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NSDC AI Data Science and Artificial Intelligence in Malayalam

Course Currilcum

    • Kickstart Your Journey in Data Science 00:25:46
    • Knowing and Outcome Of Big Data 00:30:59
    • Structured And Unstructured Data,Hadoop Made Simple 00:55:15
    • Python Basics 00:27:52
    • Variables,Keywords and Identifier 00:25:51
    • Python Operators 00:15:46
    • Conditional statement and Looping 00:43:10
    • Quiz-1 00:03:00
    • Loop Exercises 00:42:25
    • Jump Statements,String Operations 00:01:02
    • List,Tuple,Dictionaries,Set 00:03:31
    • Excercises 00:34:49
    • Functions 00:48:07
    • Lambda and Map Function 00:29:50
    • Modules and Packages 00:25:20
    • Filter function 00:31:33
    • List Comprehension 00:40:19
    • Regular Expression Part 1 00:29:10
    • Regular Expression Part 2 00:34:32
    • Exceptional Handling 00:28:36
    • Files Part-1 00:34:07
    • Files Part-2 00:26:09
    • Itertools 00:46:37
    • OOPs: Intro, Class and Objects 00:29:02
    • abstraction 00:30:41
    • Encapsulation and inheritance 00:47:01
    • Inheritance Excercise 00:25:54
    • Quiz-2 00:03:00
    • Polymorphisam 00:32:28
    • python-excercise-all 00:26:08
    • MySQL installation 00:20:52
    • Creation of tables 00:06:31
    • SQL Query 00:26:26
    • Aggregate function 00:36:45
    • Group By 00:31:38
    • Distinct 00:32:00
    • Update 00:23:37
    • Joins Part1 00:29:57
    • Joins Part2 00:35:28
    • Machine Learning and Numpy Introduction 00:20:21
    • One dimensional Array Creation 00:45:26
    • Two dimensional Array Creation 00:24:49
    • Three Dimensional Array Creation 00:01:16
    • Numpy functions 00:18:29
    • Pandas Introduction 00:18:29
    • Creation of Series 00:48:43
    • Creation of Data Frame 00:51:20
    • Pandas Functions Part 1 00:02:29
    • Pandas Functions Part 2 00:01:21
    • Machine learning introduction 00:27:05
    • Data Types 00:36:36
    • Supervised Machine Learning Introduction 00:32:09
    • EDA part 1 00:35:25
    • EDA part 2 00:28:29
    • Overfitting & Regularization (Dropout, Early Stopping, Batch Normalization) 00:24:30
    • RNN (Recurrent Neural Network) & LSTM for Sequential Data 00:30:52
    • Deep Learning Model Evaluation & Optimization 00:32:18
    • Advanced Matplotlib & Seaborn Techniques 00:34:04
    • Plotly & Cufflinks – Interactive Visualizations 00:18:56
    • KNN 00:36:44
    • KNN Excercise 00:41:44
    • Seaborn Introduction 00:44:21
    • Seaborn Part 2 00:20:32
    • Performance measures 00:36:40
    • SVC 00:21:23
    • SVC-Excercise 00:34:16
    • Simple Linear Regression 00:34:38
    • Performance measures-Regreesion 00:31:25
    • Polynomial Regression 00:23:55
    • Multiple Linear Regression 00:37:06
    • Naive Bayers Introduction 00:33:48
    • Naive Bayers Excercise – Part 1 00:22:37
    • Naive Bayers Excercise – Part 2 00:04:56
    • Handling Imbalanced data 00:26:30
    • Under Sampling 00:25:25
    • Decision trees 00:22:33
    • Decision trees-Excercise 00:29:59
    • Ensemble Machine learning model 00:31:16
    • Adaboost 00:35:16
    • Gradient Boost 00:07:04
    • XG Boost 00:30:14
    • Hyper parameter Tuning-Grid Search CV 00:26:02
    • Randomized Search CV 00:30:02
    • Visualization With MatPlotlib 00:19:50
    • Introduction 00:21:06
    • KMeans Clustering 00:31:51
    • Agglomerative Clustering 00:16:42
    • Dendrogram 00:26:52
    • Divisive Analysis 00:19:38
    • Feature Engineering and Feature Selection 00:24:42
    • Cross-Validation and Model Evaluation Techniques 00:12:14
    • Advanced Ensemble Methods (Stacking, Blending, Voting Classifiers) 00:21:05
    • Model Interpretability (SHAP, LIME) 00:25:48
    • Bias and Variance Tradeoff 00:20:31
    • Model Deployment Basics (Pickle, Joblib) 00:26:07
    • Introduction to Neural Networks 00:31:43
    • Activation Functions and Gradient Descent 00:34:31
    • Forward and Backpropagation Explained 00:12:41
    • Building ANN (Artificial Neural Network) with TensorFlow/Keras 00:26:48
    • CNN (Convolutional Neural Network) for Image Data 00:25:20
    • Power BI / Tableau Introduction 00:15:23
    • Building Dynamic Dashboards with Plotly Dash / Streamlit 00:11:04
    • Storytelling with Data – How to Present Insights 00:14:21
    • Quiz-3 00:03:00
    • What is AI and how it differs from ML & Data Science 00:16:52
    • Real-world AI applications in data science (healthcare, marketing, etc.) 00:29:43
    • Components of Data Science (data collection, cleaning, modeling, evaluation) 00:43:55
    • Python basics recap (variables, data types, loops, functions) 00:12:56
    • Importing and exploring datasets 00:27:47
    • Data cleaning (nulls, duplicates) 00:22:25
    • Feature selection and data transformation 00:29:11
    • Hands-o: Load and explore a real dataset(csv) 00:51:00
    • Supervised and unsupervised 00:01:35
    • Overview of ML pipeline 00:37:44
    • Algorithms: Linear Regression, Decision Trees 00:36:37
    • Hands-on: Build a simple Linear Regression model using Scikit-learn 00:02:40
    • Logistic Regression, KNN, Random Forest 00:22:56
    • Quiz-4 00:03:00
    • Model accuracy, precision, recall, F1-score 00:27:02
    • Hands-on: Predict target variable from real dataset (e.g., Titanic dataset 00:07:57
    • What is a Neural Network? 00:23:13
    • Auto creation of materials 00:31:45
    • Tools: TensorFlow/Keras introduction 00:23:02
    • 19 Hands-on: Simple ANN model for binary classification 00:42:57
    • Basics of image classification (CNN overview) 00:39:47
    • Confusion Matrix, ROC Curve 00:47:53
    • Cross-validation, overfitting/underfitting 00:20:46
    • Hyperparameter tuning (GridSearchCV) 00:18:23
    • Hands-on: Improve model accuracy using parameter tuning 00:14:32
    • Problem Statement: Predict outcome (e.g., loan approval, disease detection) 00:13:34
    • Data loading and cleaning 00:52:17
    • Feature selection 00:13:20
    • Model training and evaluation 00:28:31
    • Results interpretation 00:24:02
    • Intro to Streamlit / Flask 00:40:58
    • Convert ML model to web app 00:51:56
    • Hands-on: Simple ML model deployed using Streamlit 00:07:23
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