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Stock Price Prediction with Time Series Analysis using Machine Learning (with full Code)
Predictive Maintenance of Machines using Machine Learning on Sensor Data (with full Code)
CIFAR-10 Image Classification using CNN with Streamlit Deployment (with full Code)
Building a Movie Recommendation System with NLP and K-Means Clustering (with full Code)
Twitter (X) Sentiment Analysis using NLP and Logistic Regression + Streamlit UI (with full Code)
Credit Card Default Prediction using Random Forest (with full Code)

Curriculum

22 Sections
153 Lessons
Lifetime

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Stock Price Prediction with Time Series Analysis using Machine Learning (with full Code)
0
Overview of Project
11
- 2.1
  IntroductionCopy
  4 Minutes
- 2.2
  Problem Statement [with Video]Copy
  4 Minutes
- 2.3
  Challenges with Stock price dataCopy
  4 Minutes
- 2.4
  Evaluation and OutcomeCopy
  5 Minutes
- 2.5
  How to Go AboutCopy
  3 Minutes
- 2.6
  Study AlongCopy
  2 Minutes
- 2.7
  Use of ChatGPTCopy
  2 Minutes
- 2.8
  Project CodeCopy
  2 Minutes
- 2.9
  Setting up your Project Development EnvironmentCopy
  3 Minutes
- 2.10
  Setting up your Local (Laptop/Desktop) EnvironmentCopy
  6 Minutes
- 2.11
  EDA QuizCopy
  10 Minutes10 Questions
Exploratory Data Analysis (EDA) Phase
5
- 3.2
  Initial StudyCopy
  4 Minutes
- 3.3
  Data Selection and Import [with Video]Copy
  3 Minutes
- 3.4
  Perform the EDA Steps [with Video]Copy
  10 Minutes
- 3.5
  EDA Coding [with Video]Copy
- 3.6
  EDA Assignments SubmissionCopy
  3 Minutes
Machine Learning Model Building
13
- 4.1
  Model Building – StudyCopy
  8 Minutes
- 4.2
  Pre-knowledge RequirementsCopy
  10 Minutes
- 4.3
  Understanding ARIMA [with Video]Copy
  10 Minutes
- 4.4
  Data StationarityCopy
  8 Minutes
- 4.5
  Check for Data StationarityCopy
  4 Minutes
- 4.6
  ARIMA Parameters – p, d and qCopy
  6 Minutes
- 4.7
  Model Building ApproachesCopy
  7 Minutes
- 4.8
  ACF and PACF PlotsCopy
  10 Minutes
- 4.9
  Approach 1: Manual [with Video]Copy
  15 Minutes
- 4.10
  Approach 2: Auto ARIMACopy
  10 Minutes
- 4.11
  Challenges in ARIMA AccuracyCopy
  12 Minutes
- 4.12
  Improvement StepsCopy
  10 Minutes
- 4.13
  Code ZIPCopy
  10 Minutes
Predictive Maintenance of Machines using Machine Learning on Sensor Data (with full Code)
0
Overview of Project
12
- 6.2
  Introduction [with Video]Copy
  3 Minutes
- 6.3
  Importance of Predictive MaintenanceCopy
  3 Minutes
- 6.4
  Project Scope and ObjectivesCopy
  8 Minutes
- 6.5
  Project Approach that you should followCopy
  5 Minutes
- 6.6
  Study AlongCopy
  2 Minutes
- 6.7
  Use of ChatGPTCopy
  2 Minutes
- 6.8
  Project CodeCopy
  2 Minutes
- 6.12
  Setting up your Project Development EnvironmentCopy
  3 Minutes
- 6.13
  Setting up your Local (Laptop/Desktop) EnvironmentCopy
  6 Minutes
- 6.14
  EDA Quiz – Predictive MaintenanceCopy
  10 Minutes10 Questions
- 6.15
  Learning Resources [PDFs] – Logistic Regression & Random ForestCopy
- 6.16
  Learning Resources [PDFs] – Support vector Machine (SVM)Copy
Exploratory Data Analysis (EDA) Phase
7
- 7.2
  Initial StudyCopy
  4 Minutes
- 7.3
  Import Input Data [Video]Copy
  10 Minutes
- 7.4
  EDA Study [with Video]Copy
  4 Minutes
- 7.5
  EDA StepsCopy
  6 Minutes
- 7.6
  Perform EDA Steps – Quantitive AnalysisCopy
  15 Minutes
- 7.7
  EDA Assignments SubmissionCopy
  3 Minutes
- 7.9
  EDA Submission – Predictive MaintenanceCopy
  3 Days
Machine Learning Model Building
14
- 8.1
  Model Building – StudyCopy
  8 Minutes
- 8.2
  Pre-knowledge RequirementsCopy
  8 Minutes
- 8.3
  Model Building StepsCopy
  5 Minutes
- 8.4
  Logistic Regression Concepts [Video]Copy
  20 Minutes
- 8.5
  Random Forest Concepts [Video]Copy
  20 Minutes
- 8.6
  Support Vector Machine Concepts [Video]Copy
  20 Minutes
- 8.7
  Classification Errors and Metrics [Video]Copy
  10 Minutes
- 8.8
  Experiment 1: Logistic Regression Model [with Video]Copy
  10 Minutes
- 8.9
  Experiment 1: Logistic Regression Code [with Video]Copy
- 8.10
  Experiment 2: Random Forest Model [with Video]Copy
  10 Minutes
- 8.11
  Experiment 3: Support Vector Machine [with Video]Copy
  10 Minutes
- 8.12
  Model Performance ComparisnCopy
- 8.13
  Project Final Submission – Predictive MaintenanceCopy
  3 Days
- 8.14
  Code ZIPCopy
  10 Minutes
CIFAR-10 Image Classification using CNN with Streamlit Deployment (with full Code)
0
Overview of Project
9
- 10.1
  Introduction [Video]Copy
  10 Minutes
- 10.2
  Project Scope and ObjectivesCopy
  8 Minutes
- 10.3
  How to Go AboutCopy
  3 Minutes
- 10.4
  Study AlongCopy
  2 Minutes
- 10.5
  Use of ChatGPTCopy
  2 Minutes
- 10.6
  Project CodeCopy
  2 Minutes
- 10.7
  Setting up your Project Development EnvironmentCopy
  3 Minutes
- 10.8
  Setting up your Local (Laptop/Desktop) EnvironmentC
  6 Minutes
- 10.9
  Project StepsCopy
  8 Minutes
Exploratory Data Analysis (EDA) Phase
6
- 11.1
  Initial StudyCopy
  4 Minutes
- 11.2
  Understanding the CIFER-10 Dataset [Video]Copy
  10 Minutes
- 11.3
  Importing the CIFER-10 DatasetCopy
  8 Minutes
- 11.4
  Perform the EDA Steps [Video]Copy
  10 Minutes
- 11.5
  EDA Coding [Video]Copy
  10 Minutes
- 11.6
  EDA Assignments SubmissionCopy
  3 Minutes
Machine Learning Model Building
15
- 12.1
  Understanding CNNs [Video]Copy
  10 Minutes
- 12.2
  Convolutions and Pooling Concepts of CNNCopy
  15 Minutes
- 12.3
  Why CNNs for the CIFAR-10 ProblemCopy
  6 Minutes
- 12.4
  CNN Architecture for this ProjectCopy
  10 Minutes
- 12.5
  What do the Convolutional Layers learn and what do Pooling Layers learn?Copy
  10 Minutes
- 12.6
  What is the Calculation of Convolution Layers Input to output?Copy
  10 Minutes
- 12.7
  What are the Calculation of the Pooling Layers Input to Output?Copy
  10 Minutes
- 12.8
  What is ReLU and what is it doing here on the Conv Layers in this Project?Copy
- 12.9
  How does the model optimisation happen through the CNN learning EpochsCopy
  12 Minutes
- 12.10
  Classification Error and Metrics [Video]Copy
  10 Minutes
- 12.11
  Model Coding [Video]Copy
  8 Minutes
- 12.12
  What is the Model PickleCopy
  5 Minutes
- 12.13
  Streamlit Deployment [Video]Copy
  10 Minutes
- 12.14
  Streamlit FeaturesCopy
  7 Minutes
- 12.15
  Code ZIPCopy
  10 Minutes
Building a Movie Recommendation System with NLP and K-Means Clustering (with full Code)
0
Overview of Project
13
- 14.1
  Introduction [with Video]Copy
  10 Minutes
- 14.2
  Project Scope and ObjectivesCopy
  5 Minutes
- 14.3
  Learning ObjectivesCopy
  10 Minutes
- 14.4
  Step by Step ApproachCopy
  8 Minutes
- 14.5
  Study AlongCopy
  2 Minutes
- 14.6
  Use of ChatGPTCopy
  2 Minutes
- 14.7
  Project CodeCopy
  2 Minutes
- 14.8
  Setting up your Project Development EnvironmentCopy
  3 Minutes
- 14.9
  Setting up your Local (Laptop/Desktop) EnvironmentCopy
  6 Minutes
- 14.10
  What are Recommender SystemsCopy
  10 Minutes
- 14.11
  Our Approach to building the Movie RecommenderCopy
  12 Minutes
- 14.12
  Examples of Real-life Recommender SystemsCopy
  8 Minutes
- 14.13
  Netflix’s Recommender SystemCopy
  12 Minutes
Exploratory Data Analysis (EDA) Phase
7
- 15.1
  Initial StudyCopy
  5 Minutes
- 15.2
  Import Input Data [Video]Copy
  10 Minutes
- 15.3
  Step by Step Approach of CodingCopy
  10 Minutes
- 15.4
  Understand K-Means Clustering 1 – Modelling Concepts [Video]Copy
  20 Minutes
- 15.5
  Understand K-Means Clustering 2 – Optimisation [Video]Copy
  3 Minutes
- 15.6
  Understand TF-IDF Vectorisation [Video]Copy
- 15.7
  Recommender System Coding Explanation [Video]Copy
  50 Minutes
Code Files and Project Submission
2
- 16.1
  Project Final Submission – Movie Recommendation using K-MeansCopy
  3 Days
- 16.2
  Code ZIPCopy
  10 Minutes
Twitter (X) Sentiment Analysis using NLP and Logistic Regression + Streamlit UI (with full Code)
0
Getting Started and Preprocessing using NLP
12
- 18.1
  Project Overview [Video]Copy
  12 Minutes
- 18.2
  Steps to be followed in the ProjectCopy
  6 Minutes
- 18.3
  About the Input Data [Video]Copy
  10 Minutes
- 18.4
  Tweet Cleaning StepsCopy
  10 Minutes
- 18.5
  Noise in Text DataCopy
  10 Minutes
- 18.6
  Use of NLP (Natural Language Processing) Techniques in the ProjectCopy
  15 Minutes
- 18.7
  StemmingCopy
  10 Minutes
- 18.8
  LemmatizationCopy
  10 Minutes
- 18.9
  Stop Words RemovalCopy
  10 Minutes
- 18.10
  EDA Concepts [Video]Copy
  15 Minutes
- 18.11
  EDA and Preprocessing Coding Explanation [Video]Copy
  20 Minutes
- 18.12
  Text Tokenization [Video]Copy
  25 Minutes
Create Text Embeddings and Sentiment Modelling
10
- 19.1
  Bag of Words and TF-IDF Concepts [Video]Copy
  15 Minutes
- 19.2
  Bag of Words (BoW) Representation / Embeddings [Video]Copy
  20 Minutes
- 19.3
  TF-IDF (Term Frequency-Inverse Document Frequency) Representation / Embeddings [Video]Copy
  12 Minutes
- 19.4
  Difference between BoW and TF-IDFCopy
  10 Minutes
- 19.5
  Logistic Regression Algorithm [Video]Copy
  20 Minutes
- 19.6
  Classification Errors and Metrics [Video]Copy
  10 Minutes
- 19.7
  How does Logistic Regression fit / train on the Data + Coding [Video]Copy
  20 Minutes
- 19.8
  Classification MetricsCopy
  10 Minutes
- 19.9
  Streamlit UI [Video]Copy
  10 Minutes
- 19.10
  Python Code for ProjectCopy
Credit Card Default Prediction using Random Forest (with full Code)
0
Getting Started, EDA and Pre-Processing
11
- 21.1
  Project Overview [Video]Copy
  8 Minutes
- 21.2
  Project ObjectivesCopy
  5 Minutes
- 21.3
  Learning ObjectivesCopy
- 21.4
  Study GuideCopy
  5 Minutes
- 21.5
  How to Go AboutCopy
  3 Minutes
- 21.6
  Setting up your EnvironmentCopy
  10 Minutes
- 21.7
  About the Input Data [Video]Copy
  10 Minutes
- 21.8
  EDA and Pre-processing [Video]
  10 Minutes
- 21.9
  EDA Coding [Video]Copy
  20 Minutes
- 21.10
  Class Imbalance and SMOTECopy
- 21.11
  Train-Test SplitCopy
Random Forest Modelling and Evaluation
11
- 22.2
  What is Random Forest?Copy
  10 Minutes
- 22.3
  Random Forest Training ProcessCopy
  12 Minutes
- 22.4
  Decision Tree Concepts [Video]Copy
  20 Minutes
- 22.5
  Random Forest Explained [Video]Copy
- 22.6
  Classification Errors and Metrics [Video]Copy
  10 Minutes
- 22.7
  Understanding Classification Metrics [Video]Copy
  15 Minutes
- 22.8
  Hyperparameter Tuning in Random Forest (Pruning)Copy
- 22.9
  Random Forest Modelling – Coding [Video]Copy
  30 Minutes
- 22.10
  Understand Grid-Search Cross ValidationCopy
  15 Minutes
- 22.11
  Feature Importance in Random ForestCopy
  15 Minutes
- 22.12
  Python Code for ProjectCopy

Overview
Curriculum

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