Machine Learning | Batch 350 | M0100366
FREE
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Course Overview
Schedule of Classes
Start Date & End Date
Jun 02 2025 - Jun 06 2025
Total Classes
47 Classes
Course Curriculum
1 Subject
Machine Learning | Batch 350 | M0100366
9 Exercises • 76 Learning Materials
Phase 1 - Machine Learning and Preprocessing Data
What is machine learning vs Traditional approach, hoe ML approach make task simple and versatile
Types of ML algorithm
Model building, data understanding and processing with sattistical use
Model building data understanding and processing with statistical use
one_hot_encoding
outliers_handling
3rd March - What is machine learning vs Traditional approach, hoe ML approac
5th March - Types of ML algorithm
declair data ready for ML
Melbourne_housing_data_regression analysis
polynomial_regression
regression_assumptions
end_to_end_model application_steps
17th March - Data Preprocessing
18th March - Data Preprocessing with sklearn
19th March - Linear Regression Algorithm
20th March - Implementation of Linear Regression algorithm
21st March - Implementing multiple Linear Regression
24th March - Regularization and introduction to Logistic regression
logistic regression
25th March - Logistic Regression algorithm
26th March - Performance measures in classification
27th March -
8th_April_NLP_TextPreProcessing
9th_April_Text_Transformation
10th_April_NaiveBayes_Spamhamclassification
11th_April_Probability
14th_April_naivebayes and its limitations AND auc_roc curve,logloss
8th April - Introduction to Natural Language Processing
9th April - "Text Preprocessing "
10th April - "Text preprocessing and implementation of Spam and Ham case problem us
11th April - Understanding of Probability and Naive Bayes
14th April - Naive bayes and its limitations AND auc_roc curve,logloss
15th April - Mathematical foundation for ML
17th April - Mathematical Foundation continue
15_17th_April_Mathematical_Foundations
21st April - KNN Regressor
22nd April - Decision Tree Inroduction
23rd April - Decision Tree- Entropy and Information gain
24th April - Gini Index
25th April -
17th_April_KNN_Classification
21st_April_KNN_Regression
22nd_25th_April_Decision_Tree
29th April - Introduction to SVM
30th April -
2nd May -
5th May -
6th May -
7th May -
8th May -
9th May -
14th May -
15th May -
19th May -
20th May -
21st May -
23rd May -
29th_30th_April_SVM_classification
SVR_Regresssion_2nd_5th_May
Ensemble_techniques_5th_7th_May
Model_selection_8th May_part1
Model_selection_9th May_part2
Hyperparameter Tuning_15th_May
Feature selection and Transformation-15th-17th-May
Image-20-26th-May
27th_May_1st_June_Unsupervised_Learning
26th May
27th May
28th May
30th May
2nd June Part 1
2nd June Part 2
3rd June
4th June
5th June
Assignment
Machine Learning: Blog Writing Challenge
Assignment- Linear Regression
Assignment- Logistic Regression
Assignment- NLP
KNN: Blog Writing Challenge
Assignment- KNN
Assignment- Decision Tree
Unsupervised Machine Learning project
Job_fitment_FIB_practice
Phase 2- Deep Dive into ML Algorithms
Course Instructor
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Innomatics Research Labs
553 Courses • 35131 Students
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DS Mentor
244 Courses • 8036 Students
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