Machine Learning for Programmers

Upskilling Course, 40 Academic Hours

Code Smarter with ML!

Why Enroll in this Course?

Machine learning enables computers to perform tasks without explicit programming. From self-driving vehicles to facial recognition, machine learning technologies are at the forefront.

Python is a leading programming language widely used in data analysis and machine learning. It’s celebrated for its robust libraries and versatility in handling data analysis tasks.

In this course, you will explore the most effective machine learning techniques. You’ll get familiar with the theoretical foundations of learning algorithms, as well as how to apply these principles to solve real-world problems using Python. Engage with hands-on examples and come out ready to apply machine learning capabilities in your work.

By enrolling in this course, you'll gain practical skills in machine learning that you can apply in your projects, improving your work performance and productivity.

Who should attend?

  • Data Analysts
  • Software Developers 
  • Business Intelligence Professionals 
  • Data Engineers
  • Other professionals tasked with analyzing and interpreting their organization’s data.

Prerequisite

  • Basic understanding of programming concepts

Learning Goals and Outcomes

By the end of the course, participants will:

  • Develop essential Python skills, from basic programming to advanced data handling with Pandas and NumPy.
  • Gain an understanding of machine learning concepts and techniques.
  • Implement real-world machine learning tasks, including data preparation, regression, and classification techniques.
  • Understand and apply both fundamental and advanced ML concepts, covering everything from supervised to unsupervised learning.
  • Tackle real-world data science problems using strategies such as handling imbalanced data and ensemble methods.

What You'll Get...

Professional Supervisor

This unique course was built by a professional team made up of the leading experts in Software Development and AI, with vast knowledge and experience in training too.
Maor Mugrabi

Maor Mugrabi

Head of AI Courses for Software Developers

Maor is a data geek deeply passionate about Artificial Intelligence (AI) and Machine Learning (ML).

Maor is dedicated to helping businesses leverage these technologies effectively. Maor is an entrepreneur, managing his own AI/ML development and consultancy firm. Maor’s work has empowered dozens of companies to innovate and grow through custom AI/ML implementations.

He is also a lecturer at John Bryce, where he passes his knowledge and expertise in Machine Learning and Python to his students. 

Maor holds a Bachelor’s degree in Applied Mathematics from Bar-Ilan University. 

What Do Graduates Have to Say?

Course Syllabus

Python Refresher

  • Introduction
  • IDEs and Tools for data analysis
  • Data types and strings
  • Control structures
  • Functions and functional programming
  • Collections
  • Object Oriented
  • Modules and Packages

Introduction to Data Analysis

  • Data Science
  • Machine Learning Overview
  • Data Analysis and ML Packages overview

Machine Learning Basic Concepts

  • Overview
  • Why Learn
  • Applications
  • Machine Learning Process
  • Learning Types:
    • Supervised Learning
    • Unsupervised Learning
    • Semi-Supervised Learning
    • Active Learning
    • Reinforcement learning
  • Batch vs Online Learning
  • Instance-based vs model-based learning
  • CRISP – DM Methodology

Demo – Complete Machine Learning task

  • Simple Linear Regression and classification tasks
    • Data understanding
    • Variables and Features
    • Training, Validating, and Testing Data
    • Exploratory Analysis
    • Types of data/features
    • Pandas package (Python)
    • Data visualization
    • Matplotlib and Seaborn packages (Python)
  • Data Preparation
    • Data preparation and cleaning
    • Dealing with Missing values
    • Central tendency
    • Mean/median/mode
    • Bias
    • Variance and standard deviation
    • Standard scores
    • Feature scaling – standardization and normalization
    • Numpy package (Python)
    • Vectors and matrixes
    • Multi-dimensional arrays
    • Functions
    • Slicing and fancy indexing
    • Linear algebra
    • Feature Engineering
    • Feature selections
    • Dummy variables
    • Converting continuous variable to discrete

Supervised Learning

  • Regressions
  • Classification
  • Non-Linear Regression
  • SciPy package (python)
    • Interpolation
    • Curve fit
    • Optimization
    • Statistics
    • Image processing
    • Integration and more
  • Model evaluation using metrics
  • SVM
  • Tuning hyper parameters
  • Cross validation and grid search
  • Decision trees and random forests
  • Naïve Bayes
  • KNN
  • Classification
    • Multi class
    • Multi label
    • One vs All
    • All vs All
    • Error correcting codes

Unsupervised Learning

  • Overview
  • Clustering
  • K-Means
  • GMM
  • DBSCAN
  • Hierarchical clustering
  • Recommender systems
  • Anomaly detection

Advanced topics

  • Time Series
  • Handling imbalanced data
  • Ensemble methods

Deep Learning and Neural Networks

  • Deep Learning
  • Neural networks overview
  • The perceptron
  • Network structure and hidden layers
  • Activation functions
  • Training the network
  • Optimization
  • Forward and back propagation
  • Gradient descent
  • Convergence
  • Learning rate
  • Overfitting and underfitting
  • Adding bias
  • Boltzmann Machines
  • Convolutional Neural Networks
  • Recurrent Neural Networks
  • SOM
  • Auto Encoders

Interested in more details?

We’d be happy to answer all your questions!

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