Complete Data Science, Machine Learning

01 Welcome To The Course
03 Anaconda Installation
04 Getting Started With VS Code

01 Getting Started With VS Code
02 Different Ways of Creating Virtual Environment
04 Python Basics Syntax And Semantics
05 Variables In Python
06 Basics Data Types In Python
07 Operators In Python

01 Conditional Statements if elif else
02 Loops In Python

01 List And List Comprehension In Python
03 Tuple In Python
05 Sets In Python
07 Dictionaries In Python
09 Real world Usecases Of List

01 Getting Started With Functions
02 More Coding Example With Functions
03 Lambda Function In Python
04 Map Function In Python
05 Filter Function In Python

06. Function Practice Question

07. Inbuilt Data Structure - Practice Question

01 Import Modules And Packages In Python
02 Standard Library Overview

01 File Operation In Python
02 Working With File Paths

01 Exception Handling With try except else and finally blocks

01 Classes And Objects In Python
03 Inheritance In OOPS
04 Polymorphism In OOPS
05 Encapsulation In OOPS
06 Abstraction In OOPS
08 Magic Methods In Python
09 Operator Overloading In Python
10 Custom Exception Handling

01 Iterators In Python
02 Generators With Practical Implementation
03 Function CopyCloures And Decorators

01 Numpy In Python
02 Pandas DataFrame And Series
03 Data Manipulation With Pandas And Numpy
04 Reading Data From Various Data Source Using Pandas
05 Data Visualization With Matplotlib
06 Data Visualization With Seaborn

02 Crud Operation With SQLite3 And Python

01 Logging Practical Implementation In Python
02 Logging With Multiple Loggers
03 Logging With A Real World Example

01 What Is Process And Threads
02 2 Multithreading Practical Implementation With Python
03 Multiprocessing Practical Implementation With Python
04 Thread Pool Executor and Process Pool
05 Implement Web Scraping Usecase With Multithreading
06 Real World Usecase Implementation With MultiProcessing

01 Memory Allocation And DeallocationGarbage collection and Best Practises

01 Introduction To Flask Framework
02 Understanding Simple Flask App Skeleton
03 Integrating HTML With Flask Web App
04 Working With HTTP Verbs Get And Post
05 Building Dynamic Url Variables Rule And Jinja 2 Template Engine
06 Working With Rest APIs And HTTP Verbs Put And Delete

01 Building Web App Using Streamlit
02 Example Of ML App With Streamlit Web App

01 What is Statistics And its Application
02 Types Of Statistics
03 Population Vs Sample Data
04 Measure Of Central Tendency
05 Measure Of Dispersion
06 Why Sample Variance Is Divided By n 1
07 Standard Deviation
08 What Are Variables
09 What are Random Variables
10 Histograms Descriptive Statistics
11 Percentile And Quartiles Descriptive Statistics
12 5 Number Summary Descriptive Statistics
13 Correlation And Covariance

01 Addition Rule For Mutual And Non Mutual Exclusive Events
02 Probability Multiplication RuleIndependent And Dependent Events

01 The Relationship Between PDFPMF And CDF
02 Types Of Probability Distribution
03 Bernoulli Distribution
04 Binomial Distribution
05 Poisson Distribution
06 Normal Gaussian Distribution
07 Standard Normal Distribution And Z Score
08 Uniform Distribution
09 Log Normal Distribution
10 Power Law Distribution
11 Pareto Distribution
12 Central Limit Theorem
13 Estimates

01 Hypothesis Testing And Mechanism
02 What is P value
03 Z Test Hypothesis Testing
04 Student t Distribution
05 T Stats With T test Hypothesis Testing
06 Z test Vs T test
07 Type 1 And Type 2 Error
08 Bayes Theorem
09 Confidence Interval And MArgin Of Error
10 What is Chi Square Test
11 ChiSquare Goodness OF Fit
12 Annova Test
13 Assumptions Of Annova
14 Types Of Annova
15 Partioning Of Variance In Annova

01 Handling Missing Values
02 Handling Imbalanced Dataset
03 Handling Imbalanced Dataset Using Smote
04 Handling Outliers Using Python
05 Data Encoding Nominal Or OHE
06 Label And Ordinal Encoding
07 Target Guided Ordinal Encoding

01 Red Wine Dataset EDA
02 EDA And Feature Engineering Flight Price Dataset
03 Data Cleaning With Google Playstore Dataset
04 Part 2 EDA Google Play Store Cleaned Dataset

01 Introduction
02 Types of ML Techniques
03 Equation of Line 3d and Hyperplane
04 Distance of a point from a plane
05 Instance based Vs Model based learning

01 Simple Linear Regression Introduction
02 Understanding Simple Linear regression Equations
03 Cost Function
04 Convergence Algorithm
05 Convergence Algorithm Part02
06 Multiple Linear regression
07 Performance Metrics
08 MSE MAE RMSE
09 Overfitting and Underfitting
10 Linear Regression with OLS
11 Simple Linear Regression Practical
12 Multiple Linear regression
13 Polynomial Regression Intuition
14 Polynomial Regression Implementation
15 Pipeline in Polynomial

01 Ridge Regression
02 Lasso ElasticNet
03 Types Of cross Validation
04 Cleaning the Dataset
05 EDA and Feature Engineering
06 Feature Selection
07 Model Training
08 Hyperparameter tuning

01 Basic Simple Linear Regression Project
02 Multiple Linear Regression Projects With Assumptions
03 Basic Regression Project From Scratch EDA And Feature Engineering
04 Model Training With Cross Validation Using Lasso Regression
05 Model Training With Ridge and Elastic net With Cross Validation
06 Model Pickling In ML Project
07 End To End ML Project Implementation
08 Project Deployment In AWS

01 Can Linear Regression Solve Classifier Problem
02 Logistic Regression Indepth Math Intuition
03 Performance Metrics
04 Logistic Regression OVR
05 Logistic Regression Implementation
06 Grid Search Hyper Parameter
07 Randomised Search CV
08 Logistic OVR
09 Logistic Imbalanced Dataset
10 Logistic Regression ROC

01 Introduction to support vector Machine
02 SoftMargin and Hard Margin
03 SVM Maths Intuition
04 SVC Cost function
05 Support Vector Regression
06 SVM Kernels
07 Support Vector Classifiers
08 SVM Kernels implementation
09 Support Vector Regression Implementation

01 Understanding Bayes Theorem
02 Variants Of Naive Bayes
03 Naive Bayes Practical Implementation

01 KNN Classification And Regression Indepth Intuition
02 Optimization Of KNN KDtree And Ball Tree Indepth Intuition
03 KNN Classifier And Regressor Classification

01 Introduction TO Decision Tree
02 Entropy and Gini Impurity
03 Information Gain
04 Entropy vs Gini impurity
05 Decision Tree Split for Numerical Features
06 Post Pruning Pre Pruning
07 Decision Tree Regression
08 Decision Tree Implementation
09 Decision tree Prepruning
10 Diabetes Prediction Using Decision Tree Regressor

01 Bagging Boosting Ensemble Techniques
02 Random Forest Regression
03 Problem Classification
04 Feature Engineering Part 01
05 Feature Engineering Part 02
06 Model Training Step
07 Random Forest Regression Project Problem Statement
08 Feature Engineering
09 Model Training

01 Introduction to Adaboost ML algorithm
02 Creating Decision Tree Stump
03 Performance of Decision Tree Stump
04 Updating Weights
05 Normalising Weights and Assigning Bins
06 Selecting New Datapoints for Next tree
07 Final Prediction for Adaboost
08 Adaboost Model Training
09 Adaboost Regressor Model Training

01 Gradient Boosting Regression
02 Gradient Boost Classifier Training
03 Gradient Boost Regression Model Training

01 Xgboost Classification Indepth Intuit
02 Xgboost Regressor
03 Model Training Xgboost
04 Xgboost Regressor Training

01 Introduction To Unsupervised Machine Learning

01 Curse Of Dimensionality
02 Feature Selection and Extraction
03 PCA Geometric Intuition
04 PCA Maths Intuition 01
05 Eigen Decomposition on Covariance Matrix
06 PCA Implementation

01 Kmeans Clustering Geometric Intuition
02 How to Find K Values
03 Random Initialisation TrapKmeans
04 K means Clustering Implementation

01 Hierarichal Clustering
02 Agglomerative Clustering Implementation
03 Kmeans vs Hierarical Mean Clustering

01 How DBSCAN Works
02 Examples After Applying DBSCAN
03 Pros and Cons Of DBSCAN
04 DBSCAN Clustering Implementation

01 Silhoutte Clustering Intuition

01 Anomaly Detection USsing Isolation Forest Indepth Intuition
02 DBSCAN Clustering Anomaly Detection
03 Local Outlier Factor Anomaly Detection

01 Dockers Series
02 Dockers and What is Containers
03 Docker image vs Containers
04 Docker vs Virtual Machines
05 Docker Installation
06 Docker Basic Commands
07 Creating a Docker Image
08 Push Docker Image to Docker Hub
09 Docker Compose

01 Introduction Installation and Basic Commands
02 Git Merge Push Checkout and Log with commands
03 Resolving Git Branch Merge Conflict

01 End To End ML Project With Deployment Github And Code Set Up
02 Implementing Project Structure Logging And Exception Handling
03 Discussing Project Problem StatementEDA And Model Training
04 Data Ingestion Implementation
05 Data Transformation Using Pipelines Implementation
06 Model Trainer Implementation
07 Model Hyperparameter Tuning Implementation
08 Building Prediction Pipeline
09 ML Project Deployment Using AWS Beanstalk
10 Deployment EC2 Instance With ECR
11 Deployment Azure With Container And Images

01 Project Structure Set up With Environment
02 Github Repository Set Up With VS Code
03 Packaging the Project With Setuppy
04 Logging And Exception Handling Implementation
05 Introduction To ETL Pipelines
06 Setting Up MongoDb Atlas
07 ETL Pipeline Setup With Python
08 Data Ingestion Architecture
09 Implementing Data Ingestion Configuration
10 Implementing Data Ingestions Component
11 Implementing Data Validation Part 1
12 Implementing Data Validation Part 2
13 Data Transformation Architecture
14 Data Transformation Implementation
15 Model Trainer Implementation Part 1
16 Model Trainer And Evaluation With Hyperparameter Tuning
17 Model Experiment Tracking With MLFLOW
18 MLFLOW Experiment Tracking With Remote Respository Dagshub
19 Model Pusher Implementation
20 Model Training Pipeline Implementation
21 Batch Prediction Pipeline Implementation
22 Final Model And Artifacts Pusher To AWS S3 buckets
23 Building Docker Image And Github Actions
24 Github Action Docker Image Push to AWS ECR Repo Implementation
25 Final Deployment To EC2 instance

01 Getting Started With MLOPS With MLFlow And Dagshub With Project
02 Data Versioning Control Implementation
03 Building Data Science Projects With BentoML

01 Roadmap to Learn NLP for Machine Learning
02 Practical Use cases of NLP
03 Tokenisation and Basic Terminologies
04 Tokenisation Practicals
05 Text Preprocessing Stemming using NLTK
06 Text Preprocessing Lemmatization NLTK
07 Text Preprocessing Stopwords
08 Parts of Speech Tagging Using NLTK
09 Named Entity Recognition
10 Whats Next
11 One Hot Encoding Intuition
12 Advantages and Disadvantages of OHE
13 Bag of Words Intuition
14 Advantages and Disadvantages BOW
15 BOW implementation using NLTK
16 N Grams
17 N Gram BOW Implementation Using NLTK
18 TF IDF Instituion
19 Advantages and Disadvantages of TF IDF
20 TFIDF Practical implementation Python
21 Word Embeddings
22 Word2Vec Intuition
23 Word2Vec Cbow Intuition
24 SkipGram Indepth Intuition
25 Advantages of Word2Vec
26 AvgWord2vec Indepth Intuition
27 Word2vec Practical Implementation Gensim
28 Spam ham Project using BOW
29 Spam And Ham Project Using TFidf
30 Best Practises For Solving ML Problems
31 Part 1 Text Classification With Word2vec And AvgWord2vec
32 Part 2 Text Classification With Word2vec And AvgWord2vec
33 Part 1 Kindle Review Sentiment Analysis
34 Part 2 Kindle Review Sentiment Analysis

01 Introduction
02 Why Deep Learning is getting Popular
03 3 Perception Intuition
04 Advantages and Disadvantages of Perceptron
05 ANN Intuition and Learning
06 Back Propogation and Weight Updation
07 Chain Rule of Derivatives
08 Vanishing Gradient Problem and Sigmoid
09 Sigmoid Activation Function
10 Sigmoid Activation Function 20
11 Tanh Activation Function
12 Relu activation Function
13 Leaky Relu and Parametric Relu
14 ELU Activation Function
15 Softmax For Multiclass Classification
16 Which Activation Function To Apply When
17 Loss Function Vs Cost Function
18 Regression Cost Function
19 Loss Function Classification Problem
20 Which Loss Function To Use When
21 Gradient Descent Optimisers
22 SGD
23 Mini Batch With SGD
24 SGD With Momentum
25 Adagard
26 RMSPROP
27 Adam Optimiser
28 Exploding Gradient Problem
29 Weight Initialisation Techniques
30 Dropout Layers
31 CNN Introduction
32 Human Brain Vs CNN
33 All you need to Know about Images
34 Convolution Operation In CNN
35 Padding In CNN
36 Operation Of CNN Vs ANN
37 Max Min and Average Pooling
38 Flattening and Fully Connected Layers
39 CNN example with RGB

01 Discussing Classification Problem Statement And Setting Up Vs Code
02 Feature Transformation Using Sklearn With ANN
03 Step By Step Training With ANN With Optimizer and Loss Functions
04 Prediction With Trained ANN Model
05 Integrating ANN Model With Streamlit Web APP
06 Deploying Streamlit web app with ANN Model
07 ANN Regresiion Practical Implementation
08 Finding Optimal Hidden Layers And Hidden Neurons In ANN

01 Introduction To NLP In Deep Learning

01 Understanding RNN Architecture RNN Vs ANN
02 Forward Propogation With Time In RNN Training
03 Backward Propogation With Time In RNN Training
04 Problems With RNN

01 Problem Statement
02 Getting Started With Word Embedding Layers
03 Implementing Word Embedding With Keras Tensorflow
04 Loading And Understanding IMDB Dataset And Feature Engineering
05 Training Simple RNN With Embedding Layer
06 Prediction From Trained Simple RNN
07 End To End Streamlit Web App Integrated With RNN And deployment

01 Why LSTM RNN
02 LSTM RNN Architecture
03 Forget Gate In LSTM RNN
04 Input Gate And Candidate Memory In LSTM RNN
05 Output Gate In LSTM RNN
06 Training Process In LSTM RNN
07 Variants Of LSTM RNN
08 GRU RNN Complete Indepth Intuition

01 Discussing Problem Statement
02 Data Collection And Data Processing
03 LSTM Neural Network Model Training
04 Prediction From LSTM Model
05 Streamlit Webapp Integration With LSTM Trained Model
06 GRU RNN Variant Practical Implementation

01 Bidirectional RNN Architecture and Intuition

01 Indepth Intuition Of Encoder And Decoder Sequence to Sequence Architecture
02 Problems With Encoder And Decoder

01 Attention Mechanism Indepth Architecture Explanation

01 Plan Of Action
02 What And Why To Use Transformers
03 Understanding the basic architecture of transformers
04 Self Attention Layer Working
05 Multi Head Attention
06 Feed Forward Neural Network With Multi Head Attention
07 Positional Encoding Indepth Intuition
08 Layer Normalization
09 Layer Normalization Examples
10 Complete Encoder transformer architecture
11 Decoder Transformer Plan Of Action
12 Decoder Transformer Masked Multi Head Attention Working
13 Encoder Decoder Multi Head Attention
14 Final Decoder Linear And Softmax Layer

Complete Data Science, Machine Learning, DL, NLP Bootcamp 2025

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Complete Data Science, Machine Learning, DL, NLP Bootcamp 2025

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