Data Science 2025 Vaidhik Batch

Course Instructor Teachnook Team

FREE

Course Overview

Schedule of Classes

Start Date & End Date

Jun 01 2025 - Jul 31 2025

Course Curriculum

1 Subject

Data Science 2025 Vaidhik Batch

26 Learning Materials

Recorded Sesssions

Class 1 - Introduction to Data Science/Python Basics

Video

00:45:18

Class 2 - Python Basics: Variables, data types, loops, conditions, & functions.

Video

01:01:16

Class 3 - Data Acquisition: Data sources, data formats, Methods to collect and clean data

Video

01:00:04

Class 4 - Data Exploration: Descriptive statistics, data visualization, & correlation analysis

Video

01:03:58

Class 5 - Data Preparation: Data cleaning, feature scaling, encoding categorical data.

Video

01:05:50

Class 6 - Overview of machine learning, types of machine learning algorithms, & supervised learning.

Video

00:58:43

Class 7 - data cleaning project using Python & Pandas during the live session.

Video

01:03:38

Class 8 - Linear Regression: Simple linear regression, multiple linear regression

Video

01:22:02

Class 9 - Classification: Logistic regression, K-Nearest Neighbors, & model evaluation

Video

01:30:55

Class 10 - Decision Trees: Introduction to decision trees, Gini index, & Information gain.

Video

01:08:58

Class 11 - Random Forest: Introduction to random forests, bagging, and boosting.

Video

01:02:56

Class 12 - supervised learning project using scikit-learn during the live session.

Video

01:02:37

Class 13 - Introduction to unsupervised learning, clustering algorithms, and K-Means clustering.

Video

01:00:43

Class 14 - Introduction to principal component analysis (PCA) and t-Distributed (t-SNE)

Video

01:48:33

Class 15 - Introduction to NLP, tokenization, stemming, & lemmatization

Video

01:13:06

Class 16 - Introduction to sentiment analysis, preprocessing, feature extraction.

Video

00:55:40

Class 17 - Introduction to text classification, bag-of-words model, and Naive Bayes.

Video

01:11:11

Class 18 - text classification project using NLP techniques during the live session.

Video

02:05:10

Class 19 - Introduction to artificial neural networks, perceptron

Video

02:05:49

Class 20 - neural network project using TensorFlow during the live session.

Video

02:04:23

Class 21 - Introduction to CNN, convolutional layers, & pooling layers.

Video

01:12:12

Class 22 - CNN project using TensorFlow during the live session.

Video

00:47:50

Class 23 - Recurrent Neural Networks (RNN): Introduction to RNN, LSTM, and GRU.

Video

01:06:16

Class 24 -Introduction to time series analysis, trend, seasonality, and autocorrelation.

Video

01:06:16

Class 25 - RNN project using TensorFlow during the live session.

Video

01:04:09

Class 26 -Introduction to forecasting, moving average, exponential smoothing

Video

01:20:49

Course Instructor

Teachnook Team

577 Courses • 42164 Students

Data Science 2025 Vaidhik Batch

Course Overview

Schedule of Classes

Jun 01 2025 - Jul 31 2025

Course Curriculum

Data Science 2025 Vaidhik Batch

Recorded Sesssions

Class 1 - Introduction to Data Science/Python Basics

Class 2 - Python Basics: Variables, data types, loops, conditions, & functions.

Class 3 - Data Acquisition: Data sources, data formats, Methods to collect and clean data

Class 4 - Data Exploration: Descriptive statistics, data visualization, & correlation analysis

Class 5 - Data Preparation: Data cleaning, feature scaling, encoding categorical data.

Class 6 - Overview of machine learning, types of machine learning algorithms, & supervised learning.

Class 7 - data cleaning project using Python & Pandas during the live session.

Class 8 - Linear Regression: Simple linear regression, multiple linear regression

Class 9 - Classification: Logistic regression, K-Nearest Neighbors, & model evaluation

Class 10 - Decision Trees: Introduction to decision trees, Gini index, & Information gain.

Class 11 - Random Forest: Introduction to random forests, bagging, and boosting.

Class 12 - supervised learning project using scikit-learn during the live session.

Class 13 - Introduction to unsupervised learning, clustering algorithms, and K-Means clustering.

Class 14 - Introduction to principal component analysis (PCA) and t-Distributed (t-SNE)

Class 15 - Introduction to NLP, tokenization, stemming, & lemmatization

Class 16 - Introduction to sentiment analysis, preprocessing, feature extraction.

Class 17 - Introduction to text classification, bag-of-words model, and Naive Bayes.

Class 18 - text classification project using NLP techniques during the live session.

Class 19 - Introduction to artificial neural networks, perceptron

Class 20 - neural network project using TensorFlow during the live session.

Class 21 - Introduction to CNN, convolutional layers, & pooling layers.

Class 22 - CNN project using TensorFlow during the live session.

Class 23 - Recurrent Neural Networks (RNN): Introduction to RNN, LSTM, and GRU.

Class 24 -Introduction to time series analysis, trend, seasonality, and autocorrelation.

Class 25 - RNN project using TensorFlow during the live session.

Class 26 -Introduction to forecasting, moving average, exponential smoothing

Course Instructor

Teachnook Team

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