The document provides an overview of artificial intelligence (AI) and its subset, machine learning (ML), explaining their definitions, differences, and applications. It outlines the machine learning life cycle, types of ML, popular algorithms, and introduces neural networks and deep learning. Additionally, it covers TensorFlow, an open-source ML library by Google, highlighting its features, concepts, and applications in various domains.

1. Session on
TensorFlow
By GDGC, BBDNIIT
2024 -25

2. Impact of AI in the world

3. What is Artificial Intelligence?
AI is a broad field of computer science focused on creating intelligent
machines that can simulate human-like thinking and behavior.
Aims to develop systems capable of performing tasks that typically require
human intelligence.
Encompasses reasoning, problem-solving, learning, perception, and
language understanding.
Example:- Virtual assistants (e.g., Siri)

4. What is Machine Learning?
A subset of AI that focuses on enabling computers to learn and improve
from experience without being explicitly programmed.
Uses statistical techniques to help machines "learn" from data.
Allows systems to automatically improve their performance over time.
Example:- Spam email detection etc

5. Difference between AI and ML
Artificial Intelligence Machine Learning
Broader concept of creating intelligent
machines.
Specific approach to achieving AI through
data and algorithms.
Aims to create human-like intelligence. Focuses on learning patterns from data.
Includes reasoning and problem-solving.
Primarily concerned with statistical
learning.
Can be rule-based or learning-based. Strictly data-driven learning approach.
Example: Robotics, Expert Systems.
Example: Prediction models,
Classification

6. Machine Learning Life Cycle

7. Machine Learning Life Cycle
1. Problem Definition: Define the problem and set objectives.
2. Data Collection: Gather relevant data.
3. Data Preprocessing: Clean, transform, and prepare data.
4. Model Building: Choose and train an algorithm.
5. Model Evaluation: Test model performance.
6. Deployment: Deploy the model into production.
7. Monitoring: Track model performance and improve iteratively.

8. Types of Machine Learning
Supervised Learning
Unsupervised Learning
Semi-supervised Learning
Reinforcement Learning

9. Supervised Learning
Learning with labeled training data
Algorithm learns from input-output pairs
Goal is to predict outcomes for new, unseen data
Some key algorithms include: Linear & Logistic Regression, Decision
Trees, Support Vector Machines (SVM) etc.

10. Unsupervised Learning
Learning from un-labeled data
Discovers hidden patterns or intrinsic structures
No predefined outputs
Some key algorithms include: K-Means & Hierarchical Clustering,
Principal Component Analysis (PCA), Apriori Algorithm etc.

11. Semi-Supervised Learning
Combination of labeled and unlabeled data.
Uses small amount of labeled data with large unlabeled dataset.
Useful when labeling data is expensive or time-consuming.
Some key algorithms include: Self-training, Co-training, Graph-based
label propagation etc.

12. Reinforcement Learning
Learning through interaction with environment.
Agent learns to make decisions by receiving rewards/penalties.
Goal is to maximize cumulative reward.
Some key algorithms include: Q-Learning, Deep Q Network (DQN),
Actor-Critic Methods etc.

13. Popular Machine Learning Algorithms
 Regression Algorithms:-
 Linear Regression-
■ Predicts continuous numerical values
■ Establishes linear relationship between variables
 Polynomial Regression-
■ Captures non-linear relationships
■ Adds polynomial features to linear regression

14. Popular Machine Learning Algorithms
 Regression Algorithms:-
 Logistic Regression-
■ Used for binary classification
■ Predicts probability of an instance belonging to a class

15. Popular Machine Learning Algorithms
 Classification Algorithms:-
 Decision Trees-
■ Tree-like model of decisions
■ Splits data based on feature conditions

16. Popular Machine Learning Algorithms
 Classification Algorithms:-
 Support Vector Machines (SVM) -
■ Creates hyperplane to separate classes
■ Effective in high-dimensional spaces

17. Popular Machine Learning Algorithms
 Classification Algorithms:-
 Random Forest -
■ Creates hyperplane to separate classes
■ Effective in high-dimensional spaces

18. Popular Machine Learning Algorithms
 Classification Algorithms:-
 K-Nearest Neighbours (KNN) -
■ Classification based on nearest training examples.
■ Simple, non-parametric algorithm.

19. Popular Machine Learning Algorithms
 Clustering Algorithms:-
 K-Means Clustering -
■ Partitions data into K clusters.
■ Minimizes within-cluster variance.

20. Popular Machine Learning Algorithms
 Dimensionality Reduction:-
 Principal Component Analysis (PCA)-
■ Reduces feature dimensions.
■ Preserves maximum variance.

21. Popular Machine Learning Algorithms
 Some advanced Algorithms:-
 Gradient Boosting-
■ Creates strong predictive models.
■ Builds models sequentially.
 XGBoost -
■ Optimized gradient boosting.
■ High performance and scalability.

22. Basic code snippet for making ML model

23. Basic code snippet for making ML model

24. Basic code snippet for making ML model

25. Introduction to Neural Networks
What is a Neural Network?
● Computational model inspired by the human brain's neural structure.
● Consists of interconnected nodes (neurons) organized in layers.
● Capable of learning complex patterns and relationships in data.
Artificial Neural Network (ANN) –
● Computational system with interconnected processing nodes (artificial neurons).
● Designed to recognize patterns and solve complex problems.
● Consists of three main layers:
1. Input Layer: Receives initial data
2. Hidden Layer(s): Processes and transforms data
3. Output Layer: Produces final result

26. What is Deep Learning?
Advanced subset of machine learning using multi-layered neural networks.
Capable of learning hierarchical representations of data.
Uses multiple hidden layers to extract increasingly abstract features.
Example:- image recognition, natural language processing etc.

27. TensorFlow
Open-source machine learning library developed by Google Brain.
Primarily used for deep learning and neural network development.
Supports multiple programming languages (Python, C++, Java).
Provides flexible ecosystem for research and production

28. Features of TensorFlow
High scalability for large datasets and models.
Supports both CPU and GPU computations and extends its support to TPU
for faster computation. It also supports distributed computing.
Provides high-level APIs for easy model development such as pre-built
models and APIs like Keras.
Excellent visualization tools (TensorBoard).

29. Applications of TensorFlow
Image Recognition: Medical imaging, object detection.
Natural Language Processing: Chatbots, sentiment analysis.
Time Series Analysis: Forecasting stock prices.
Reinforcement Learning: Gaming AI, robotics.

30. Important TensorFlow Concepts
Tensors: Multidimensional arrays that form the fundamental data structure
in TensorFlow.
Graphs: Represent the computational flow of a model.
Variables: Store and update model parameters.
Placeholders: Input data to the model.

31. Important TensorFlow Concepts
Variables: Store and update model parameters.
Operations: Mathematical operations performed on tensors.
Loss Functions: Measure the model's error.
Optimizers: Adjust model parameters to minimize loss.

32. Basic TensorFlow Code Snippets
Basic Tensor Operations

33. Basic TensorFlow Code Snippets
Linear Regression Example

34. Basic TensorFlow Code Snippets
Building a Simple Neural Network

35. TensorFlow represents a powerful, flexible platform for
developing machine learning and deep learning
solutions across various domains. Understanding its
core principles and capabilities is crucial for modern AI
practitioners.
Conclusion