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
    Muhammad Shoaib 2 months ago
    nice
    Reply
    Muhmmad Bilal Ramzan 5 months ago
    good sir g
    Reply
    Zain Sultan 6 months ago
    good
    Reply
    Muhammad Faizan 8 months ago
    I learned about the difference between Feature Scaling and Normalization. Scaling: Scale the data into a range without changing its distribution. Normalization: Scaling the data points individually and also changing the distribution.
    Reply
    Muhammad Rameez 9 months ago
    Done
    Reply
    Rana Anjum Sharif 9 months ago
    Done
    Reply
    Sibtain Ali 1 year ago
    I learned in this video Scaling and Normalization.
    Reply
    tayyab Ali 1 year ago
    I learned in this lecture about Scaling and Normalization.
    Reply
    Javed Ali 1 year ago
    Feature scaling (standardization) transforms features to have a zero mean and unit variance. It is useful when the scale of the features needs to be consistent across all variables.Normalization scales features to a specific range, typically between 0 and 1. It is useful when the range of feature values needs to be controlled or when specific algorithms require input in a certain range. The function of STANDARDIZATION is to bring the data to scale, and the function of NORMALIZATION is to have an impact on the distribution of the data.
    Reply
    Shahid Umar 1 year ago
    Scaling:- To change the range of data without changing the shape of the data. Normalization:- Transform data into scale and change its distribution.
    Reply
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