Machine Learning
Machine Learning
Machine Learning One O One – 3 Months Professional Training
Duration: 3 Months | Level: Beginner to Intermediate | Format: Hands-on Training + Assignments + Real-World Projects
Learn the foundations of Machine Learning and start building intelligent systems!
This course introduces you to essential mathematics, key algorithms, and hands-on machine learning development. Designed for students, fresh graduates, and professionals who want to enter the world of AI, Data Science, and Automation.
Course Outline
Module 1: Linear Algebra Basics for Machine Learning
Introduction to Matrices, Vectors, and Tensors
Matrix Operations: Addition, Multiplication, Inversion
Understanding Transformations and their role in Machine Learning Models
Module 2: Calculus Essentials for Machine Learning
Introduction to Derivatives and Gradients
Chain Rule and its Application in Deep Learning
How Optimization Works: Gradient Descent and Cost Functions
Module 3: Introduction to Supervised Learning
What is Supervised Learning?
Difference between Supervised, Unsupervised, and Reinforcement Learning
Real-Life Applications: Spam Detection, Credit Scoring, Image Recognition
Module 4: Key Machine Learning Algorithms
Regression Algorithms
Linear Regression, Polynomial Regression
Understanding Error Metrics (MSE, RMSE, R² Score)
Classification Algorithms
Logistic Regression, K-Nearest Neighbors (KNN)
Decision Trees and Support Vector Machines (SVM)
Evaluating Classification Models (Confusion Matrix, Precision, Recall, F1 Score)
Module 5: Hands-on Exercise – Build Your First Machine Learning Model
Setting up the Python Environment (Anaconda, Jupyter Notebook)
Loading and Preprocessing Data (Pandas, NumPy)
Training and Testing a Machine Learning Model
Model Evaluation and Improvements
Real-world Mini-Project: Predicting House Prices / Classifying Images
Course Description
Total Duration: 3 Months
Total Training Hours: 108 Hours
Daily Class Time: 3 Hours
Theory: 30% | Practical Hands-On: 70%
Batch Options: Morning, Afternoon, Evening, Weekend
Learning Outcomes By the end of this course, students will:
Understand the mathematical foundations behind ML models.
Build, train, and evaluate machine learning algorithms from scratch.
Gain practical experience working with real-world datasets.
Build a mini portfolio to kickstart their ML/AI career.
Machine Learning Certification
SKY Tech IT Institute
Requirements for Admission
-
Basic Understanding of Mathematics (High School Level)
Basic Programming Knowledge (Python preferred)
Valid CNIC/B-Form
Age Limit: No Limit
Laptop Recommended (but not compulsory – Institute Labs Available)
Career Opportunities After the Course
Machine Learning Engineer (Entry Level)
Data Analyst
AI Research Assistant
Python Developer (ML Focused)
Junior Data Scientist
ML Freelancer (Projects on Fiverr, Upwork, Freelancer.com)
Tools & Technologies Covered Python Programming
NumPy and Pandas for Data Manipulation
Matplotlib and Seaborn for Data Visualization
Scikit-Learn for Machine Learning
Jupyter Notebook for Coding Practice
