1
Introduction to Machine Learning
2
Nearest Neighbours & Distance Vectors
3
Understanding Metrices & Implement NN using NumPy
6
Regression in NN & Common Datasets
8
Unsupervised Learning using KMeans
9
Data Preprocessing for Machine Learning
10
Using Preprocessing in ML Applications
11
Introduction to Linear Models
13
implement Linear Regressions & Gradient Descent
16
Using Linear Regression in Applications
21
Cross Validation and Hyper Parameter
23
Pipeline & ColumnTransformer for ML Model
24
Case Studies for Bias, Variance, Validation and Hyper-parameters
25
Dealing Heterogenous Data
26
Fundamentals of Decision Tree
28
Understand Decision Tree Algorithm
30
Implement Regression and Classification DC in Python
32
Natural Language Processing
34
Conditional Probability & Bayes Theorem
35
Types of Naive Bayes & Applications
46
Handling of Outliers and imbalanced classes
50
CNN for Image Classification
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