Udemy-DataScience&AIMasters2025-FromPythonToGenAI2025-8

Course content

Sections: 17

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Activities: 1

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Resources: 506

Announcements
1 Welcome Page

2 Lets install Python together
3 Google Colab whats that
4 Lets leverage chatGPT for help
5 Introduction to Python
6 Variables Keywords
7 Datatypes Operators
8 Lists
9 Tuples
10 Sets
11 Dictionary
12 Loops Iterations
13 Functions
14 Map Reduce Filter
15 File Handling
16 Control Structures
17 OOPs
18 NumPy
19 Pandas
20 Data Visualization
21 Matplotlib
22 Seaborn

2 Introduction
3 Types of Data Agenda
4 Descriptive Stats
5 Inferential Stats
6 Qualitative Data
7 Quantitative Data
8 Sampling Techniques Agenda
9 Population vs Sample
10 Why Sampling is important
11 Types of Sampling
12 Cluster Random Sampling
13 Probability Sampling
14 Non probability sampling
15 Population Sampling
16 Why n 1 and not n
17 Descriptive Analytics Agenda
18 Measures of Central Tendency
19 Mean
20 Median
21 Mode
22 Measures of Dispersion
23 Range
24 IQR
25 Variance Standard Deviation
26 Mean Deviation
27 Probability Agenda
28 Probability
29 Addition Rule
30 Independent Events
31 Cumulative Probability
32 Conditional Probability
33 Bayes Theorem 1
34 Bayes Theorem 2
35 Probability Distrubution Agenda
36 Uniform Distribution
37 Binomial Distribution
38 Poisson Distribution
39 Normal Distribution Part 1
40 Normal Distribution Part 2
41 Skewness
42 Kurtosis
43 Calculating Probability with Z score for Normal Distribution Part 1
44 Calculating Probability with Z score for Normal Distribution Part 2
45 Calculating Probability with Z score for Normal Distribution Part 3
46 Covariance Correlation Agenda
47 Covariance
48 Correlation
49 Covariance VS Correlation
50 Hypothesis Testing
51 Tailed Tests
52 p value
53 Types of Test
54 T Test
55 Z Test
56 Chi Square Test
57 ANOVA
58 Correlation Test Practicals

2 DADS Processes
3 What is EDA
4 Visualization
5 Agenda
6 Steps involved in EDA Data Sourcing
7 Steps involved in EDA Data Cleaning
8 Handle Missing Values Theory
9 Handle Missing Values Practicals
10 Feature Scaling Theory
11 Standardization Example
12 Normalization Example
13 Feature Scaling Practicals
14 Outlier Treatment Theory
15 Outlier Treatment Practicals
16 Invalid Data
17 Types of Data
18 Types of Analysis
19 Univariate Analysis
20 Bivariate Analysis
21 Multivariate Analysis
22 Numerical Analysis
23 Analysis Practicals
24 Derived Metrics
25 Feature Binning Theory
26 Feature Binning Practicals
27 Feature Encoding Theory
28 Feature Encoding Practicals
29 Case Study
30 Data Exploration
31 Data Cleaning
32 Univariate Analysis
33 Bivariate Analysis Part 1
34 Bivariate Analysis Part 2
35 EDA Report

2 Installation
3 Data Architecture File server vs client server
4 Introduction to SQL
5 Constraints in SQL
6 Table Basics DDLs
7 Table Basics DQLs
8 Table Basics DMLs
9 Joins
10 Data Import Export
11 Aggregation Functions
12 String functions
13 Date Time Functions
14 Regular Expressions
15 Nested Queries
16 Views
17 Stored Procedures
18 Windows Function
19 SQL Python connectivity

1 Agenda
2 Introduction to ML
3 Types of ML
4 Use Cases Part 1
5 Use Cases Part 2
6 Pre Requisites Features
7 Pre Requisites Train Test Split
8 Pre Requisites Feature Scaling
9 Pre Requisites Standardization Example
10 Pre Requisites Normalization Example
11 Pre Requisites Feature Encoding
12 Pre Requisites Feature Encoding Practicals
13 Regression Introduction to Regression Models
14 Regression Regression Metrics
15 Regression Regression Metrics Practicals
16 Regression Simple Linear Regression
17 Regression Multiple Linear Regression
18 Regression Linear Regression Practicals
19 Regression Multiple Linear Regression Practicals
20 Regression Polynomial Regression
21 Regression Polynomial Regression Practicals
22 Regression Bias Variance Tradeoff
23 Regression Ridge Regression
24 Regression Lasso Regression
25 Regression Lasso Ridge Regression Practicals
26 Classification Introduction to Classification
27 Classification Types of Classification
28 Classification Log Loss
29 Classification Confusion Matrix
30 Classification AUC ROC Curve
31 Classification Classification Report
32 Classification kNN Classifier
33 Classification kNN Classifier Example
34 Classification Practicals Part 1
35 Classification kNN Classifier Practicals
36 Classification Decision Tree
37 Classification Decision Tree Entropy based
38 Classification Decision Tree gini based
39 Classification Decision Tree Practicals
40 Classification Decision Tree Visualizing
41 Classification Random Forest Classifier
42 Classification Random Forest Classifier Practicals
43 Classification Naive Bayes Classifier
44 Classification SVM Classifier Part 1
45 Classification SVM Classifier Part 2
46 Classification Logistic Regression
47 Classification Practicals so far
48 Classification Issues in Classification Part 1
49 Classification Issues in Classification Part 2
50 Classification Project
51 Ensemble Introduction to Ensemble Learning
52 Ensemble Bagging
53 Ensemble Bagging vs Random Forest
54 Ensemble Bagging Practicals 1
55 Ensemble Bagging Practicals 2
56 Ensemble Boosting
57 Ensemble Ada Boost
58 Ensemble Gradient Boost
59 Ensemble CF vs LF
60 Ensemble Cross Entropy
61 Ensemble Xtreme Gradient Boosting XGB
62 Ensemble Project
63 Clustering Introduction to Clustering
64 Clustering kMeans Clustering
65 Clustering kMeans Clustering Practicals
66 Clustering Hierarchical Clustering
67 Clustering Hierarchical Clustering Practicals
68 Clustering Mean Shift Clustering
69 Feature Engineering Introduction
70 Feature Engineering RFE and SFS
71 Feature Engineering RFE Practicals
72 Feature Engineering Successive Feature Selection
73 Feature Engineering Chi Square
74 Feature Engineering Chi Square Practicals
75 Feature Engineering Principal Component Analysis
76 Feature Engineering Principal Component Analysis Practicals
77 Feature Engineering Linear Discriminant Analysis
78 Feature Engineering Linear Discriminant Analysis Practicals
79 Feature Engineering kPCA QDA
80 Feature Engineering kPCA QDA Practicals
81 Hyper Parameter Optimization Basics
82 Hyper Parameter Optimization Manual HPO
83 Hyper Parameter Optimization GridSearch vs RandomizedSearch
84 Hyper Parameter Optimization Manual HPO Practicals
85 Hyper Parameter Optimization RandomizedSearchCV Practicals
86 Hyper Parameter Optimization GridSearchCV Practicals

1 Introduction to TSA
2 Time Series vs Regression
3 Time Series Analysis
4 Anomaly Detection
5 Components of Time Series
6 Decomposition
7 Decomposition Practicals
8 AdditiveMultiplicative Decomposition
9 Stationarity
10 Testing TS Stationarity
11 Transformation
12 Introduction to Pre Processing
13 Handle Missing Value
14 Handle Missing Value Practicals
15 Outlier Treatment
16 3 Sigma Technique
17 Feature Scaling
18 Feature Scaling Standardization
19 Feature Scaling Normalization
20 Feature Scaling Practicals
21 Feature Encoding
22 Feature Encoding Practicals
23 Models Algorithms
24 Models ARIMA Part 1
25 Models ARIMA Part 2
26 Models AR Theory
27 Models MA Theory
28 Models ACFPACF Plots
29 Models Find pdq in ARIMA
30 Models ARIMA Practicals Part 1
31 Models ARIMA Practicals Part 2
32 Models ARIMA Final
33 Models Decomposition
34 Models ACFPACF
35 Models Best Transformation
36 Models Grid Search Part 1
37 Models Grid Search Part 2
38 Models Final Model Building
39 Models Facebook Prophet Part 1
40 Models Facebook Prophet Part 2
41 Models Facebook Prophet Part 3
42 Models Multi Variate Time Series Analysis
43 Models Facebook Prophet Uni vs Multi
44 Introduction to Metrics
45 Forecasting Evaluation Metrics
46 Mean Squarred Error
47 Root Mean Squarred Error
48 Mean Absolute Percentage Error
49 Project 1 Energy Forecasting Part 1
50 Project 1 Energy Forecasting Part 2
51 Project 1 Energy Forecasting Part 3
52 Project 2 Stock Market Prediction Part 1
53 Project 2 Stock Market Prediction Part 2
54 Project 2 Stock Market Prediction Part 3
55 Project 3 Demand Forecasting Part 1
56 Project 3 Demand Forecasting Part 2
57 Project 3 Demand Forecasting Part 3
58 Project 3 Demand Forecasting Part 4
59 Project 3 Demand Forecasting Part 5
60 Project 3 Demand Forecasting Part 6

2 Introduction to Deep Learning
3 Understanding Deep Learning
4 What is a Neuron
5 Activation Functions
6 Activation Function Step Function
7 Activation Function Linear Function
8 Activation Function Sigmoid Function
9 Activation Function TanH Function
10 Activation Function ReLu Function
11 Backpropagation Forward Pass
12 Gradient Descent
13 Artificial Neural Networks Intuition
14 Artificial Neural Networks Practicals
15 Artificial Neural Networks Hyper Parameter Optimization
16 Convolutional Neural Networks What is CNN
17 Convolutional Neural Networks Steps in CNN
18 Convolutional Neural Networks Architecture Explained
19 Convolutional Neural Networks Image Augmentation
20 Convolutional Neural Networks Batch size vs iterations vs epochs
21 Convolutional Neural Networks Practicals
22 Convolutional Neural Networks Model Summary Parameters
23 Convolutional Neural Networks Project X Ray detection
24 Recurrent Neural Networks Basics
25 Recurrent Neural Networks Types of RNN
26 Recurrent Neural Networks Vanishing Gradient Exploding Gradient Problem
27 Recurrent Neural Networks LSTMs
28 Recurrent Neural Networks LSTMs Practicals
29 Pre Trained Models
30 Pre Trained Models Practicals
31 Pre Trained Models VGG16
32 Pre Trained Models MobileNet
33 Transfer Learning
34 Project Pneumonia Detection from X Ray Images

2 Intro to NLP Introduction
3 Intro to NLP Introduction continued
4 Intro to NLP Key Challenges
5 Intro to NLP Linguistics
6 NLP Basics Case Folding
7 NLP Basics SCR
8 NLP Basics Handling Contractions
9 NLP Basics Tokenization
10 NLP Basics Stop Word Removal
11 NLP Basics nGrams
12 NLP Basics Vectorization
13 NLP Basics Word Embeddings
14 NLP Basics Bag of Words
15 NLP Basics Bag of Words Practicals
16 NLP Basics TF IDF
17 NLP Basics TF IDF Practicals
18 NLP Basics Part of Speech Tagging and Named Entity Recognition
19 NLP Basics NER Practicals
20 Word Embeddings Word2Vec Introduction
21 Word Embeddings Word2Vec Part 2
22 Word Embeddings Pre Trained Word2Vec
23 Word Embeddings Word2Vec Intuition
24 Word Embeddings Word2Vec Check X Features
25 Word Embeddings Word2Vec CBOW
26 Word Embeddings Word2Vec Skip Grams
27 Word Embeddings GloVe
28 Word Embeddings FastText
29 Word Embeddings Cosine Similarity
30 Neural Networks LSTMs Part 1
31 Neural Networks LSTMs Part 2 Architecture
32 Neural Networks LSTMs Part 3 Deep Dive
33 Neural Networks LSTMs Part 4 Pointwise Operations
34 Neural Networks LSTMs Part 5 forget gate
35 Neural Networks LSTMs Part 6 inpute gate
36 Neural Networks LSTMs Part 7 output gate
37 Neural Networks LSTMs Part 8 Practicals 1
38 Neural Networks LSTMs Part 9 Practicals 2
39 Neural Networks LSTMs Part 10 Practicals 3
40 Neural Networks GRU Part 1
41 Neural Networks GRU Part 2
42 Neural Networks GRU Part 3 reset gate
43 Neural Networks GRU Part 4 update gate
44 Neural Networks GRU Part 5 Practicals
45 Neural Networks Bi Directional LSTMs

2 Transformer Types
3 Introduction to Transformers
4 Self Attention
5 Encoder Architecture
6 Contextual Embeddings
7 Decoder Architecture
8 Introduction to BERT
9 Configurations of BERT
10 BERT Fine Tuning
11 BERT Pre Tuning Masked LM
12 BERT Input Embeddings
13 ARLM vs AELM
14 RoBERTa
15 DistilBERT
16 AlBERT
17 Introduction to GPT Decoder Only
18 GPT Architecture
19 GPT Masked Multi Head Attention
20 GPT Blocks
21 GPT Training
23 LLM Basics Context Window
24 LLM Basics Prompt
25 LLM Basics Prompt Engineering
26 LLM Basics Prompt Tuning
27 LLM Basics Prompt Structures
28 RAGs Introduction to RAG
29 RAGs What and Why
30 RAGs Use Cases
31 RAGs Paper Explanation
32 RAGs Architecture Explanation
33 RAGs Detailed Architecture Walkthrough
34 RAGs Practical Use Cases
35 LangChain
36 Introduction to Prompt Engineering
37 Types of Prompting
38 Few Shot Limitations
39 Chain of Thoughts Prompting
40 Vector Databases
41 Vector Database vs Vector Index
42 How Vector Databases works
43 Vector Database Practicals
44 LSH
45 Model Overview Ollama
46 Getting Started Ollama
47 Model Testing Ollama
48 Python Implementation Ollama
49 RAG Systems Ollama
50 RAG Systems Practicals Ollama
51 Model Overview LLM APIs
52 RAG Systems with xAI LLM APIs
53 RAG Systems with xAI Practicals LLM APIs

1 Introduction to Fine Tuning
2 RAGs vs Fine Tuning
3 When to use RAGs vs When to use Fine Tuned models
4 Fine Tuning
5 PEFT and Quantization
6 LoRA Phase I Low Rank
7 LoRA Phase II Adapters
8 LoRA Phase III Low Rank Adapters
9 qLoRA Quanitzed Low Rank Adapters

2 Introduction to RAG Evaluation
3 N Gram Metrics BLEU
4 N Gram Metrics ROUGE
5 N Gram Metrics METEOR
6 Intrinsic Metrics PPL Score
7 Intrinsic Metrics PPL Score Practicals
8 Model based Metrics BARTScore vs BERTScore
9 RAGAS Framework RAG Assessment
10 PRACTICALS Rag Evaluation by RAGAS
11 PRACTICALS Rag Evaluation by BLEU ROUGE
12 PRACTICALS Rag Evaluation by BERTScore BARTScore

2 Introduction
3 Agentic AI Examples
4 What are Tools
5 LangChain Built in tools
6 Custom Tools using Tool Decorator
7 Custom Tools using StructuredTool
8 Custom Tools using BaseTool
9 LangChain Toolkits
10 LangChain Tool Calling Part 1
11 LangChain Tool Calling Part 2
12 Tool Execution
13 Currency Conversion Tool Mini Project
14 Travel Planning Agent Part 1
15 Travel Planning Agent Part 2
16 Travel Planning Agent Part 3
17 AI Agent Code Explanation and Theory Part 1
18 AI Agent Code Explanation and Theory Part 2

1 Deployment Basics
2 Introduction to Flask
3 Flask Basic App
4 Model Building Breast Cancer Prediction
5 Flask App Breast Cancer Prediction
6 AWS
7 AWS Deployment Breast Cancer Prediction

1 Introduction to Data Engineering
2 What is ETL
3 ETL Tools
4 What is Data Warehouse
5 Benefits of Data Warehouse
6 Data Warehouse Structure
7 Why do we need Staging
8 What are Data Marts
9 Data Lake
10 Data lake vs Data Warehouse
11 Elements of Datalake

1 Project 1 ChatScholar EdTech Project
2 Project 2 Research RAG Chatbot
3 Project 3 Automated AI Claims Processing using Gen AI
4 Project 4 Multi PDF RAG Chatbot built on Web Scraped Data
5 Project 5 AI Career Coach Part 1
6 Project 5 AI Career Coach Part 2
7 Project 5 AI Career Coach Part 3
8 Project 6 Sustainability Chatbot using xAIs Grok 3 AI
9 Project 7 Text to SQL Chatbot Part 1
10 Project 7 Text to SQL Chatbot Part 2
11 Project 7 Text to SQL Chatbot Part 3
12 Project 7 Text to SQL Chatbot Part 4
13 Project 7 Text to SQL Chatbot Part 5
14 Project 7 Text to SQL Chatbot Part 6
15 Project 7 Text to SQL Chatbot Part 7

1 ML Interview Prep
2 ML Interview 1
3 ML Interview 2
4 ML Interview 3
5 ML Interview 4
6 ML Interview 5
7 ML Interview 6
8 ML Interview 7
9 ML Interview 8
10 ML Interview 9
11 ML Interview 10
12 DL Interview 1
13 DL Interview 2
14 DL Interview 3
15 DL Interview 4
16 DL Interview 5
17 DL Interview 6
18 DL Interview 7
19 DL Interview 8
20 DL Interview 9
21 DL Interview 10
22 Gen AI Interview 1
23 Gen AI Interview 2
24 Gen AI Interview 3
25 Gen AI Interview 4
26 Gen AI Interview 5
27 Gen AI Interview 6
28 Gen AI Interview 7
29 Gen AI Interview 8
30 Gen AI Interview 9
31 Gen AI Interview 10

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Udemy - Data Science & AI Masters 2025 - From Python To Gen AI 2025-8

Course modified date: 11 Aug 2025

Udemy - Data Science & AI Masters 2025 - From Python To Gen AI 2025-8

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Udemy - Data Science & AI Masters 2025 - From Python To Gen AI 2025-8

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Udemy - Data Science & AI Masters 2025 - From Python To Gen AI 2025-8

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