Machine Learning & AI Basics Course Roadmap
Why Learn From Wakeupcoders?
- 9+ Years Experienced AI & ML Experts
- Trained 12,000+ Students Worldwide
- Latest Curriculum Covering AI & ML Trends
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- AI-Based Learning Infrastructure to Optimize Learning & Development
Machine Learning & AI Basics Course Modules
Module 1: Introduction to AI & Machine Learning
Understanding the fundamentals of AI and ML. Topics covered:
- What is Artificial Intelligence?
- History and Evolution of AI & ML
- Types of Machine Learning (Supervised, Unsupervised, Reinforcement Learning)
- Real-World Applications of AI
Module 2: Mathematics for Machine Learning
Strengthening the mathematical foundation. Topics covered:
- Linear Algebra Basics
- Probability and Statistics
- Calculus for Optimization
- Matrix Operations and Vector Spaces
Module 3: Programming for AI & ML
Hands-on coding and implementation. Topics covered:
- Introduction to Python for AI
- Essential Python Libraries: NumPy, Pandas, Matplotlib
- Data Structures and Algorithms for ML
- Jupyter Notebook and Google Colab Basics
Module 4: Data Preprocessing & Feature Engineering
Preparing data for machine learning models. Topics covered:
- Data Cleaning and Handling Missing Values
- Feature Scaling and Normalization
- Data Transformation Techniques
- Dimensionality Reduction Methods
Module 5: Supervised Learning Algorithms
Understanding predictive modeling techniques. Topics covered:
- Regression Algorithms (Linear Regression, Logistic Regression)
- Classification Algorithms (Decision Trees, Random Forest, SVM)
- Model Evaluation Metrics (Accuracy, Precision, Recall, F1-Score)
Module 6: Unsupervised Learning Algorithms
Learning from unstructured data. Topics covered:
- Clustering Algorithms (K-Means, Hierarchical Clustering)
- Association Rule Learning (Apriori, FP-Growth)
- Principal Component Analysis (PCA)
Module 7: Neural Networks & Deep Learning Basics
Introduction to modern AI techniques. Topics covered:
- Basics of Neural Networks
- Activation Functions and Loss Functions
- Introduction to TensorFlow and Keras
- Implementing a Simple Neural Network
Module 8: Model Deployment & AI Ethics
Bringing AI models into real-world applications. Topics covered:
- Model Deployment Techniques (Flask, FastAPI)
- AI Bias and Fairness
- Ethical Considerations in AI Development
- Future Trends in AI & ML
Mini Projects
Minimum 3+ mini-projects to apply your learning:
- Predicting House Prices using Regression
- Customer Segmentation using Clustering
- Sentiment Analysis using NLP
Lab Sessions
10 hands-on coding practice sessions:
- Implementing Regression & Classification Models
- Data Visualization and Feature Engineering
- Training Neural Networks with TensorFlow
Certification
Complete assignments, projects, and tests to earn a Wakeupcoders AI & ML Basics Certification, adding value to your resume.
Course Fee Structure
- Full Payment: Rs. 40,000/-
- EMI Options: No-cost EMI plans in 3–4 installments
Class Schedules at Wakeupcoders
- Online Instructor-Led Course
- Online Self-Paced Course
- Free One-Year Access to Course Materials
Placement Assistance
- Resume Building Guidance
- Interview Preparation
- Freelancing and Job Market Strategies
Mock Interviews
- Conducted with Industry Experts
- Personalized Feedback for Improvement
Contact Us
For more details and enrollment, reach out to us at: wakeupcoders.com/contact