Udemy - Data Science & AI Masters 2025

Course content

Sections: 13

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Activities: 0

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Resources: 424

1 Welcome Page
2 - Course Resources
2 - Introduction to the Course

1 - Welcome to the module
1 Introduction to Python
2 Variables Keywords
3 Datatypes Operators
4 Lists
5 Tuples
6 Sets
7 Dictionary
8 Loops Iterations
9 Functions
10 Map Reduce Filter
11 File Handling
12 Control Structures
13 OOPs
14 NumPy
15 Pandas
16 Data Visualization
17 Matplotlib
18 Seaborn

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

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

1 - Course Contents
1 Installation
2 Data Architecture File server vs client server
3 Introduction to SQL
4 Constraints in SQL
5 Table Basics DDLs
6 Table Basics DQLs
7 Table Basics DMLs
8 Joins
9 Data Import Export
10 Aggregation Functions
11 String functions
12 Date Time Functions
13 Regular Expressions
14 Nested Queries
15 Views
16 Stored Procedures
17 Windows Function
18 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
51 Project 1 Energy Forecasting Part 3
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
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

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

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

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

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 ChatScholar EdTech Project
2 Research RAG Chatbot
3 Automated AI Claims Processing using Gen AI
4 Multi PDF RAG Chatbot built on Web Scraped Data
5 AI Career Coach Part 1
6 AI Career Coach Part 2
7 AI Career Coach Part 3

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Udemy - Data Science & AI Masters 2025 - From Python To Gen AI 2025-1

Course modified date: 6 Feb 2025

Enrolled students: 1

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Udemy - Data Science & AI Masters 2025 - From Python To Gen AI 2025-1

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