Course Overview
Schedule of Classes
Start Date & End Date
May 01 2025 - Sep 30 2025
Course Curriculum
1 Subject
Data Science MAY 2025 Vaidhik Batch
26 Learning Materials
Recorded Sesssions
Class 1 - Introduction to Data Science/Python Basics
Video
45:18
Class 2 - Python Basics: Variables, data types, loops, conditions, & functions.
Video
1:1:16
Class 3 - Data Acquisition: Data sources, data formats, Methods to collect and clean data
Video
1:04
Class 4 - Data Exploration: Descriptive statistics, data visualization, & correlation analysis
Video
1:3:58
Class 5 - Data Preparation: Data cleaning, feature scaling, encoding categorical data.
Video
1:5:50
Class 6 - Overview of machine learning, types of machine learning algorithms, & supervised learning.
Video
58:43
Class 7 - data cleaning project using Python & Pandas during the live session.
Video
1:3:38
Class 8 - Linear Regression: Simple linear regression, multiple linear regression
Video
1:22:2
Class 9 - Classification: Logistic regression, K-Nearest Neighbors, & model evaluation
Video
1:30:55
Class 10 - Decision Trees: Introduction to decision trees, Gini index, & Information gain.
Video
1:8:58
Class 11 - Random Forest: Introduction to random forests, bagging, and boosting.
Video
1:2:56
Class 12 - supervised learning project using scikit-learn during the live session.
Video
1:2:37
Class 13 - Introduction to unsupervised learning, clustering algorithms, and K-Means clustering.
Video
1:043
Class 14 - Introduction to principal component analysis (PCA) and t-Distributed (t-SNE)
Video
1:48:33
Class 15 - Introduction to NLP, tokenization, stemming, & lemmatization
Video
1:13:6
Class 16 - Introduction to sentiment analysis, preprocessing, feature extraction.
Video
55:40
Class 17 - Introduction to text classification, bag-of-words model, and Naive Bayes.
Video
1:11:11
Class 18 - text classification project using NLP techniques during the live session.
Video
2:5:10
Class 19 - Introduction to artificial neural networks, perceptron
Video
2:5:49
Class 20 - neural network project using TensorFlow during the live session.
Video
2:4:23
Class 21 - Introduction to CNN, convolutional layers, & pooling layers.
Video
1:12:12
Class 22 - CNN project using TensorFlow during the live session.
Video
47:50
Class 23 - Recurrent Neural Networks (RNN): Introduction to RNN, LSTM, and GRU.
Video
1:6:16
Class 24 -Introduction to time series analysis, trend, seasonality, and autocorrelation.
Video
1:6:16
Class 25 - RNN project using TensorFlow during the live session.
Video
1:4:9
Class 26 -Introduction to forecasting, moving average, exponential smoothing
Video
1:20:49
Course Instructor
Teachnook Team
567 Courses • 41580 Students
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