1 - Introduction to Course
2 - What is Machine Learning
3 - Life Cycle
4 - Introduction to Numpy Library
5 - Creating Arrays from Scratch
6 - Creating Arrays from Scratch Continued
7 - Array Indexing and Slicing
8 - Numpy Array Functions and Shape Modification
9 - Mathematical Operations on Numpy Arrays
10 - Introduction to Pandas Library
11 - Working with Pandas DataFrames
12 - Slicing and Indexing with Pandas
13 - Create DataFrame and Explore Dataset
14 - Data Analysis with Pandas DataFrame
15 - Other Useful Methods in Pandas Library
16 - Introduction to Matplotlib
17 - Customizing Line Plots
18 - Create Plot Using DataFrame
19 - Standard Scaler to Scale the Data
20 - Encoding Categorical Data
21 - Sklearn Pipeline and Column Transformer
22 - Evaluation Metrics in Sklearn
23 - Linear Regression
24 - Evaluation of Linear Regression Model
25 - Polynomial Regression
26 - Polynomial Regression Continued
27 - Sklearn Pipeline Polynomial Regression
28 - Decision Tree Classifier
29 - Decision Tree Evaluation
30 - Random Forest
31 - Support Vector Machines
32 - Kmeans Clustering
33 - KMeans Clustering Hands On
34 - Data Loading and Analysis
35 - Dimensionality Reduction with PCA
36 - Hyper Parameter Tuning
37 - Summary
