Machine Learning Engineer

What You Will Learn:

1. Python Programming: Master the principles of programming in Python.

2. Popular Machine Learning Libraries: Learn to use libraries like Pandas, Matplotlib, Numpy, and Scikit-learn.

3. Machine Learning Process: Dive deep into data cleaning, visualization, feature selection, and applying ML algorithms.

4. Supervised and Unsupervised Learning: Understand various machine learning algorithms, including supervised and unsupervised techniques.

5. Capstone Project: Work on a project based practical real case scenarios.

6. Mini Projects: Complete mini projects to solidify your understanding of machine learning techniques.

7. Graduation Project: Apply all your machine learning skills to create a meaningful methodology.

Course Description:

Machine Learning Engineer a joined course Between Assaal Academy & Dotpy Academy is a comprehensive course designed specifically for business professionals looking to leverage the power of machine learning to enhance decision-making and optimize operations.

Over 36 hours of interactive sessions, participants will explore key machine learning concepts, learn how to apply algorithms to real-world business problems and gain practical skills in data analysis and automation. By the end of the course, attendees will be equipped to identify opportunities for machine learning within their organizations and collaborate effectively with data teams to implement AI-driven solutions. This course is ideal for professionals, managers, analysts, and decision-makers aiming to drive innovation in their business processes.

Course Content:

Section 1: Introduction to AI :

• What is AI ?

• The definition of large language models .

• What is generative AI ?

• AI in the market .

Section 2: Programming with python :

• Intro to programming .

• Basics of python (Variables , Loops , Inputs , Condition) .

• Functions with python .

• Data Structure (Lists , Tuples , Sets , Dictionaries) .

• Python packages (NumPy , Panda , Matplotlib ,Sklearn) .

• 
  

Section 3 : Data Handling :

• Data Reading (all files) .

• Data Preprocessing .

• Data Visualization .

• Feature Selection .

Section 4 : Stats Of Machine Learning :

• Descriptive Statistic .

• Probability Distributions .

• Correlation & Covariance .

• Sampling Methods .

• Dimensionality Reduction .

• Outliers & Anomalies .

Section 5 : Machine Learning :

• Supervised Learning .

• Unsupervised Learning .

Section 6 : Deep Learning :

• Artificial Neural Network "ANN" .

• Convolutional Neural Networks " CNN"

• Large language models .

• Object Delection "YOLO"

Section 7 : Soft Skills :

• Interviewing Skills .

• Linkedin 101.

• How to present your ideas .

• Free lancing .

  
  

Who This Course is For:

This course is ideal for:

• Business Professionals, Managers, analysts, and decision-makers.

• Aspiring Data Scientists: Students who completed our Data Analysis (DA) course and want to pursue a career in data science.

• Professionals: Individuals with a good knowledge of calculus and statistics fundamentals looking to enhance their machine learning skills.

• Anyone Interested in Machine Learning: Those with a solid foundation in Python and statistics, aiming to delve into machine learning.

  
  

Certification:

Upon completing this course, you will receive two certificates:

• You will receive a certificate from Assaal Academy & Dotpy Academy, recognizing your proficiency in machine learning with Python for Business Applications.

  
  

Course Requirements:

1. Prior Basic knowledge of calculus and statistics fundamentals, is a Plus but not required.

2. No prior experience in programming is needed, but recommended

3. Students who finished our Data Analysis (DA) course, and are looking to pursue a career in data science.

4. Basic English language knowledge is a must.

5. A working computer/laptop.