Matrices
24:09
Matrices are Linear Transformations
06:50
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)
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Addition and Subtraction of matrices
03:22
Matrices Multiplication
05:27
Importance of Matrices Multiplication in Data Science and AI
01:05
Determinants of Matrices
10:14
Important Tasks
00:17
Day-47: Linear Algebra (part-6)
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Inverse of a Matrix
23:11
System of Equations
08:39
Types of Linear Equations
03:59
Writing System of Equations
18:39
Matrix form of System of Equations
11:16
Assignment Alert
01:57
Day-48: Linear Algebra (Part-7)- Solving System of Equations
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Solving system of equations
05:50
Graphical Method
23:54
Substitution Method
14:06
Elimination Method
09:18
Matrix inversion Method
10:49
Advance Methods
16:28
Assignment Alert
01:09
Day-49: Linear Algebra (Part-8)- Solving System of Equations
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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
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Row Operations Permissible vs. non-permissible
04:59
Eigenvalues and Eigenvectors
16:15
SVD in Python
10:37
Important Tasks before upcoming lectures
01:42
Day-51: Linear Algebra (Part-10) – Python
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Linear Algebra Articles
05:44
Solving System of Equations in python
08:31
Solve system of complex equations in python
05:02
Vectors in Python
04:41
Linear Transformation in python
07:11
Shear Transformation in python
04:02
singular value decompositions (SVD) in python
08:57
Linear Algebra-Notes
00:00
Vectors in Linear Algebra-Notes
00:00
Linear Transformation-Notes
00:00
Matrices-Notes
00:00
Statistics is next
00:52
Day-52: Statistics for Data Science (Part-1)
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Introduction
08:03
What is Statistics?
06:05
What will we learn?
08:09
Why statistics is important?
07:15
Scales or Levels of Measurements
17:26
Qualitative vs. Quantitative Data
09:37
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)
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Categorical vs. Ordinal Categorical Data
04:43
Multivariate Data Type
03:01
Structured vs. Unstructured Data
09:34
Boolean Data Type
01:17
Operationalization and Proxy Measurements
08:25
Day-54: Statistics for Data Science (Part-3)
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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)
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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?
10:37
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)
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Central Tendency: Mean, Median, and Mode
06:20
Day-57: Statistics for Data Science (Part-6)
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Central Tendency of the Data Complete Guide
10:33
Mean
24:42
Hormonic Mean understanding of the formula
01:18
Limitations of using mean in Data Science and statistics
04:15
Median in statistics
12:54
Mode in Statistics
15:17
Population vs. sample and their means
06:42
Variability | Dispersion | Spread of the Data
21:02
Day-58: Statistics for Data Science (Part-7)
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Range
09:28
Inter quartile Range (IQR)
17:39
Variance
12:40
Standard Deviation and Standard Error
24:03
Normal distribution and Standard Deviation
07:43
Day-59: Statistics for Data Science (Part-8)
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Data Distributions and types of data distributions
45:24
Skewness and Kurtosis
45:30
Day-60: Statistics for Data Science (Part-9)
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Primary vs. Secondary | Data
13:43
Next Tasks for upcoming lectures on sampling
01:19
Day-61: Statistics for Data Science (Part-10)
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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)
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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)
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Dependent vs. independent variables
09:59
Inferential Statistics
07:17
Hypothesis Testing
21:03
Confidence Interval in statistics
10:05
Day-64: Statistics for Data Science (Part-13)
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Chi-Square Test in Python
12:37
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)
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ANOVA | one-way, 2-way and N-way in Python
27:42
MANOVA in python
05:55
Correlation in Python
15:40
Day-66: Statistics for Data Science (Part-15)
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Chi-squared test case study in python
18:51
t-test and case studies in python
16:48
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)
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What is machine learning?
07:27
Types of Machine Learning
15:37
Day-68: Machine Learning (Part-2)
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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
14:44
Day-69: Machine Learning (Part-3)
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Data Pre-processing before ML Model
30:28
Day-70: Machine Learning (Part-4)
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Steps in Data Pre-processing
07:23
Dealing with Missing values in Python
45:52
Day-71: Machine Learning (Part-5)
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Dealing with Data Inconsistencies / Anomalies
25:39
Outliers | Data Pre-processing
45:19
IQR Method to remove Outliers
17:39
Day-72: Machine Learning (Part-6)
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Data Merging
09:03
Data concatenation
03:58
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)
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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
01:54
Day-74: Machine Learning (Part-8)
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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
17:47
Data preprocessing steps completed
03:12
Scikit-learn base Jupyter notebook
20:04
Day-75: Machine Learning (Part-9)
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Linear Regression A-Z
01:03:55
Day-76: Machine Learning (Part-10)
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Logistic Regression (Theory)
08:07
Logistic Regression and example in Python A-Z
29:10
Evaluation Metrics for Regression & Classification Models
08:12
Day-77: Machine Learning (Part-11)
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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
17:31
Day-78: Machine Learning (Part-12)
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K-Nearest Neighbors (KNN) Theory
21:48
Euclidean Distance
19:01
Manhattan Distance
09:09
Minkowski Distance
08:55
Why Minkowski Distance is Important?
03:44
Hamming Distance
04:55
K-Nearest Neighbors (KNN) in Python
20:25
Day-79: Machine Learning (Part-13) Decision Tree
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Algorithms we have learned so far
07:45
What is Decision tree Algorithm?
09:33
Elements of Decision Tree
12:51
Entropy, Gini impurity and information gain theory
22:09
Entropy, Gini impurity and information gain in python
16:20
Decision Tree Classifier in Python
23:01
Day-80: Machine Learning (Part-14)-Ensemble Algorithms
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Ensemble Algorithms
21:21
Random Forest (Theory)
18:55
Random Forest in Python for Classification and Regression
17:25
Day-81: Machine Learning (Part-15)-Evaluation Metrics
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Evaluation Metrics in Machine Learning
04:31
Regression Metrics
19:22
Day-82: Machine Learning (Part-16)-Metrics for Classification
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Evaluation Metrics for Classification Algorithms
30:59
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)
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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)
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catBoost algorithm in Python
16:13
Day-86: Machine Learning (Part-20)
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Hyperparameter Tuning and Cross Validation
18:57
Cross Validation in machine learning (Part-1)
12:22
Cross Validation in machine learning (Part-2)
02:24
Day-87: Machine Learning (Part-21)
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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)
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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)
07:16
Questions and Answers | Naive Bayes Algorithm (Part-4)
01:06
Day-89: Machine Learning (Part-23)
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Best hyperparameter tuned Model Selection
24:08
Day-90: Machine Learning (Part-24)
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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)
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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)
13:06
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
14:09
Polynomial Regression in Python with coding
08:18
Day-96: Machine Learning (Part-30)
0/2
Ridge Regression in Python L2 Regularization
23:45
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)
01:17:15
Heart Disease prediction | A complete ML Project (Part-3)
21:27
Day-98: Machine Learning (Part-32)
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Heart Disease prediction | A complete ML Project (Part-4)
11:03
Heart Disease prediction | A complete ML Project (Part-5)
08:25
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?
16:15
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
36:27
Hierarchical Clustering | Coding in python
23:57
DBSCAN
53:04
OPTICS
11:27
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
01:13:40
Day-103: Deep Learning (Part-1)
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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
07:06
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
24:00
Day-108: Deep Learning (Part-6)
0/2
GPU for Deep Learning with TensorFlow
03:20
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
07:46
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
10:30
How to choose an activation function for neural network?
17:29
Day-111: Case Studies (Complete Machine/Deep Learning Projects)
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Case Study on UCI Heart Disease Data Project 1
24:51
Case Study on UCI Heart Disease Data Project 2
34:38
Case Study on UCI Heart Disease Data Project 3
23:11
Case Study on UCI Heart Disease Data Project 4
10:22
Competitions and Assignments for the course
10:55
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
01:05:04
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)
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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)
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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
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What is Computer Vision?
30:19
Python & Computer vision
22:04
Assignment alert
01:06
Day-117: Deep Learning (Part-14)-Complete CNN Project
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Paddy Rice Disease Detection Project
54:42
Day-118: Deep Learning (Part-15)-Recurrent Neural Network (RNN)
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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)
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Introduction to Natural Language Processing
01:01:46
Sentiment Analysis and NLP
46:29
Day-120: Deep Learning (Part-17)-Natural Language Processing (NLP)
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LSTM (Long Short Term Memory) for NLP
27:04
History of ANN, CNN, RNN, LSTM, GRU
50:16
LSTM vs. GRU types of RNN for Deep learning in NLP
24:40
Day-121: Time Series Analysis (Part-1)
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Introduction to Time Series Analaysis
01:09:47
Day-122: Time Series Analysis (Part-2)
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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)
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Time Series Project understanding and development
51:25
Day-124: Time Series Analysis (Part-4)
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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)
17:13
Day-125: Time Series Analysis (Part-5)
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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)
26:20
Day-126: Time Series Analysis (Part-6): Web-scrapping
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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
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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
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git and GitHub crash course
41:54
Day-130: Over-fitting and Under-fitting in a Model
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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
04:36
Day-131: Improving Machine/Deep Learning Model’s Performance
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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)
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Introduction to Transfer learning
19:54
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)
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Use of TensorFlow for Image Classification – A complete Project
44:37
Day-134 Transfer Learning and Pre-trained Models (Part-3)
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MobileNetV2 pre-trained Mode: A complete Project
30:27
Day-135: Generative AI (Part-1)
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What is Generative AI?
13:07
Important Key Terms used in Generative AI
33:04
Day-136: Generative AI (Part-2)
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How does Generative AI work?
07:14
Generative AI in 2024
15:01
Day-137: Generative AI (Part-3)
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Generative AI | Kia meri job kha jaye ge?
05:43
Day-138: Generative AI (Part-4)
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Progressive growing Generative Adversarial Networks (GANs)
51:04
Assignment Alert
02:33
Day-139: Generative AI (Part-5)-Tensorboard
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TensorBoard: a crash course
38:46
Day-140: Generative AI (Part-6)-Diffusion Models
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Introduction to Diffusion Models
17:14
Stable Diffusion Models in python
21:38
Day-141: Generative AI (Part-7)
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SORA: An openAI’s model for AI video generation
10:17
Important Logins to create before next lecture
03:45
Day-142: Prompt Engineering
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Introduction to prompt Engineering
24:36
Types of Prompt Enginnering
08:05
Prompt Designing Strategies to write best prompt
11:47
Generate Prompts from LLMs
08:09
LLMs and prompt engineering live demo
44:02
Day-143: Huggingface
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Use pre trained models from Huggingface
33:08
Day-144: LangChain and APIs
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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)
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Introduction and installation
16:01
My first app in streamlit
20:29
streamlit cheat sheet
01:06
Day-146: Streamlit for webapp development and deployment (Part-2)
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Streamlit Data Science application
26:14
Day-147: Streamlit for webapp development and deployment (Part-3)
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Streamlit webapp without coding but prompting
45:09
Day-148: Streamlit for webapp development and deployment (Part-4)
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Creating a word Cloud app on streamlit python
17:00
requirements.txt file for streamlit apps
03:42
Day-149: Streamlit for webapp development and deployment (Part-5)
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Streamlit app deployment on free servers
16:28
Day-150: Streamlit for webapp development and deployment (Part-6)
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Create an app only in 14 lines of codes
06:30
Day-151: Streamlit for webapp development and deployment (Part-7)
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Manage Secrets in streamlit deployment with python
13:48
Day-152: Streamlit for webapp development and deployment (Part-8)
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ask the pdf and other docx application
22:33
Day-153: Streamlit for webapp development and deployment (Part-9)
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Streamlit project read pdfs in a folder
27:47
Day-154: Streamlit for webapp development and deployment (Part-10)
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Stock Market Analysis and Prediction app
18:59
Day-155: Streamlit for webapp development and deployment (Part-11)
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Dall-e-3 Image generation app in python
12:45
Day-156: Streamlit for webapp development and deployment (Part-12)
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Speech to text Streamlit Application development
17:44
Day-157: Streamlit for webapp development and deployment (Part-13)
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Text to speech Streamlit application
32:15
How to Earn using Data Science and AI skills
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How to earn online using Data Science and AI
51:04
Day-158: Flask for web app development (Part-1)
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is FLASK better than streamlit?
09:07
Installation and basics of web app development in flask
24:12
Day-159: Flask for web app development (Part-2)
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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)
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End to End machine Learning project in flask | Tip prediction
07:53
Day-162: Flask for web app development (Part-5)
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Diabetes prediction app development
24:51
Day-163: Flask for web app development (Part-6)
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Text to speech web app development
15:18
Day-164: Flask for web app development (Part-7)
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WordCloud app development
05:55
Speech to text app using openAI API
09:43
Day-165: Flask for web app deployment (Part-8)
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Web application deployment on Amazon AWS EC2 Instance
53:06
Day-166: FastAPI (Part-1)
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Intro to FastAPI | is FastAPI really fast?
07:51
Installing FastAPI using conda
12:32
Day-167: FastAPI (Part-2)
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Book Store app in FastAPI in python
10:51
Day-168: FastAPI (Part-3)
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FastAPI basics and running server
07:26
Day-169: FastAPI (Part-4)
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Swagger docs in FastAPI
04:34
Day-170: FastAPI (Part-5)
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Path parameters in FastAPI
09:46
Day-171: FastAPI (Part-6)
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Users path parameters in FastAPI
05:53
Day-172: FastAPI (Part-7)
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Titanic app backend development with FastAPI
05:47
Day-173: FastAPI (Part-8)
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Add static plots to FastAPI app
05:10
Add interactive plots in FastAPI app using plotly
07:24
Day-174: FastAPI (Part-9)
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Different port numbers for FastAPI
07:18
Day-175: Tableau Complete Course (Part-1)
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What is Tableau?
03:24
Tableau Learning Resources
01:14
Tableau Public Installation Free
05:19
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
05:56
Day-176: Tableau Complete Course (Part-2)
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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)
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Your Own Project Visualization in Tableau Public
05:03
Saving your plots with Tableau Public
05:43
Day-178: Tableau Complete Course (Part-4)
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Area Charts in Tableau Public
06:00
Group charts in Tableau
02:25
Tableau Interactive Dashboard Development
11:37
Tableau Dashboard Deployment for Community
07:18
Day-179: Power BI (Part-1)
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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)
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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)
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Business Intelligence with Power BI
11:08
Seven Steps Workflow to use Power BI
04:40
Power BI interface and Features
12:19
Connect Data in Power BI
14:13
Day-182: Power BI (Part-4)
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Data Visualization and Plotting in Power BI
03:33
Column/Bar charts in Power BI
07:57
Create a bar plot like a professional
16:36
Pro tips to make a stunning plot in Power BI
14:54
Day-183: Power BI (Part-5)
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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
Day-184: Structured Querry Language (SQL) for Beginners
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What is SQL?
08:17
Basics of SQL
20:16
Install MySQL on Windows 10
14:12
Create a MySQL Database for Beginners
02:29
Create/modify tables in mySQL
19:22
DISTINCT function in mySQL
05:39
Constraints in mySQL
51:51
Auto increment in mySQL
05:56
mySQL workbench first look and interface
20:08
Import mySQL database in mySQL workbench
12:14
Pandas processing on mySQL database
05:08
SELECT clause in mySQL
12:25
Data Manupulation in mySQL
14:05
WHERE clause and comparison operators
10:48
AND OR NOT and IN
12:30
BETWEEN clause in mySQL
04:14
Six months of AI and Data Science Mentorship Program
    Join the conversation
    suhail Ahmed 7 days ago
    30,20,10,50,60,90,5,2 , let's find out the median. step 1. to arrange in ascending order 2,5,10,20,30,50,60,90 So the list of numbers are even. step2: taking average of middle two number which are 20,30 avg = 20+30/2=25. hence the median is 25
    Reply
    Imtiaz Ahmad 2 months ago
    25
    Reply
    shariq ismail 8 months ago
    25
    Reply
    Muneer Ahmed 9 months ago
    25
    Reply
    Muhammad Faizan 1 year ago
    0.5+1+1.5+2+2+3+3+4.5+5 median will be the middle value of our sorted array or list of numbers which is "2".
    Reply
    Muhammad Faizan 1 year ago
    2+5+10+20+30+50+60+90 --> number of values=8/2= 4. So we will take only the middle 2 values 20 and 30 having index 4,5. take an average of them 20+30/2=50/2 -->25 ... So, he Median is 25.
    Reply
    Syed Abdul Qadir Gilani 1 year ago
    4.5
    Reply
    Syed Abdul Qadir Gilani 1 year ago
    50
    Reply
    Rana Anjum Sharif 1 year ago
    0.5,1,1.5,2,2,3,3,4.5,5 so median is 2
    Reply
    Rana Anjum Sharif 1 year ago
    10,20,30,50,52,60,90 so the median is 50
    Reply
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