Journey from Basics of Python to Machine Learning

Machine Learning using Python | Webinar in association with Diazonic Labs
Day 1:- Getting Started with Python
Python Core –
Introduction to Artificial Intelligence
Python IDLE and Jupyter Notebook
Variables, Functions and Objects
Lists, Tuples and Dictionaries
Day 2 – Machine Learning Basics ( Numpy , Matplotlib and Pandas Library)
Normalization technique
Arrays using Numpy
Libraries in Python for Numerical Analysis and Visualization
Scatter plot,Line plot and Bar Graph using matplotlib
Day 3 – Linear Regression
Types of ML – Supervised vs Unsupervised
Usage of Sklearn library in ML
Creating Dataframe using Pandas
DataFrames vs Series
Accessing the values from DataFrame
Creating CSV Files
Explaining ML Steps
Day 4 – Logistic Regression
Least Square Method(Maths behind Logistic Regression)
Linear Regression using Sklearn
Dividing into train and test
Plotting the best fit line
Coding in Sklearn
What is Joblib?
Day 5 – KNN Algorithm
K-Nearest Neighbor Algorithm mathematical concept
KNN for Binomial Category
Normalization and Scaling
Confusion Matrix
Euclidean Distance and Root Mean Square Formula
Coding for KNN Algorithm
Day 6 – K-Means Algorithm
Kmeans Algorithm – mathematical concept
Unsupervised Learning Algorithms
Clustering forms
Elbow Method
Euclidean and Manhattan Distance
Centroid concept
Coding in Sklearn for Kmeans
Day 7 – Capstone Project
Capstone Project on Classification problem using the real world Dataset
Exploratory Data Analysis
Graphical Representation of Data
Applying Normalization and Accuracy Model
Hands-on coding will be done on Google Colab. No software installation required.

Duration: 1:30 hrs daily

Webinar Date: 28th May to 03rd June

Webinar Timings:- 05:00 PM to 06:30 PM (DAILY) (Subject to change)

Webinar Fees: Rs. 400/- only(Per Attendee)

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