Course Content
AI, ML, Gen AI, Prompt Engineering & LLMs End-to-End Real-Time Projects & Portfolio Building
0/12
End-to-End Data Science Project with Amazon S3, Snow Flake, Spark & Snow Park
End-to-End Machine Learning Project Using Spark & Microsoft Fabric
AGILE CRISP: End-to-End Machine Learning Project Pipeline
Agile DataOps for Real-Time Credit Risk Scoring and Analysis
MLOps Pipeline: End-to-End Data Science Lifecycle using MLOps
Streaming Analytics: End-to-End NLP Project
End-to-End Computer Vision Project with Deployment
Image Based Generative AI Project using Diffusion Models
Prompt Engineering LLM Project using Zero Shot, Single Shot and Few Shot Learning
Building ChatBot Using LangChain, Amazon BedRock & StreamLit
Building Company Policy LLMs using RAG Architecture, Amazon BedRock & Streamlit
Fine-Tuning LLM using Hugging Face
Mastering Python[Zero to Hero]
0/30
Introduction to Python
01:02:54
Why Python, Value, Variable, Function, Library[Roadmap on Python]
01:04:10
IDE in Python, Different Data Types
01:01:53
List, Tuple, Set & Dictionary Overview
57:16
Different List Methods
55:48
Different Tuple Methods
01:04:52
Set & Frozenset
01:00:50
Dictionary & String Manipulations
50:56
Overview on Loops, If Statements, UDFs, Escape Sequences, Lambda
01:03:15
Types of Operators, Conditional Statements
23:42
While Loop, List Comprehension, Break, Continue, Arguments
01:01:18
Functions, Escape Sequences, Lambda Functions
52:23
Hackathon-1
54:32
Introduction to OOPS
16:16
Instance Variable, Class Variable, Class Method
56:53
Association vs Composition & Aggregation
50:02
Oops Concept
36:29
Encapsulation, Inheritance
01:07:07
Polymorphism, Method OverLoading, Method Overriding
53:59
Introduction to Pandas
01:05:22
Data Analysis using Pandas
01:34:29
Introduction to Numpy
57:26
Different Numpy Commands
01:03:00
Introduction to Data Visualisation
51:41
Data Visualisation using Matplotlib
01:00:20
Data Visualization using Seaborn
59:34
Data Visualization using Plotly
41:44
Why Data Cleaning?
47:05
Data Cleaning with Sklearn & Pandas
49:57
Regular Expression Basics
54:01
Advanced Data Structures and Algorithms
0/23
Introduction to Power Bi
Non-Primitive Data Structures
Non-Linear Data Structures
What is an Algorithm
Theory & Code Implementation of Linked List
Stacks & Queues Assignment
Coding Stack Data Structure
Coding Queue Data Structure
Tree Data Structures
Types of Tree Data Structure
Tree Traversal
BFS Traversal
Bubble Sort Theory
Bubble Sort Code Implementation
Selection Sort Theory
Selection Sort Code Implementation
Insertion Sort Theory
Insertion Sort Code Implementation
Merge Sort Theory
Merge Sort Code Implementation
Quick, Merge Sort Performance
Quick Sort Theory
Linear Search & Bisection Search
Applied Statistics
0/40
What is Data? Various Types of Data
Levels of Measurement
Categorical Variables & Visualisation Techniques
Continuous Variables and Visualisations
Measures of Central Tendency
Measuring Skewness
Measures of Dispersion
Covariance & Correlation Coefficient
Box & Whisker Plot
Outlier Identification
Treating Outliers
Sampling Techniques
Statistical Analysis Consideration
Types of Analytics
Sample vs Population
Introduction to Inferential Statistics
Law of Large Numbers
Central Limit Theorem
Probability Distributions
Conditional Probability
Continuous Probability Distribution & Discrete Probability Distribution
PMF, CDF, PDF, PPF in Probability
Binomial Distribution
Poisson Distribution
Normal Distribution
Exponential Distribution
Laplace Distribution
Log-Normal Distribution
Standard Error
Confidence Interval Calculations
Z-Table for Confidence Interval
T-Table for Confidence Interval
Hypothesis Testing
Null & Alternate Hypothesis
Type-1 Error & Type-2 Error
2-Sample T Test
ANOVA Test
p-Value Calculations
Chi-Square Test
Project Demonstration using all the Statistical Techniques
Statistics with Real-Time Project Demonstration on EDA
0/4
Basics of Statistics
01:41:48
Central Limit Theorem, Normal Distribution, Skewness
01:29:44
Real-Time Project Demonstration on EDA with Real-World Project
01:01:55
End-to-End Statistics using Excel
03:31:41
Real-Time Project Demonstration on Probability Distribution, Hypothesis Testing
0/8
Probability Distribution, Random Variable, Binomial, Poisson, Exponential
Normal Distribution
Real-Time Project Demonstration on Probability
Hypothesis Testing Part-1
Hypothesis Testing Part-2
How to Calculate P-Value? Coding Hypothesis Testing
How to solve PDF, CDF, PMF?
Chebyshevs, Log, Power Law, Q-Q, CLT
Unsupervised Learning with Real-Time Projects
0/9
Supervised vs Unsupervised Learning
21:51
Fundamentals of Machine Learning Part-1
32:19
Fundamentals of Machine Learning Part-2
48:41
Insights from Train & Test Accuracy
21:23
Associate Rules Part-1
23:02
Associate Rules Part-2
02:27
Problem Identification & Approach Designing
01:02:13
Associate Rules Scripting
57:15
Why to do Dimensionality Reduction? Project Implementation using PCA & T-SNE
01:03:36
Feature engineering Techniques
0/28
Feature Scaling Techniques
Normalisation
Standardisation
Min Max Scaler
Robust Scaler
Label Encoding Technique
One-Hot Encoding Technique
Dummies Technique
Ordinal Encoding Technique
Imputation Technique
Feature Selection Techniques
Filter Method: Feature Importance
Filter Method: Fishers Score Technique
Filter Method: Chi-Square Technique
Filter Method: Variance Threshold
Filter Method: Correlation Coefficient
Filter Method: Mutual Information
Wrapper Method: Forward Selection Technique
Wrapper Method: Backward Selection Technique
Wrapper Method: Recursive Feature Elimination Technique
Wrapper Method: Exhaustive Feature Selection Technique
Wrapper Method: Sequential Feature Selection Technique
Embedded Methods: Regularisation
Embedded Method: Elastic Net Regression
Embedded Method: Decision Tree
Outlier Treatment
Over Sampling Technique
Under Sampling Techniques
Supervised Learning Algorithms
0/34
How to Prepare Machine Learning Math for Interviews
06:52
Why to use Linear Regression?
06:13
What is Linear Regression
04:42
Case Study on Linear Regression
06:21
Math Behind Linear Regression Part-1
07:03
Math Behind Linear Regression Part-2
06:25
Math behind OLS Technique
04:32
Assumptions of Linear Regression
23:08
Evaluation Metrics for Regression Model
15:50
Accuracy Improving Techniques
14:29
Coding Linear Regression Model
Regularisation Techniques
08:56
Why Logistic Regression
04:27
Math Behind Logistic Regression
17:44
Evaluation Metrics Behind Classification Algorithms
17:10
ROC & AUC Curve
10:20
Coding Logistic Regression Model
Introduction to Decision Tree
06:12
Intuition Behind Decision Tree
07:32
Math Behind Decision Tree
16:03
Math Behind Decision Tree using GINI
04:30
Drawbacks of Decision Tree
05:07
Random Forest and Gradient Boosting
13:08
Coding Decision Tree, Random Forest, GB
SMOTE Technique to Handle Imbalanced Dataset
06:35
Filter Method, Wrapper Method, Embedded Method – Feature Selection Techniques
08:47
Coding Feature Selection Techniques
Math Behind KNN
32:21
Math Behind SVM
47:20
Plotting SVM
Introduction to PCA
Math behind PCA
Coding PCA
AutoML Using Pycaret
Probability Distributions, Hypothesis Testing
0/8
Probability Distribution, Random Variable, Binomial, Poisson, Exponential
13:07
Normal Distribution
23:00
Real-Time Project Demonstration on Probability
01:34:20
Hypothesis Testing Part-1
13:07
Hypothesis Testing Part-2
05:58
How to Calculate P-Value? Coding Hypothesis Testing
01:15:23
How to solve PDF, CDF, PMF?
10:03
Chebyshevs, Log, Power Law, Q-Q, CLT
22:14
Mastering NLP, NLU, NLG
0/6
Introduction to NLP, NLP Applications
09:12
NLP Challenges
11:59
NLP Pre-Processing Techniques
14:18
NLP Feature Extraction Techniques
10:19
NLP Practical Example
15:48
NLU, Word2Vec, RNN, NER
02:39:52
Real-Time Projects to Work as Part of Job simulation
0/11
Data Analytics Real-Time Project-1
Data Analytics Real-Time Project-2
Data Analytics Real-Time Project-3
Data Analytics Real-Time Project-4
Data Science Real-Time Project-1
Data Science Real-Time Project-2
Data Science Real-Time Project-3
Data Science Real-Time Project-4
Real-Time NLP Project-1
Real-Time NLP Project-2
Real-Time NLP Project-3
ML Deployment & MLOps
0/14
End-to-End Deployment Using MLOps & Streamlit
03:11:38
Setup Virtual Environment
04:49
Docker Installation
03:47
How to Activate Virtual Environment
03:03
Docker Desktop
02:51
Regression Model using Pycaret
13:26
Regression Model using Pycaret Part-2
02:10
Interpretability of Model using SHAP
04:46
What is SHAP
01:16
Application Development using Gradio
03:31
Creating API using FASTAPI
03:54
Creating Docker Image
05:29
Model Versioning using Pycaret & MLFlow
06:51
End-to-End ML Proof of Concept
00:00
Advanced Time Series with Real-Time Projects
0/7
Time Series
12:50
Introduction to Time Series Modelling
20:43
Time Series Modelling Part-2
50:18
Characteristics of Time Series Modelling
47:56
Time Series Modelling Techniques
47:05
Simple Time Series Project End-to-End
01:13:04
End-to-End Project on Time Series Modelling
01:06:10
Azure Machine Learning Engineering
0/10
Introduction to Azure Environment
Training & Tuning ML Models using Azure
Working with Data in Azure
Training ML Models in Azure ML
Tuning ML Models in Azure
Azure Automated ML
Deploying & Explaining ML Models
Deploying ML Model for Real-Time Inferencing
Deploying ML Models for Batch Scoring
Responsible AI
AWS Sagemaker Studio
0/2
Setting up AWS Sagemaker & Deploying Simple ML Model
15:13
AIOps with AWS Sagemaker Studio
01:02:44
Introduction to Deep Learning & Tensorflow
0/8
Building Blocks of Artificial Neural Networks
What are Optimisers and Types of Optimisers
Math Behind Feed Forward Neural Network
Math Behind BackPropagation
Why Activation Functions & Importance
Introduction to Tensorflow
FFNN-Regression Script using Tensorflow
FFNN – Classification Script Using Tensorflow
Feed Forward Neural Network Coding
0/5
Lecture-5
Lecture-6
Back Propagation
Feed Forward Script Explanation
Introduction to Tensorflow & Feed Forward Neural Network
Convolution Neural Network – Math & coding
0/12
Introduction to CNN & Why CNN Algorithm
What is Object Detection & Object Classification
Intuitive Understanding Behind CNN
Math Behind CNN
CNN Coding using Tensorflow
CNN Coding using Keras
Types of CNN Networks
What is Transfer Learning
Tensorflow Hub & Various Pre-Trained Models
Using Pre-Trained Models for Object Classification
Deploying Image Classification Model in AWS
Project Demonstration: Image Classification Project
Computer Vision Mastering Using Tensorflow
0/9
Introduction to Object Detection vs Object Classification
Introduction to CNN
Convolution Matrix
Max Pooling Layer
Point-Wise & Element-Wise Convolution
Batch Normalisation
Strides & Padding
Convolution Filters
End-to-End Computer Vision
Transfer Learning OpenCV & YOLO
0/5
Introduction to Transfer Learning
Headless & With Head Transfer Learning
Object Detection using YOLO
Object Detection using Open CV
Transfer Learning with Tensorflow Hub
RNN & LSTM Coding & Math
0/8
What is RNN & Why RNN?
Limitations of RNN & Why LSTM
Intuitive Understanding Behind RNN & LSTM
Math Behind RNN
Math Behind LSTMS
Math Behind GRUs
Coding RNN, LSTM, GRU using Tensorflow
Project Demonstration: Forecasting Project using LSTMS
Mastering NLP
0/13
Introduction to NLP & NLP Applications
NLP Challenges
NLP Pre-Processing Techniques
NLP Feature Extraction
Project Demonstration: NLP Classification Project
What is N-Gram & Why N-Gram Technique
Coding N-Gram Technique
What is Word2Vec?
Word2Vec Algorithms like CBOW & Skip-Gram
Case Study: Coding Word2Vec Model
What is latent dirichlet allocation? Why LDA
What is Topic Modelling?
Case Study: Coding Topic Modelling
Mastering NLU
0/17
What is NLU & Why NLU
What is POS Tagging
Case Study: Coding POS Tagging
What is Dependency Parsing?
What is Constituency Parsing?
Case Study: Coding Dependency & Constituency Parsing
What is NER? & Usage of NER
Case Study: Coding NER
What is Coreference Resolution?
History of NLU Models
Project Demonstration: NLU Classification Model using LSTM
What is Encoder & Decoder Model
Math Behind Encoder-Decoder Models
Case Study: Coding Encoder-Decoder Model
What is Attention Based Models
Math behind Attention Based Models
Case Study: Coding Attention Based Models
Generative AI: Transformer Models BERT, T5, ELMO
0/14
Introduction to Transformers
Scaled Dot Product Attention
Multi-Headed Attention
Introduction to Transfer Learning
Introduction to PyTorch
Fine-Tuning Transformers using PyTorch
Introduction to BERT
Wordpiece Tokenisation
Embeddings of BERT
Masked Language Modelling Task
Project Demonstration: Sentence Prediction Task
Types of BERT
BERT for Sequence Classification
BERT for Question & Answering
Generative AI: Natural Language Generation Using GPT
0/7
Introduction to GPT
Masked Multi-Headed Attention
Pre-Training GPT
Project Demonstration: GPT for Style Completion
Few Shot Learning
Project Demonstration: GPT code Dictation
GPT Multiple Tasks at once with Prompt Engineering
Generative AI: Mastering T5
0/3
Encoder Decoder T5 Architecture
Cross Attention
T5 for Abstractive Summarisation
Generative AI: Vision Transformers
0/2
Introduction to Vision Transformers
Project Demonstration: Fine-Tuning Image Captioning
Generative AI: Autoencoders
0/6
What are Autoencoders?
Types of Autoencoders
Math Behind Autoencoders
Project Demonstration: Coding Different Types of Autoencoders
DeNoising Autoencoders
Variational AutoEncoders
Generative AI: Image Based Models
0/10
What are Generative Models
Math Behind Generative Models
Project Demonstration: GANs
Types of GANs
What are Restricted Boltzmann Machines?
Math Behind Restricted Boltzmann Machines
Project Demonstration: Coding RBMs with Case Study
What is Neural Style Transfer?
Math Behind Neural Style Transfer
Project Demonstration: Coding of NST
Generative AI: Diffusion Models, Energy Based Models & NFMs
0/9
Introduction to Diffusion Models
Deep Dive into CLIP Research Paper
Deep Dive into GLIDE Research Paper
Understanding Diffusion Model Theory & Architecture
Project Demonstration on Diffusion Models
Understanding Energy Based Models Theory & Architecture
Project Demonstration on Energy Based Models
Understanding Normalising Flow Models
Project Demonstration on Normalising Flow Models
Generative AI – LLMS: Hugging Face
0/5
Introduction to Hugging Face
End-to-End Pre-Trained Model using Hugging Face
Fine-Tuning BERT Models using Hugging Face
Building LLMs using Hugging Face
Deployment using Hugging Face & StreamLit
Prompt Engineering – LLMs: Lang Chains
0/9
Introduction to Lang Chains
Using LLMs using Lang Chains
What is Prompt Engineering
Types of Prompt Engineering
Best Way to Improve LLMs Accuracy
Doing Prompt Engineering using Lang Chains
Building RAG Architecture with LangChains
Fine-Tuning LLMs with LangChains
Pre-Training LLMs
MLOps for Transformer Models
0/4
Introduction to MLOps
Model Sharing on HuggingFace
BERT Model Deployment using FastAPI
Modern Language Models like GPT-3 & ChatGPT
Deep Reinforcement Learning
0/17
What is Reinforcement Learning
RL versus ML Framework
Why RL?
Examples of RL
Limitations of RL
Terminologies of RL
Q-Learning & Q-Table Theory
Implementation of Q-Learning
Reinforcement Learning Based Q-Learning
Introduction to Rules of Frozen Lake
Implementation of Frozen Lake
Hyper Parameter Tuning
Introduction to SARSA(State-Action-Reward-State-Action)
Implementation of SARSA
Introduction to Deep Reinforcement Learning DQN
Implementation of DQN
Project Demonstration: DQN Project
R Programming
0/10
Installation of R Studio
19:42
Basics of R Studio
39:40
Data Visualisation with R Studio
33:25
Data Modelling with R Studio
13:53
Data Joining with R Studio
09:45
Data Visualisation with GGPlot2
11:03
Data Preparation with R Studio
09:39
Data Cleaning with Regexp
34:30
Data Visualisation with Plotly
44:40
test
AWS Sagemaker Deployment & Project
0/1
Lecture-11
Interview Questions: Frequently Asked Statistics
0/10
Introduction to Statistics
Properties of Data
Sample vs Population
Types of Sampling Techniques
Exploratory Data Analysis
Analysing Data with Applied EDA
Measures of Dispersion
Skewness & Normality
Correlation & Scatterplot
Barplot & Lineplot
MindMap for Interview Revision
0/11
Data Science Interview Revision Mindmap
Statistics Mindmap
Python Learning Mindmap
SQL Interview Mindmap
Machine Learning Accuracy Improving Techniques Mindmap
Machine Learning Math Mindmap
Real-Time Project Procedure Mindmap
ML Framework Mindmap
Power BI Interview Mindmap
NLP Interview Mindmap
Deep Learning Interview Mindmap
Interview Preparation
0/5
How to build Linkedin Profile
01:10:27
Resume Preparation for Data Science
52:37
Do’s and Don’t while Marketing our Resume
12:54
Do’s and Don’ts in LinkedIn/Job Portals
09:47
3 Elements to Crack Any Interview
11:57
Course Files
0/1
Course Files
Handwritten Notes
0/2
Python Handwritten Notes
SQL Handwritten Notes
Private: Be A Job-Ready Generative AI Engineer in 7 Months
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