What you'll learn:
- Data Science libraries like Numpy , Pandas , Matplotlib, Scipy, Scikit Learn, Seaborn , Plotly and many more
- Machine learning Concept and Different types of Machine Learning
- Machine Learning Algorithms like Regression, Classification, Naive Bayes Classifier, Decision Tree, Support Vector Machine Algorithm etc..
- Feature engineering
- Python Basics
Artificial Intelligence is the next digital frontier, with profound implications for business and society. The global AI market size is projected to reach $202.57 billion by 2026, according to Fortune Business Insights.
This Data Science & Machine Learning (ML) course is not only ‘Hands-On’ practical based but also includes several use cases so that students can understand actual Industrial requirements, and work culture. These are the requirements to develop any high level application in AI.
In this course several Machine Learning (ML) projects are included.
1) Project - Customer Segmentation Using K Means Clustering
2) Project - Fake News Detection using Machine Learning (Python)
3) Project COVID-19: Coronavirus Infection Probability using Machine Learning
4)Project - Image compression using K-means clustering | Color Quantization using K-Means
This course include topics ---
What is Data Science
Describe Artificial Intelligence and Machine Learning and Deep Learning
Concept of Machine Learning - Supervised Machine Learning , Unsupervised Machine Learning and Reinforcement Learning
Python for Data Analysis- Numpy
Working envirnment-
Google Colab
Anaconda Installation
Jupyter Notebook
Data analysis-Pandas
Matplotlib
What is Supervised Machine Learning
Regression
Classification
Multilinear Regression Use Case- Boston Housing Price Prediction
Save Model
Logistic Regression on Iris Flower Dataset
Naive Bayes Classifier on Wine Dataset
Naive Bayes Classifier for Text Classification
Decision Tree
K-Nearest Neighbor(KNN) Algorithm
Support Vector Machine Algorithm
Random Forest Algorithm I
What is UnSupervised Machine Learning
Types of Unsupervised Learning
Advantages and Disadvantages of Unsupervised Learning
What is clustering?
K-means Clustering
Image compression using K-means clustering | Color Quantization using K-Means
Underfitting, Over-fitting and best fitting in Machine Learning
How to avoid Overfitting in Machine Learning
Feature Engineering
Teachable Machine
Python Basics
In the recent years, self-driving vehicles, digital assistants, robotic factory staff, and smart cities have proven that intelligent machines are possible. AI has transformed most industry sectors like retail, manufacturing, finance, healthcare, and media and continues to invade new territories. Everyday a new app, product or service unveils that it is using machine learning to get smarter and better.
NOTE :- In description reference notes also provided , open reference notes , there is Download link. You can download datasets there.
