Machine Learning (ML) Course
Best Machine Learning Course from GeekBase, Exclusively designed for Working Professionals and college students. with our expert-guided training and 100% Placement Assistance.
Course overview
Machine Learning with Artificial Intelligence: From Basics to Advanced Applications
Course Description:
Unlock the power of Machine Learning and AI with GeekBase Technology’s industry-focused training program. This course is designed to take learners from foundational concepts to advanced AI applications. Through hands-on projects, real-world datasets, and expert guidance, learners will gain the skills needed to build intelligent systems and launch careers in AI/ML.
Key Highlights:
Target Audience:
Students interested in AI/ML careers.
Developers looking to transition into Data Science / AI-ML.
Professionals aiming to upskill in ML technologies.
Prerequisites:
Basic Python programming experience is required for this course. However, basic computer literacy and familiarity with using a computer are recommended.
Mentor Support:
Learners will have access to an experienced instructor who will provide support through one on one meeting, live Q&A sessions, and email to answer questions and provide guidance throughout the course.
Curriculum
12 modulesModule 1: Introduction to AI & Machine Learning
- What is AI, ML, Deep Learning.
- Applications of AI in real world.
- Types of Machine Learning (Supervised, Unsupervised, Reinforcement).
- ML workflow.
Module 2: Python for Machine Learning & Data Science
- Python basics (variables, loops, functions).
- Lists, dictionaries, tuples
- NumPy basics.
- Pandas (DataFrames, operations).
- Data visualization (Matplotlib, Seaborn).
- Data preprocessing.
Module 3: Statistics & Mathematics for ML
- Mean, Median, Mode.
- Probability basics.
- Correlation & covariance.
- Linear algebra basics.
Module 4: Data Preprocessing
- Handling missing values.
- Encoding categorical data.
- Feature scaling.
- Train-test split.
- Outlier Detection :Z-score method,Feature Selection.
- Feature Selection :Filter methods(correlation), Wrapper methods, Embedded methods.
Module 5: Supervised Learning – Regression
- Linear Regression.
- Multiple Regression.
- Model evaluation (MSE, RMSE, R²).
Module 6: Supervised Learning – Classification
- Logistic Regression.
- K-Nearest Neighbors (KNN).
- Decision Trees.
- Random Forest.
- Boosting Algorithms -AdaBoost (basic intro)
- XGBoost (Industry standard)
Module 7: Unsupervised Learning
- Clustering (K-Means).
- Hierarchical clustering.
- Dimensionality reduction (PCA basics).
Module 8: Model Evaluation & Optimization
- Confusion matrix.
- Accuracy, Precision, Recall, F1-score.
- Cross-validation.
- Hyperparameter tuning.
Module 9: Introduction to Deep Learning
- Neural Networks basics.
- Activation functions.
- TensorFlow / Keras basics.
- Simple ANN model.
Module 10: Natural Language Processing (NLP)
- Text preprocessing.
- Tokenization.
- Bag of Words / TF-IDF.
- Basic sentiment analysis.
- Introduction to Modern AI
- Introduction to LLMs (Large Language Models)
- Basics of Transformers architecture
- Real-world applications (ChatGPT, Bard, etc.)
- API usage overview (optional demo)
Module 11: Computer Vision Basics
- Image processing basics.
- OpenCV introduction.
- Image classification concepts.
Module 12: Deployment & Final Project
- Model deployment basics (Flask / Streamlit).
- Building end-to-end ML project.
- Real-world project - House price prediction.
- Real-world project - Spam detection system.
- Real-world project - Number plate detection.
- Real-world project - Face recognition system.
- Real-world project - Chatbot creation.
- Real-world project - FAQ bot using LLM APIs.
Certification
Course Certification:
Upon successful completion of the course, there will be cumulative test conducted and students who scored above 60% marks will receive a certificate of completion from GeekBase Technology, which can be used to showcase their newly acquired Machine-Learning - AI skills.
Note: Test will be a MCQ pattern and maximum two attempts allowed.
Why certified Machine-Learning with AI ?
