₹9,999.00 ₹5,999.00

Data Science/AI Masters

Become a Data Scientist/AI Engineer in just 3 months

The most intense Data Analytics Course of 2024 with detailed teaching.
  • English
  • Satyajit Pattnaik
  •   Course
  • 5.0  
Description

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]

Course Content
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
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
Addition Rule
Independent Events
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
Correlation
p value
T Test
Tailed Tests
Types of Test
Z Test
Chi Square
ANOVA
Installation
File Server vs Client Server
Introduction to SQL
Constraints in SQL
Table Basic DDLs
Table Basics - DQLs
Table Basics - DMLs
Joins
Data Import Export
Aggregation
String Functions
Date Time Functions
Regular Expressions
Nested Queries
Views
Stored Procedures
Windows Function
SQL Python Connectivity
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
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
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
Before we move on
LSTM
LSTM Code
Pre trained RAW
Pre trained Code
VGG16
MobileNet
Transfer Learning
Final Project + Streamlit
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 - Vectorization
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
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
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 - Tokens
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
Vector database Practicals
LSH
Deployment
Intro To Flask
Flask Basic App
Model Building Breast Cancer
Flask App Breast Cancer
AWS
AWS Deployment
Course Details
Course Details
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
Data Lake vs Data Warehouse
Elements of Datalake
House Price Prediction (Advanced)
Appliance Energy Prediction
Autralia Weather Prediction
Banking Term Deposit
Big Mart Sales Prediction
Bike Sharing Demand Prediction
Car Price Prediction
Cirrhosis Prediction
Credit Card Default Prediction
GS Rice Files
Heart Failure Prediction
Hospital Mortality
Income Data Classification
Loan Prediction
Migraine Classification
Policy Recommendation Systems
Prediction of Used Car Prices
Real Estate Analysis
Spanish Wine Quality
Stroke Prediction
Supply Chain Management
Water Quality Classification
ChatScholar (EdTech Project)
Research RAG Chat
Automated AI Claims Processing using Gen AI
Multi PDF Rag Chatbot built on Web Scraping
AI Career Coach (Part 1)
AI Career Coach (Part 2)
AI Career Coach (Deployment)
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
Interview Preparation
Assignment Details
Ratings & Reviews

5.0 

Venkatesh M

Well structured and explained.

Dec, 27 2024

5.0 

Satyam Subhaprakash

Dec, 30 2024

5.0 

Rajesh

Best faculty for AI ML, able to complete my M.Tech project with his content.

Feb, 28 2025

5.0 

Sudhir Chaudhary

Brilliant course

Feb, 28 2025

This course includes

  150 Hours of on-demand video
  Full lifetime access
  Certificate of completion

Instructor
Avatar
Satyajit Pattnaik

Lead Data Consultant, PALO IT