Certificate Programme in Fundamentals of Machine Leaning & its Algorithms
GoAskNow Academy welcomes you to excel with this introductory Certificate Programme in Fundamentals of Machine Learning and its Algorithms to equip yourself in making decision autonomously. This module is designed by expert leaders from top universities and global industries.
Created by: Machine Learning
- Last updated 6/2025
- English
- English [CC]
Certificate Programme in Fundamentals of Machine Leaning & its Algorithms
GoAskNow Academy welcomes you to excel with this introductory Certificate Programme in Fundamentals of Machine Learning and its Algorithms to equip yourself in making decision autonomously. This module is designed by expert leaders from top universities and global industries.
Created by: Machine Learning
- Last updated 6/2025
- English
- English [CC]
OBJECTIVE
Premium
Access this top-rated course, plus 26,000+ more top-rated courses, with a Goasknow plan.
278,545
learners
What you'll learn
- Understanding about the fundamentals of machine learning and neural networks
- Enabling the students to implement the ML algorithm in real world applications.
- Motivating the students to apply Machine learning algorithms for solving real life problems.
Eligibility Criteria:
Students from Engineering (CSE, ECE, EEE),
Mathematics, Statistics, Physics,
or Data Science domains.
This course includes:
- 4 week 45 hours
- Live meet sessions
- 5 Modules
- Online/Offline
- Pre-recorded Videos
- Reference Materials
- Certified Certificates
Course content
Module 1
Introduction to Machine Learning
Duration: 9 hours
Duration: 9 hours
- Various paradigms of learning problems
- Forms of Learning: Supervised, Semi-supervised, and Unsupervised algorithms, Reinforcement Learning
- Machine Learning Terminologies and Model Evaluation: Confusion Matrix, Accuracy, Precision, Recall, F1-Score, the curse of dimensionality, training, testing, validation, cross-validation, over-fitting, under fitting, early stopping, regularization, bias and variance.
Module 2
Supervised Learning Algorithms
Duration: 12 hours
Duration: 12 hours
- Regression: Linear Regression, Polynomial Regression,
- Evaluation metrics: MSE, RMSE, R² score.
- Classification: Logistic Regression, k-Nearest Neighbours (KNN), Decision Trees, Random Forests, Support Vector Machines (SVM)
- Evaluation: Confusion Matrix, Accuracy, Precision, Recall, F1-score, ROC Curve.
Module 3
Unsupervised Learning Algorithms
Duration: 8 Hours
Duration: 8 Hours
- Clustering: k-Means Clustering, Hierarchical Clustering, DBSCAN.
- Dimensionality Reduction: Principal Component Analysis (PCA), overview of t-SNE.
- Applications of clustering: Market segmentation, customer analytics, social network analysis, and anomaly detection
Module 4
Artificial Neural Networks & Deep Learning
Duration: 6 hours
Duration: 6 hours
- Introduction to Artificial Neural Networks
- Differences between Biological and Artificial Neural Networks
- Multi-layer neural network, Perceptron model and activation functions
- Forward and backward propagation
Module 5
Practical Implementation of ML
Duration: 8 hours
Duration: 8 hours
- Python libraries: NumPy, Pandas, Matplotlib, Scikit-learn.
- Real-world datasets: Iris, Titanic, MNIST, etc.
- Case Studies: Predicting house prices, Handwritten digit classification, Customer segmentation.
- Emerging Trends: Introduction to Reinforcement Learning, ML in edge/IoT devices, AutoML & Transfer Learning, Quantum Machine Learning.
Module 6
Contemporary Issues
Duration: 2 hours
Duration: 2 hours
- Guest lectures and case studies from industry experts
Programme Duration
- 4 Weeks (6 days a week)
- 45 Hours online sessions
- 10 Practical experiments and case studies (20 – 25 hours)
- Core areas data pre-processing, model training, and interpretation tasks
Learning outcomes
- At the end of the program the student will be able to
- Comprehend the categorization of machine learning algorithms.
- Understand the types of neural network architectures, activation functions
- Acquaint with the pattern association using neural networks
- Adopt different feature selection and classification techniques
Job Roles
ML Engineer / Data Scientist
AI
Researcher
Business Intelligence Developer
Data Analyst / NLP Engineer
IoT & ML Integration Specialist
