This document provides an overview of a machine learning workshop covering generative AI, TensorFlow, Pandas, and machine learning concepts. The workshop is led by Mudassir Shaikh and covers topics such as generative AI models, the types and applications of machine learning, an introduction to TensorFlow and its architecture, and an overview of the Pandas library for data manipulation in Python. The document includes summaries and definitions for each topic discussed in the one-day workshop.

1. Machine Learning with
TensorFlow
Shaikh M. Mudassir,
Mumbai, India,
13th Jan, 2024.

2. Day 1
Overview
1. Generative AI
2. Intro to Machine Learning
3. Tensorflow
4. Pandas

3. Hello My Name is
Mudassir Shaikh
About Me:
● A Machine Learning Enthusiast
● ML Domain Lead @GDSC-PCE
● Data Analyst Intern @OnerFusion LCC

4. 1. Generative AI

5. 1. Gen AI for Developers
What is Generative AI
● Generative AI refers to the set
Artificial Intelligence Methodologies
that resembles training data they were
exposed to
● The Content could be anything from
synthesising data text, generating code,
realistic images, musics and more

6. Why Generative AI?
Road To 100 Million for various Platforms

7. Gen AI Models in Market

8. Let’s Generate
Something

9. 2. Introduction To
Machine Learning

10. 1. Intro to Machine Learning
Basic Definition
● Machine learning is a field of study in artificial intelligence concerned with the
development and study of statistical algorithms that can learn from data and generalize
to unseen data, and thus perform tasks without explicit instructions.

11. Machine
Learning
Semi Supervised
Learning
Supervised
Learning
Unsupervised
Learning
Reinforcement
Learning
Types of Machine
Learning

12. Supervised Learning:
Classification
● Classification is a process of finding a function which
helps in dividing the dataset into classes based on
different parameters.
● In Classification, a computer program is trained on
the training dataset and based on that training, it
categorizes the data into different classes.

13. Supervised Learning:
Regression
● Classification is a process of finding a function which
helps in dividing the dataset into classes based on
different parameters.
● In Classification, a computer program is trained on
the training dataset and based on that training, it
categorizes the data into different classes.

14. Unsupervised Learning:
Clustering
● Clustering or cluster analysis is a machine learning
technique, which groups the unlabelled dataset.
● It can be defined as "A way of grouping the data points
into different clusters, consisting of similar data points.
The objects with the possible similarities remain in a
group that has less or no similarities with another
group."

15. Reinforcement Learning:
● Reinforcement learning (RL) is a machine learning (ML) technique that trains software to make decisions to
achieve the most optimal results.
● It mimics the trial-and-error learning process that humans use to achieve their goals

16. Here We Go Again!

17. 3. Tensorflow

18. What is Tensorflow
● Open source library for numerical computation using data flow graphs
● Developed by Google Brain Team to conduct machine learning research
● Based on DisBelief used internally at Google since 2011
● “TensorFlow is an interface for expressing machine learning algorithms, and an implementation for executing such
algorithms”

19. What is Tensorflow
● Key idea: express a numeric computation as a graph
● Graph nodes are operations with any number of inputs and outputs
● Graph edges are tensors which flow between nodes
● Installation of TF: pip3 install tensorflow

20. Why Tensorflow?
● Whether you're an expert or a beginner, TensorFlow is an end-to-end platform that makes it easy for you to build and
deploy ML models.
● Easy Model Building: TensorFlow offers multiple levels of abstraction so you can choose the right one for your needs.
Build and train models by using the high-level Keras API, which makes getting started with TensorFlow and machine
learning easy.
● Robust ML production anywhere: TensorFlow has always provided a direct path to production. Whether it's on servers,
edge devices, or the web, TensorFlow lets you train and deploy your model easily, no matter what language or platform
you use.
● Scalability and Experimental: TensorFlow also supports an ecosystem of powerful add-on libraries and models to
experiment with, including Ragged Tensors, TensorFlow Probability, Tensor2Tensor and BERT.

21. Tensorflow Architechture
● Core in C++ Very low overhead
● Different front ends for
specifying/driving the computation
Python and C++ today, easy to add
more

22. 4. Pandas

23. What is Pandas?
● Pandas is a powerful and versatile library that simplifies tasks of data manipulation in
Python .
● Pandas is built on top of the NumPy library and is particularly well-suited for working
with tabular data, such as spreadsheets or SQL tables.
● Its versatility and ease of use make it an essential tool for data analysts, scientists, and
engineers working with structured data in Python.

24. What can you do using Pandas?
● Data set cleaning, merging, and joining.
● Easy handling of missing data (represented as NaN) in floating point as well as non-floating
point data.
● Columns can be inserted and deleted from DataFrame and higher dimensional objects.
● Powerful group by functionality for performing split-apply-combine operations on data
sets.
● Data Visulaization
Installing Pandas:
 pip install pandas
Importing Pandas:
 import pandas as pd

25. Pandas Data Structures
Pandas generally provide two data structures for manipulating data, They are:
● Series
● DataFrame
Pandas Series
 A Pandas Series is a one-dimensional labeled array capable of holding
data of any type (integer, string, float, python objects, etc.). The axis
labels are collectively called indexes.
Pandas Series
 Pandas DataFrame is a two-dimensional data structure with labeled
axes (rows and columns).

26. Let’s Know Pandas!

27. Thank You!
● Linkedin:
● Github:
● Instagram: