Generative AI

1. What is Artificial Intelligence
2. History of Artificial Intelligence
3. Use Cases of Artificial Intelligence
4. Role of Machine Learning Engineer
5. Machine Learning Tools & Packages

1. Python Basics
2. Python Packages
3. Working with NUMPY
4. Working with Pandas
5. Introduction to Data Visualization
6. Introduction to Matplotlib and Seaborn

1. Introduction to Data preprocessing
2. Importing the Dataset
3. Handling Missing data
4. Working with Categorical Data
5. Splitting the data into Train and Test set
6. Feature Scaling

1. The Neuron
2. The Activation Function
3. How do Neural Networks work?
4. How do Neural Networks learn?
5. Gradient Descent
6. Stochastic Gradient Descent
7. Backpropagation

1. Introduction to Tensorflow & Keras Framework
2. Introduction to the Sequential Mode
3. Activation functions
4. Layers
5. Training
6. Loss function
7. Building ANN Using Tensor flow
8. Evaluating Improving and Tuning ANN
9. (Live Project)

1. Introduction to Convolutional Neural Networks
2. What are convolutional neural networks?
3. Step 1 - Convolution Operation
4. Step 2 - Pooling
5. Step 3 - Flattening
6. Step 4 - Full Connection
7. Live project
8. Evaluating, Improving, and Tuning the CNN

1. Introduction to Natural Language Processing
2. Introduction to NLTK
3. Bag of Words model
4. Natural Language Processing in Python
5. Sentiment analysis using Natural Language Processing
6. Live Project

1. Introduction to Generative AI
2. Types of Generative AI
3. Hands-on Activity: GAN Paint
4. Generative AI tools
5. GenAI vs. Gemini
6. Introduction to Replit
7. Live Project