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ACM BPDC

🧠 Imagine | 👨🏻‍💻 Implement | 🚀 Innovate

Project Truffle

This is project Truffle. Here's how to eat it:

  • Take a small bite by exploring the contents listed below.
  • Be patient. Let the truffle melt as it enriches you with the knowledge of AI.
  • Have another bite... We have a lot to offer!
  • Start your AI journey by having a Truffle.

Clearing the Confusion: AI vs ML vs DL

AI


Evolution of AI

AI


📕 Table of Contents

📆  More topics coming soon!

🤖 Overview of Machine Learning

AI

Data Preprocessing
  1. Importing the dataset
  2. Missing Data
  3. Categorical Data
  4. Splitting the dataset into Training and Test Set
  5. Feature Scaling
Regression
  1. Simple Linear Regression
  2. Multi-Linear Regression
  3. Polynomial Regression
  4. SVR
  5. Desicion Tree
  6. Random Forest
Classification
  1. Logistic Classification
  2. K-Nearest Neigbors
  3. Support Vector Machine
  4. Kernel SVM
  5. Naive Bayes
  6. Desicion Tree
  7. Random Forest
Clustering
  1. K-Means Clustering
  2. Hierarchical Clustering
Association Rule Learning
  1. Apriori
  2. Eclat
Reinforcement Learning
  1. Upper Confidence Bound (UCB)
  2. Thompson Sampling
Deep Learning
  1. Artificial Neural Networks
  2. Convolutional Neural Network
Dimensionality Reduction
  1. Principal Component Analysis (PCA)
  2. Linear Discriminant Analysis (LDA)
  3. Kernal PCA
Model Selection & Boosting
  1. K-Fold Cross Validation
  2. Grid Search
  3. XG-Boost

🗺️ Roadmaps

The purpose of these roadmaps is to give you an idea about the landscape and to guide you if you are confused about what to learn next and not to encourage you to pick what is hip and trendy. You should grow some understanding of why one tool would better suited for some cases than the other and remember hype and trendy never means best suited for the job.

🎓 Higher Degrees

💸 Artificial Intelligence (AI): Salaries Heading Skyward

Artificial intelligence salaries benefit from the perfect recipe for a sweet paycheck: a hot field and high demand for scarce talent. It’s the ever-reliable law of supply and demand, and right now, anything artificial intelligence-related is in very high demand.

While the average salary for a Software Engineer is around $100,000 to $150,000, to make the big bucks you want to be an AI or Machine Learning (Specialist/Scientist/Engineer). Read more about it here.

Therefore, to be an effective and in-demand AI developer, you need a lot of skills, not just one or two. Here is list of the top 10 skills you need to know for AI:

1. Machine Learning

2. Python

3. Statistics

4. Data Science

5. Hadoop

6. Big Data

7. Java

8. Data Mining

9. Spark

10. R

⚙️ Contribution Guidelines

  • Contributions are always welcome!
  • Please read the Contribution guidelines first.
  • You can find our Contributing guidelines here.

🔐 Code Of Conduct

You can find our Code of Conduct here.

🤝 How to Share

🧑‍🤝‍🧑 Connect with us: