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
This course will enable you to gain the skills and knowledge that you need to successfully carry-out real-world data science and machine learning projects.
The first part of the course covers data analysis and visualization. You will be working on real datasets using Python’s Numpy, Pandas, Matplotlib and Seaborn libraries.
The second part of the course focuses on machine learning. We will be covering both supervised and unsupervised learning. We will be working on case studies from wide range of 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.
