ZepAnalyticsZep Analytics
Get Started
Back to Courses
CourseData Science & AIIntermediate

Data Science/AI Masters

Become a Data Scientist/AI Engineer in just 3 months

0 enrolledEnglish
Z

Zep Admin

Data Science/AI Masters

₹5,999

  • 449 lessons
  • 0 students enrolled
  • English
  • Intermediate

About this course

The outburst of data is transforming businesses. Companies - big or small - are now expecting their business decisions to be based on data-led insight.

  • Data specialists have a tremendous impact on business strategies and marketing tactics.
  • The demand for data specialists is on the rise while the supply remains low, thus creating great job opportunities for individuals within this field.
  • Today, it is almost impossible to find any brand that does not have social media presence; soon, every company will need data analytics professionals.
  • This makes it a wise career move that has a future in business.

Job Roles after the course

This course will help you to step forward in Data Analytics and choose following roles

  • Data Scientist
  • AI Engineer
  • Gen AI Engineer
  • Data Analyst
  • Business Analyst
  • BI Analyst
  • BI Developer
  • Power BI Developer
  • Tableau Developer
  • and many more..…

Syllabus: Click Here

Module 1: Introduction to the Course

Module 2: Python for Data Science

Module 3: Statistics for Data Science

Module 4: SQL

Module 5: Machine Learning 

Module 6: Time Series Analysis

Module 6: Deep Learning

Module 7: NLP

Module 8: Transformers & Gen AI

Module 9: Deployment

Module 10: Power BI [Add-on]

Module 11: Tableau [Add-on]

Module 12: Data Engineering Basics [Add-on]

Projects: 20+ Industry level projects

FYI: This is a SELF PACED program that comes with 24/7 chat support, resume preparation guidance and interview prep materials

Any queries, reach out to Satyajit Pattnaik at +91 8237040802 [WhatsApp only]

Curriculum

14 sections · 449 lessons

  • Intro to NLP - NLP Intro
  • Intro to NLP - Intro Part 2
  • Intro to NLP - NLP Key Challenges
  • Intro to NLP - Linguistics
  • NLP Basics - Case Folding
  • NLP Basics - SCR
  • NLP Basics - Handling Contractions
  • NLP Basics - Tokenization
  • NLP Basics - Stop Words Removal
  • NLP Basics - nGrams
  • NLP Basics - Word Embeddings
  • NLP Basics - BoW
  • NLP Basics - BoW Practicals
  • NLP Basics - TFIDF
  • NLP Basics - TFIDF Practicals
  • NLP Basics PoS & NER
  • NLP Basics - NER Practicals
  • NLP Basics - Vectorization
  • Word Embeddings - Word2Vec Intro
  • Word Embeddings - Word2Vec Part 2
  • Word Embeddings - Pretrained Word2Vec
  • Word Embeddings - Word2Vec INTUITION
  • Word Embeddings - Word2Vec 50Features
  • Word Embeddings - Word2Vec CBOW
  • Word Embeddings - Word2Vec SkipGrams
  • Word Embeddings - GloVe
  • Word Embeddings - fastText
  • Word Embeddings - Cosine Similarity
  • Neural Networks - LSTM Part 1
  • Neural Networks - LSTM Part 2 Architecture
  • Neural Networks - LSTM Part 3 Deep Dive Architecture
  • Neural Networks - LSTM Part 4 Pointwise Operations
  • Neural Networks - LSTM Part 5 forgetGate
  • Neural Networks - LSTM Part 6 inputGate
  • Neural Networks - LSTM Part 7 outputGate
  • Neural Networks - LSTM Part 8 Practicals
  • Neural Networks - LSTM Part 9 Practicals
  • Neural Networks - LSTM Part 10 Practicals
  • Neural Networks - GRU Part 1
  • Neural Networks - GRU Part 2
  • Neural Networks - GRU Part 3 resetGate
  • Neural Networks - GRU Part 4 updateGate
  • Neural Networks - GRU Part 5 Practicals
  • Neural Networks - Bi Directional RNN
  • GloVe vs FastText (Practicals #1)
  • GloVe vs FastText (Practicals #2)

  • Introduction to Fine Tuning
  • RAGs vs Fine Tuning
  • When to use RAGs
  • Fine Tuning
  • PEFT and Quantization
  • LoRA (Phase I - Low Rank)
  • LoRA (Phase II - Adapters)
  • LoRA (Phase III - Low Rank + Adapters)
  • qLoRA (Quantized LoRA)
  • Introduction to RAG Evaluation
  • RAG Evaluation (BLEU)
  • RAG Evaluation (ROUGE)
  • RAG Evaluation (METEOR)
  • RAG Evaluation (PPL Score)
  • RAG Evaluation (BERTScore, BARTScore)
  • RAG Evaluation (PPL Score) Practicals
  • RAGAS Framework
  • RAG Evaluation (Practicals #1)
  • RAG Evaluation (Practicals #2)
  • RAG Evaluation (Practicals #3)

  • Welcome to the course!!
  • Let's install Python together
  • Google Colab, what's that?
  • Let's leverage chatGPT for help!!

  • Transformers
  • Self Attention
  • Encoder Architecture
  • Contextual Embeddings
  • Decoder Architecture
  • Introduction to BERT
  • Configurations of BERT
  • BERT - Fine Tuning
  • BERT - Pre Tuning (Masked LM)
  • BERT - Input Embeddings
  • ARLM vs AELM
  • RoBERTa
  • distilBERT
  • AlBERT
  • Introduction to GPT (Decoder-Only Architecture)
  • GPT Architecture
  • GPT Masked Multi Head Attention
  • GPT Blocks
  • GPT Training
  • LLM Basics - Context Window
  • LLM Basics - Prompt
  • LLM Basics - Prompt Engineering
  • LLM Basics - Prompt Tuning
  • LLM Basics - Prompt Structures
  • RAGs - Introduction
  • RAGs - What and Why?
  • RAGs - Use Cases
  • RAGs - Paper Explanation
  • RAGs - Architecture Explanation
  • RAGs - Detailed Architecture Walkthrough
  • RAGs - Practical Use Case
  • Langchain
  • Prompt Engineering (Intro)
  • Types of Prompting
  • Few Shot Limitations
  • Chain of Thoughts Prompting
  • Vector Databases
  • Vector database vs Vector Index
  • How Vector databases work
  • LSH
  • Vector database Practicals
  • Model Overview: Ollama
  • Getting Started with Ollama
  • Testing Models with Ollama
  • Python Implementation: Ollama
  • RAG Systems with Ollama
  • RAG Systems with Ollama (Practicals)
  • Model Overview: LLM APIs
  • RAG Systems with xAI
  • RAG Systems with xAI (Practicals)

  • Automated AI Claims Processing using Gen AI
  • Research RAG Chat
  • ChatScholar (EdTech Project)
  • Multi PDF Rag Chatbot built on Web Scraping
  • AI Career Coach (Part 1)
  • AI Career Coach (Part 2)
  • AI Career Coach (Deployment)
  • Sustainability Chatbot (GROK API)
  • Sustainability Chatbot (Ollama)

  • ML Interview #1
  • ML Interview #2
  • ML Interview #3
  • ML Interview #4
  • ML Interview #5
  • ML Interview #6
  • ML Interview #7
  • ML Interview #8
  • ML Interview #9
  • ML Interview #10
  • DL Interview #1
  • DL Interview #2
  • DL Interview #3
  • DL Interview #4
  • DL Interview #5
  • DL Interview #6
  • DL Interview #7
  • DL Interview #8
  • DL Interview #9
  • DL Interview #10
  • Gen AI Interview #1
  • Gen AI Interview #2
  • Gen AI Interview #3
  • Gen AI Interview #4
  • Gen AI Interview #5
  • Gen AI Interview #6
  • Gen AI Interview #7
  • Gen AI Interview #8
  • Gen AI Interview #9
  • Gen AI Interview #10

  • INTRO - Agenda
  • INTRO - Introduction
  • INTRO - Types of ML
  • INTRO - Use Cases Part 1
  • INTRO - Use Cases Part 2
  • PRE REQUISITE - Features
  • PRE REQUISITE - Train-Test-Split
  • PRE REQUISITE - Feature Scaling
  • PRE REQUISITE - Standardization
  • PRE REQUISITE - Normalization
  • PRE REQUISITE - Feature Encoding
  • PRE REQUISITE - Feature Encoding Practicals
  • REGRESSION - Regression
  • REGRESSION - Regression Metrics (PRACTICALS)
  • REGRESSION - SLR
  • REGRESSION - MLR
  • REGRESSION LR Codes
  • REGRESSION - MLR Example
  • REGRESSION - Polynomial Regression
  • REGRESSION - Polynomial Regression Practicals
  • REGRESSION - Bias Variance Tradeoff
  • REGRESSION - Ridge Regression
  • REGRESSION - Lasso Regression
  • REGRESSION - Ridge and Lasso Regression Practicals
  • CLASSIFICATION - Classification
  • CLASSIFICATION - Types Of Classification
  • CLASSIFICATION - Log Loss
  • CLASSIFICATION - Confusion Matrix
  • CLASSIFICATION - AOC RUC
  • CLASSIFICATION - Classification Report
  • CLASSIFICATION - KNN
  • CLASSIFICATION - KNN Excel Example
  • CLASSIFICATION - Classification Practicals Part 1
  • CLASSIFICATION - KNN Code
  • CLASSIFICATION - Decision Tree
  • CLASSIFICATION - DT Example Entropy
  • CLASSIFICATION - DT Gini Index
  • CLASSIFICATION - DT Code
  • CLASSIFICATION - Visualizing DT
  • CLASSIFICATION - Random Forest
  • CLASSIFICATION - RF Code
  • CLASSIFICATION - Naive Bayes
  • CLASSIFICATION - SVMs Part 1
  • CLASSIFICATION - SVMs Part 2
  • CLASSIFICATION - Logistic Regression
  • CLASSIFICATION - Practicals So Far
  • CLASSIFICATION - Issues in Classification Part 1
  • CLASSIFICATION - Issues in Classification Part 2
  • CLASSIFICATION - Practicals
  • ENSEMBLE - Ensemble
  • ENSEMBLE - Bagging
  • ENSEMBLE - Bagging vs RF
  • ENSEMBLE - Bagging Practicals
  • ENSEMBLE - Bagging Reg Practicals
  • ENSEMBLE - Boosting
  • ENSEMBLE - Adaboost
  • ENSEMBLE - Gradient Boosting
  • ENSEMBLE - CF vs LF
  • ENSEMBLE - Cross Entropy
  • ENSEMBLE - XGBoost
  • ENSEMBLE - Practicals
  • CLUSTERING - Clustering
  • CLUSTERING - HC Practicals
  • CLUSTERING - Hierarchial Clustering
  • CLUSTERING - K Means Clustering
  • CLUSTERING - K means Clustering Practicals
  • CLUSTERING - Mean Shift Theory and Practicals
  • FEATURE ENGINEERING - Dimensionality Reduction
  • FEATURE ENGINEERING - RFE SFS
  • FEATURE ENGINEERING - RFE Practicals
  • FEATURE ENGINEERING - SFS
  • FEATURE ENGINEERING - CHI SQUARE TEST
  • FEATURE ENGINEERING - CHI SQUARE PRACTICALS
  • FEATURE ENGINEERING - PCA THEORY
  • FEATURE ENGINEERING - PCA PRACTICALS
  • FEATURE ENGINEERING - LDA THEORY
  • FEATURE ENGINEERING - LDA PRACTICALS
  • FEATURE ENGINEERING - kPCA & QDA
  • FEATURE ENGINEERING - kPCA & QDA PRACTICALS
  • HPO - HYPER PARAMETER OPTIMIZATION BASICS
  • HPO - MANUAL HPO
  • HPO - GRID VS RANDOM
  • HPO - MANUAL PRACTICALS
  • HPO - RSCV PRACTICALS
  • HPO - GSCV PRACTICALS
  • REGRESSION - Regression Metrics

  • Introduction to TSA
  • Time Series vs Regression
  • Time Series Analysis
  • Anomaly Detection
  • Components of Time Series
  • Decomposition
  • Decomposition (Implementation)
  • Additive/Multiplicative Decomposition
  • Stationarity
  • Testing TS Stationarity
  • Transformation
  • Introduction to Pre-Processing
  • Handle Missing Value
  • Handle Missing Value (Code)
  • Outlier Treatment
  • Sigma Technique
  • Feature Scaling
  • Standardization
  • Normalization
  • Feature Scaling (Code)
  • Feature Encoding
  • Feature Encoding (Code)
  • Models - Algorithms
  • Models - ARIMA [part 1]
  • Models - ARIMA [part 2]
  • Models - AR Theory
  • Models - MA Theory
  • Models - ACF/PACF
  • Models - Find PDQ
  • Models - ARIMA (Code) [part 1]
  • Models - ARIMA (Code) [part 2]
  • Models - ARIMA (Final Code)
  • Models - Decomposition
  • Models - ACF/PACF
  • Models - Best Transformation
  • Models - Grid Search [part 1]
  • Models - Grid Search [part 2]
  • Models - Final Model
  • Models - FBProphet [part 1]
  • Models - FBProphet [part 2]
  • Models - FBProphet [part 3]
  • Models - Multi Variate TS Analysis
  • Models - FBProphet [Uni/Multi]
  • Introduction to Metrics
  • Forecasting Evaluation Metrics
  • Mean Squared Error
  • Root Mean Squared Error
  • Mean Absolute Percentage Error
  • Project 1 - Energy Forecasting #1
  • Project 1 - Energy Forecasting #2
  • Project 1 - Energy Forecasting #3
  • Project 2 - Stock Market Prediction #1
  • Project 2 - Stock Market Prediction #2
  • Project 2 - Stock Market Prediction #3
  • Project 2 - Demand Forecasting #1
  • Project 2 - Demand Forecasting #2
  • Project 2 - Demand Forecasting #3
  • Project 2 - Demand Forecasting #4
  • Project 2 - Demand Forecasting #5
  • Project 2 - Demand Forecasting #6

  • Introduction to DL
  • Understanding Deep Learning
  • What is a Neuron
  • Activation Funations
  • Step Function
  • Linear Function
  • Sigmoid Function
  • TanH Function
  • ReLU Function
  • Backpropagation and Forward Pass
  • Gradient Descent
  • ANN Intuition
  • ANN Code
  • ANN HPO
  • CNN Steps in CNN
  • CNN What is CNN
  • CNN CNN Architecture Explained
  • CNN Image Augmentation
  • CNN Batch Size vs Iterations vs Epochs
  • CNN Code Implementation of CNN
  • CNN Model Summary & Parameters
  • CNN Hands on XRAY
  • RNN Basics
  • Types of RNN
  • RNN VG+EG
  • LSTM
  • LSTM Code
  • Pre trained RAW
  • Pre trained Code
  • VGG16
  • MobileNet
  • Transfer Learning
  • Final Project + Streamlit

  • Installation
  • File Server vs Client Server
  • Introduction to SQL
  • Constraints in SQL
  • Table Basic DDLs
  • Table Basics - DQLs
  • Table Basics - DMLs
  • Joins
  • Aggregation
  • Data Import Export
  • String Functions
  • Date Time Functions
  • Regular Expressions
  • Nested Queries
  • Stored Procedures
  • Windows Function
  • Views
  • SQL Python Connectivity

  • Elements of Datalake
  • Introduction
  • What is ETL
  • ETL Tools
  • What is a Data Warehouse
  • Table Basics - DDLs
  • Data Warehouse Structure
  • Why do we need staging
  • What are Data Marts
  • Data Lake vs Data Warehouse
  • Data Lake

  • Deployment
  • Flask Basic App
  • Model Building Breast Cancer
  • Flask App Breast Cancer
  • AWS
  • AWS Deployment
  • Intro To Flask

  • Intro to Stats
  • Agenda
  • Descriptive Statistics
  • Inferential Statistics
  • Qualitative Data
  • Quantitive
  • Agenda
  • Pop vs Sample
  • Why Sampling is Important
  • Types of Sampling
  • Cluster Random Sampling
  • Probability Sampling
  • Non Probability Sampling
  • Population Sampling
  • Why n1 and not n
  • Agenda NEW
  • Agenda
  • Measures of Central Tendency
  • Mean
  • Median
  • Mode
  • Measures of Dispersion
  • Range
  • IQR
  • Variance and Standard Deviation
  • Mean Deviation
  • Agenda
  • Probability
  • Independent Events
  • Addition Rule
  • Cumulative Probability
  • Conditioanal Probability
  • Bayes Theorem 1
  • Bayes Theorem 2
  • Agenda NEW
  • Agenda
  • Uniform Distribution
  • Binomial Distribution
  • Poisson Distribution
  • Normal Distribution 1
  • Normal Distribution 2
  • Skewness
  • Kurtosis
  • Calculating Probability with Z Score [part 1]
  • Calculating Probability with Z Score [part 2]
  • Calculating Probability with Z Score [part 3]
  • Agenda
  • Covariance
  • Correlation
  • Correlation vs Covariance
  • ANOVA
  • Correlation
  • p value
  • T Test
  • Tailed Tests
  • Types of Test
  • Z Test
  • Chi Square

  • Introduction to Python
  • Variables Keywords
  • Datatypes Operators
  • Lists
  • Tuples
  • Dictionary
  • Sets
  • Loops Iteration
  • Functions
  • File Handling
  • Control Structures
  • OOPs
  • NumPy
  • Pandas
  • Data Vizualization
  • Matplotlib
  • Seaborn
Data Science/AI Masters | ZepAnalytics