## Python for Data Science and Machine Learning

Preference | Dates | Timing | Location | Registration Fees |
---|---|---|---|---|

Instructor-Led Training (In-Person and Live Webinars) |
September 21, 22, 28, 29, 2024 | Saturdays & Sundays 6:00 PM - 8:30 PM | Dubai Knowledge Park | 960 USD |

### Course Description

*verticals including finance, heath-care, real estate, sales and marketing.*Some of the algorithms that will be discussed include Linear Regression, Logistic Regression, Support Vector Machines (SVM), and K-means clustering. This course is the foundation for Deep Learning courses in this specialization.

Course Outline

Audience

Prerequisites

After the Course

Course Outline

#### Unit 1 – Course Introduction

- Overview of Data Analysis, Data Visualization and Machine Learning

#### Unit 2 – Python for Data Analysis – NumPy

- Numpy Arrays
- Numpy Array Indexing
- Numpy Operations

#### Unit 3 – Python for Data Analysis – Pandas

- Series
- Missing Data
- Groupby
- Merging Joining and Concatenating
- Operations
- Data Input and Output

#### Unit 4 – Python for Data Visualization – Matplotlib

- Data Visualization with Matplotlib

#### Unit 5 – Python for Data Visualization – Seaborn

- Distribution Plots
- Categorical Plots
- Matrix Plots
- Regression Plots
- Grids
- Style and Color

#### Unit 6 – Introduction to Machine Learning

- What is machine learning?
- Supervised Learning
- Unsupervised Learning
- Machine Learning with Python

#### Unit 7 – Linear Regression

- Model Representation
- Cost Function
- Gradient Descent
- Gradient Descent for Linear Regression
- Linear Regression with Python
- Linear Regression Project

#### Unit 8 – Cross Validation and Bias-Variance Trade-Off

- Bias Variance Trade-Off

#### Unit 9 – Logistic Regression

- Classification
- Hypothesis Representation
- Decision Boundary
- Cost function and Gradient Descent
- Logistic Regression with Python
- Logistic Regression Project

#### Unit 10 – K Nearest Neighbors

- KNN Theory
- KNN with Python
- KNN Project

#### Unit 11 – K-Means Clustering

- Optimization Objective
- Random Initialization
- Choosing the Number of Clusters
- K-Means with Python
- K-Means Project

#### Unit 12 – Introduction to Deep Learning

- Neural Network Representation
- Forward Propagation
- Activation Functions
- Cost Functions
- Back-Propagation with Gradient Descent
- Solving a Regression Problem with Deep Learning

Audience

- Professionals or students who are interested in machine learning, and already have Python Programming experience.
- Future Data Science Professionals and Engineers
- Intermediate Python programmers interested in enhancing their existing skills.

Prerequisites

Python Programming Experience

Basic Knowledge of Calculus, Linear Algebra and Statistics

After the Course

The participants who have successfully completed this course are encouraged to take Innosoft Certified AI Professional Exam (AI-200)

Course Outline

Audience

Prerequisites

After the Course

Course Outline

#### Unit 1 – Course Introduction

- Overview of Data Analysis, Data Visualization and Machine Learning

#### Unit 2 – Python for Data Analysis – NumPy

- Numpy Arrays
- Numpy Array Indexing
- Numpy Operations

#### Unit 3 – Python for Data Analysis – Pandas

- Series
- Missing Data
- Groupby
- Merging Joining and Concatenating
- Operations
- Data Input and Output

#### Unit 4 – Python for Data Visualization – Matplotlib

- Data Visualization with Matplotlib

#### Unit 5 – Python for Data Visualization – Seaborn

- Distribution Plots
- Categorical Plots
- Matrix Plots
- Regression Plots
- Grids
- Style and Color

#### Unit 6 – Introduction to Machine Learning

- What is machine learning?
- Supervised Learning
- Unsupervised Learning
- Machine Learning with Python

#### Unit 7 – Linear Regression

- Model Representation
- Cost Function
- Gradient Descent
- Gradient Descent for Linear Regression
- Linear Regression with Python
- Linear Regression Project

#### Unit 8 – Cross Validation and Bias-Variance Trade-Off

- Bias Variance Trade-Off

#### Unit 9 – Logistic Regression

- Classification
- Hypothesis Representation
- Decision Boundary
- Cost function and Gradient Descent
- Logistic Regression with Python
- Logistic Regression Project

#### Unit 10 – K Nearest Neighbors

- KNN Theory
- KNN with Python
- KNN Project

#### Unit 11 – K-Means Clustering

- Optimization Objective
- Random Initialization
- Choosing the Number of Clusters
- K-Means with Python
- K-Means Project

#### Unit 12 – Introduction to Deep Learning

- Neural Network Representation
- Forward Propagation
- Activation Functions
- Cost Functions
- Back-Propagation with Gradient Descent
- Solving a Regression Problem with Deep Learning

Audience

- Professionals or students who are interested in machine learning, and already have Python Programming experience.
- Future Data Science Professionals and Engineers
- Intermediate Python programmers interested in enhancing their existing skills.

Prerequisites

Python Programming Experience

Basic Knowledge of Calculus, Linear Algebra and Statistics

After the Course

The participants who have successfully completed this course are encouraged to take Innosoft Certified AI Professional Exam (AI-200)

## Testimonials

The workshop on big data and machine learning was an excellent introduction to practitioners considering using data science. Ahmed demonstrated considerable teaching talent rooted in his long expertise with systems development.

Very rewarding course. Rare to find a Deep learning course in Dubai that teaches concepts from scratch and provides practical applications. Will definitely recommend.

Innosoft Gulf Institute is educating students breaking and revolutionary techniques with focus on future trends in CIT industry. Mr. Ahmed is well updated on latest technologies related to Big Data, AI, Machine Learning, etc.
Rated as 5 star in terms of overall deliverance.

The most important thing is to be convinced of what you are studying. It's not just about teaching...
I'm taking four courses at Innosoft Gulf institute, and I think it's much better than my bachelor's degree.

Ahmad TahboubSenior Year Computer Science Student

Innosoft Gulf really gave me a head start for college. The teacher was amazing and I really learned a lot. I highly recommend the Python, Java and Machine Learning courses.

Oshin VatsDistinguished High School Student