AI & Machine Learning

Course details

AI & ML Course – Master Artificial Intelligence and Machine Learning Skills

Our AI & ML course provides an in-depth understanding of Artificial Intelligence and Machine Learning concepts and tools to help you succeed in the tech industry. Learn from industry experts with over a decade of experience in the Middle East and master tools like Python, Pandas, NumPy, Tableau, and more.

Why Enroll in Our AI & ML Course?

• Comprehensive Curriculum: Gain practical skills with 15 weeks of intensive industry-led training.
• Hands-On Experience: Work on 5+ industry projects and a capstone project to build real-world expertise.
• Essential Tools Covered: Master Python libraries such as Pandas, NumPy, Matplotlib, and Seaborn.
• Career Support: Access CareerHub for resume building, job support, and interview preparation.
• Flexible Learning: Enjoy lifetime access to classes and easy payment options

Key Learning Outcomes

• Develop expertise in machine learning models and data analysis.
• Master NLP and Deep Learning techniques for advanced data insights.
• Learn to use CNN and RNN models for complex problem-solving.
• Build confidence with industry-standard projects and a capstone project.

Course Modules

Module 1: Python Programming Fundamentals

Module: Introduction to Python

• Overview of Python: history, features, and use cases
• Installing Python and setting up the development environment
• Basic syntax: variables, data types, and simple operations.
• Hands-on: Writing a simple Python program to understand basic concepts

Module: Sequences and File Operations

• Introduction to sequences: lists, tuples, sets, and dictionaries.
• File handling: reading and writing files in Python.
• Intro to Mathematical Concepts: Basic probability—how to generate random numbers and use them to simulate random events.
• Hands-on: A program simulating coin tosses or dice rolls to explore simple probability

Module: Functions and Object-Oriented Programming

• Defining and using functions: arguments, return values, and scope.
• Object-oriented concepts: classes, objects, inheritance, and polymorphism.
• Hands-on: Writing classes to represent basic statistical concepts (, creating a class for a random variable or distributions)

Module: Working with Modules and Handling Exceptions

• Importing and using Python modules.
• Exception handling: try, except, and finally blocks.
• Hands-on: Use Python’s built-in statistics module for basic statistical operations (mean, median, variance)

Module 2: Python for Data Science

Module: Array Manipulation using NumPy

• Introduction to NumPy and its applications.
• Creating and manipulating arrays.
• Mathematical Concepts
• Probability distributions
• Hands-on

Module: Introduction to Pandas

• Basic Pandas operations
• Cleaning and transforming data
• Mathematical Concepts
• Hands-on

Module: Advanced Data Manipulation with Pandas

• Merging, joining, and reshaping data.
• GroupBy, pivot tables, and handling time-series data.
• Mathematical Concepts
• Hands-on

Module: Data Visualization with Matplotlib and Seaborn

• Basic visualization
• Mathematical Concepts
• Hands-on

Module: Advanced Data Visualization

• Creating subplots, and advanced customizations in Matplotlib.
• Statistical visualizations in Seaborn
• Mathematical Concepts
• Hands-on

Module: Data Gathering and Web Scraping using Python

• Introduction to web scraping
• Using Selenium for scraping dynamic websites.
• Hands-on

Module 3: Machine Learning Foundations

Module: Foundations of Machine Learning

• Introduction to Machine Learning
• Mathematics for Machine Learning
• Introduction to ML Tools

Module: Supervised Learning

• Regression
• Classification
• Feature Engineering

Module 4: Advanced Machine Learning Techniques

• Ensemble Methods
• Hyperparameter Tuning
• Unsupervised Learning

Module 5: Time Series Analysis and Scalability

• Time Series Analysis
• Big Data Tools
• Model Scalability

Module 6: Deep Learning and Real-World Implementations

Module: Deep Learning and NLP

• Recurrent Neural Networks (RNNs)
• Convolutional Neural Networks (CNNs)
• Advanced Neural Network Architectures
• NLP Applications

Module: Ethics and Real-World Applications

• Ethics in AI
• Real-World Applications
• Capstone Projects

Module 7: Introduction to Tableau for Data Science

• Module: Data Preparation using Tableau Prep
• Module: Data Connection with Tableau Desktop
• Module: Basic Visual Analytics
• Module: Calculations in Tableau

Module 8: Visual Analytics and Data Insights with Tableau

• Module: Advanced Visual Analytics
• Module: Level of Detail (LOD) Expressions in Tableau
• Module: Advanced Charts in Tableau
• Module: Dashboards and Stories

Module 9: Capstone project

Why Learner’s Point?

At Learner’s Point, we are committed to your success by offering:

• Expert Guidance: Learn from seasoned trainers who blend theory with practical insights.
• Interactive Learning: Participate in engaging sessions, hands-on projects, and collaborative activities.
• Job Assistance: Our CareerHub ensures you’re ready for the job market with the right tools and resources.
• Comprehensive Curriculum: Stay ahead of industry trends with the latest AI and ML techniques.

Testimonials

Sarah Johnson, Software Engineer

"My experience at Learners Point Academy's Artificial Intelligence course was simply transformative. As a software engineer at a leading tech firm, I found the curriculum comprehensive and applicable. Kudos to the instructors for their expertise!"

Michael Chen, Data Analyst

"Learners Point Academy's Artificial Intelligence Training exceeded my expectations. The hands-on projects provided invaluable practical insights. I am grateful for the guidance of the instructors who made complex concepts easily ;

Take the next step in your career. Join our Artificial Intelligence and Machine Learning Course today to gain the skills, confidence, and credentials to thrive in the AI and ML industry. Updated on 07 February, 2025