Python for Data Science and Machine Learning

Preference Dates Timing Location Registration Fees
Instructor-Led Training

(In-Person and Live Webinars)
17, 18, 24, 25 February 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.

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
  • 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.

Python Programming Experience
Basic Knowledge of Calculus, Linear Algebra and Statistics

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