Learn Numpy, Pandas, Matplotlib, Seaborn, Scipy, Supervised & Unsupervised Machine Learning A-Z and feature engineering
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,K-Nearest Neighbor(KNN) Algorithm,Support Vector Machine Algorithm,Random Forest Algorithm
- Feature engineering
- Python Basics
This course includes:
- 11 hours on-demand video
- 29 downloadable resources
- Full lifetime access
- Access on mobile and TV
- Certificate of completion
- No previous programming experience needed.
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
- What is Supervised Machine Learning
- 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.
Who this course is for:
- Anyone interested in Machine Learning.
- Any students in college who want to start a career in Data Science.
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