Self Paced 2025 - Machine Learning and Deep Learning
Course Instructor
Innomatics Research Labs
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Course Overview
Schedule of Classes
Course Curriculum
1 Subject
Self Paced 2025 - Machine Learning and Deep Learning
11 Exercises • 184 Learning Materials
Phase 1 - Machine Learning and Preprocessing Data
Datasets - ML and DL
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Introduction to the Machine Learning
Video
2:12:11
Introduction to AL, ML and DL
PDF
How to solve ML Problems
Video
2:21:12
How to solve a ML problem & Data Architecture
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Handling missing values in ml
Video
2:12:3
Restaurant Review Dataset
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Social_Network_Ads Dataset
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Data PreProcessing -- Handling Missing Values
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Data Preprocessing
Video
2:14:48
Data PreProcessing -- Handling Outilers & Removing Unwanted Information
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Hands on data preprocessing
Video
2:15:00
Data PreProcessing -- Encoding Categorical Data
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Building a ML Model
Video
2:26:13
Train test split, scaling and Customer Churn Prediction
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Working with text data -- Sentiment Analysis
Video
2:12:22
Working with text data -- Sentiment Analysis
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Working with Text Data -- Stemming & Lemmatization / EDA, Bag of words
Video
2:6:18
14th Nov - Working with Text Data -- Stemming & Lemmatization / EDA, Bag of words
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15th Nov - Sentiment Analysis Use-case
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Sentiment Analysis Use-case
Video
2:12:59
Working with PIL Images
Video
2:1:5
Working with PIL Images
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Working with PIL --Images
Video
2:22:22
- Working with PIL --Images
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MNIST Hand written words
Video
2:16:11
MNIST Dataset JPG Format
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MNIST -- Hand written digit recognition
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Mathematical concepts
Video
2:11:15
Mathematical concepts
PDF
Cosine Similarity, Unit Vector, Equation of line
Video
2:16:49
Cosine Similarity, Unit Vector, Equation of line
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Datasets - ML and DL
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Phase 2 - Deep Dive into ML Algorithms
Linear Regression
Video
1:47:10
Linear Regression
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Gradient Decent
Video
2:9:45
50 Startup Dataset
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Linear Regression Using Gradient Descent
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Multiple Linear Regression
Video
2:18:6
Building MLR Model
Video
2:9:33
Variance inflation factor
Video
2:14:26
Logistic Regression
Video
2:15:51
Logistics regression for multiclass
Video
2:17:21
Multiple Linear Regression
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Linear Regression using OLS
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Linear Regression assumptions
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Logistic Regression
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Logistic Regression for Multiclass
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Polynomial sklearn pipeline
Video
2:14:47
Logistic Regression 2
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sklearn pipeline
Video
2:21:2
sklearn pipeline
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KNN
Video
2:9:9
KNN Classification and Regression
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Soft max SVM
Video
2:22:29
SVM Classification
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Kernel SVM
Video
2:9:10
Kernel SVM
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Model selection and Regularization
Video
2:11:42
Kernel SVM & Model Selection
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Regularization & Bayes Theorem
Video
2:20:47
Regularization & Bayes Theorem File
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Navie Bayes
Video
2:18:41
Navie Bayes
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Decision Tree
Video
2:23:29
Decision Tree
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Decision Tree Regressor
Video
2:14:41
Cross validation
Video
2:13:9
DT for Classification and Regression
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Decision Tree Regression & Hyperparameters
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Hyper parametric tunning and metrics for regression
Video
2:13:54
Hyperparameter tuning
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Hyper parametric Tuning
Video
2:15:37
24thd Dec - Parallel Ensemble
Video
2:19:36
Parallel Ensemble
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AdaBoost and GBM
Video
2:19:41
AdaBoost and GBM
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GBM
Video
2:13:00
Ensemble Learning -- Random Forest
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XG Boost
Video
2:17:45
AUC ROC Score & Ensemble Learning -- Random Forest
Video
1:59:32
Parallel Ensembles -- RandomForest, Voting, Stacking
Video
2:13:17
Materials
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XGBoost
Video
2:20:35
XGBoost
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Feature Selection
Video
2:17:2
Feature Selection
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Phase 3 - Unsupervised Learning
Unsupervised machine learning_ k means clustering
Video
2:16:53
hierarchical and Metrix clustering
Video
2:14:29
hierarchical and Metrix clustering
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Dimensionality Reduction and PCA
Video
2:27:38
Dimensionality Reduction and PCA
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n model select_ t - SNE
Video
2:7:32
t-SNE
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ML - Assignments / Project
Assignment 1 - Model Building for Regression
Assignment
Project 2 : Diamond Data Set
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Project 3 : Penguin Dataset
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Project 4 : Telecom Customer Churn
Assignment
Project 5 - Fedex Project
Assignment
Project 6 - Final Project
Assignment
Deep Learning - ANN
Introduction to deep Learning
Video
2:20:48
21st Jan - Neural Network Learning Process
Video
2:17:25
22nd Jan - Explanation of Neural Network
Video
1:52:7
23rd Jan - Churn Modeling
Video
2:7:34
24th Jan - Over Sampling
Video
2:14:52
27th Jan - MINST Hand written words
Video
2:12:28
28th Jan - work with image data
Video
2:14:44
29th Jan - Exponential Weighted averages
Video
2:22:26
20th Jan - Introduction to Deep Learning
PDF
21st Jan - Neural Network Architecture and Activation Functions
PDF
22nd Jan - Forward and Backward Propagation
PDF
23rd Jan - Bank Churn Prediction using ANN
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24th Jan - ANN Hyperparameters and Model Optimization
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27th Jan - Handwritten Digit Identification using ANN -- Multiclass
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28th Jan - Gradient Descent
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29th Jan - Backpropagation in Neural Network and EWA
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3rd Feb - Optimizers -- SGD with Momentum, RMSProp
Video
2:26:42
3rd Feb - Optimizers -- SGD with Momentum, RMSProp
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Deep Learning - CV
4th Feb - Intro to Images and Image Preprocessing with OpenCV
Video
2:13:47
4th Feb - Intro to Images and Image Preprocessing with OpenCV
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5th Feb - thresholds in open cv
Video
2:11:25
5th Feb - Image Preprocessing with OpenCV
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6th Feb - Erosion and dilation
Video
2:11:27
6th Feb - Opencv-Erosion Dilation & Face Detection
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7th Feb - Intro to Convolutional Neural Network
Video
2:17:57
7th Feb - Intro to Convolutional Neural Network
PDF
10th Feb-CNN Practical
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11th Feb - CNN Practical
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12th Feb - CNN Architectures
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17th Feb - Vanishing and Exploding Gradient Problem
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13th Feb - CNN Architectures -- VGG Practical
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18th Feb - CNN ResNet
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20th Feb -- YOLO Practical
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19th Feb -- Object Detection -- YOLO
PDF
21th Feb PAR Inventory Object Detection Usecase
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8th Feb -
Video
2:45:2
10th Feb - CNN Practical
Video
1:59:3
11th Feb - CNN Practical
Video
2:11:37
12th Feb - CNN Architectures
Video
2:20:18
13th Feb - CNN Architectures -- VGG Practical
Video
2:17:6
17th Feb - Vanishing and Exploding Gradient Problem
Video
2:12:33
18th Feb - CNN ResNet
Video
2:11:00
19th Feb - Object Detection -- YOLO
Video
2:15:22
20th Feb - YOLO Practical
Video
2:15:47
21st Feb - PAR Inventory Object Detection Usecase
Video
2:5:42
24th Feb - RNN
Video
2:18:28
25th Feb - Amazon Stock Price prediction using RNN
Video
2:5:49
26th Feb - Amazon Stock Price prediction using RNN-2
Video
2:000
28th Feb - GRU
Video
2:10:33
03rd March -- Amazon Stock Price prediction using LSTM
Video
2:6:39
04th March -- Word Embeddings
Video
2:22:9
05th March -- Word2Vec
Video
1:57:19
06th March -- Next Word Prediction
Video
2:20:57
07th March -- Next Word Prediction
Video
1:50:6
10th March - Chatbot using Encoder Decoder
Video
1:57:35
11th March - Chatbot using Encoder Decoder
Video
2:15:38
12th March - Attention Mechanism
Video
2:033
13th March -Transformer
Video
2:15:31
18th March -Segmentation
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15th March -
Video
3:12
17th March - Language Translation using Transformer
Video
2:3:47
18th March -Segmentation
Video
2:14:33
19th March - BERT
Video
2:3:52
20th March -
Video
2:13:21
21st March -
Video
1:59:16
25th March - Introduction about deployment, streamlit, huggingface. creating pytho
Video
1:59:2
28th March - DeepLearning Model Deploymetn using Streamlit and Huggingface
Video
2:8:59
26th March - EDA with Streamlit
Video
2:021
27th March - ML model Deployment using streamlit and Huggingface
Video
2:12:6
1st April -
Video
1:57:33
2nd April -
Video
1:54:46
Deep Learning - NLP
24th Feb RNN
PDF
25th Feb Amazon Stock Price prediction using RNN
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26th Feb Amazon Stock Price prediction using RNN-2
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28th Feb -- GRU
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03rd March -- Amazon Stock Price prediction using LSTM
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04th March -- Word Embeddings
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05th March -- Word2Vec
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06th March -- Next Word Prediction
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7th March -- Next Word Prediction
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10th March --Chatbot using Encoder Decoder
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11th March - Chatbot using Encoder Decoder
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12th March - Attention Mechanism
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13th March -Transformer
PDF
17th March - Language Translation using Transformer
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19th March - BERT
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20th March Introduction to GenAI
PDF
21th March Working with OpenSource LLM's
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1st April SqlQueryGen
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2nd April Explore Open source LLMs and Prompting techniques
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Assignments/Projects
Project 1 : ANN
Assignment
Project 2 : CNN
Assignment
Project 3 : Spam and Ham
Assignment
Project 4 - Next Word Prediction
Assignment
Project 5 - ChatBot
Assignment
Course Instructor
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Innomatics Research Labs
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