Artificial Intelligence & Machine Learning with Python

Course Overview

This course provides a structured and hands-on learning approach to Python, Machine Learning, Deep Learning, and Natural Language Processing (NLP), preparing participants for AI-driven careers. Designed for aspiring Data Scientists, Machine Learning Engineers, and AI Professionals, this program covers industry-relevant problem-solving techniques, real-world applications, and advanced AI/ML methodologies. By the end of the course, learners will be proficient in building AI/ML models and deploying them in practical scenarios.

Trainer: Jaswant Kaur

Total Course Duration: 45 Hours

Course Breakdown (45 Hours)

Module

Duration

Python for AI & ML

6 Hours

Probability & Statistics for Machine Learning

4 Hours

Data Preprocessing & Feature Engineering

5 Hours

Exploratory Data Analysis (EDA) & Visualization

4 Hours

Machine Learning Fundamentals

8 Hours

Advanced Machine Learning Topics

4 Hours

Time Series Forecasting

4 Hours

Deep Learning & Neural Networks

5 Hours

Model Deployment & MLOps

3 Hours

End-to-End Project & Career Preparation

2 Hours

Python for AI & ML

- Introduction to Python & Its Role in AI

- Variables, Data Types, and Operators

- Object-Oriented Programming (OOP) in Python

- Control Structures, Functions, and Modules

- Python Libraries: NumPy, pandas, matplotlib, seaborn, Scikit-Learn

- Data Structures, String Manipulation, File Handling, and Exception Handling

Probability & Statistics for Machine Learning

- Descriptive Statistics: Mean, Median, Mode, Variance, Standard Deviation

- Probability Theory: Conditional Probability, Bayes’ Theorem

- Probability Distributions: Normal, Binomial, Poisson

- Hypothesis Testing, Confidence Intervals, and Correlation

Machine Learning Fundamentals

- Supervised vs. Unsupervised Learning

- Regression (Linear & Logistic Regression)

- Model Evaluation Metrics: Accuracy, Precision, Recall, F1 Score, Confusion Matrix, ROC Curve

- Decision Trees, Random Forests, Support Vector Machines (SVM)

- Hyperparameter Tuning (GridSearchCV & RandomizedSearchCV)

Deep Learning & Neural Networks

- Introduction to Neural Networks & Deep Learning

- Building Neural Networks with TensorFlow & Keras

- Convolutional Neural Networks for Image Processing

- Recurrent Neural Networks: LSTM & GRU

- Transfer Learning Concepts

Model Deployment & MLOps

- Deploying Models with Flask & FastAPI

- Introduction to MLOps and CI/CD

- Model Versioning, Monitoring, and Cloud Deployment (AWS/GCP/Azure)

- Containerization with Docker and Kubernetes

End-to-End Project & Career Preparation

- Real-World Case Studies and Practical Applications

- Assignments & Hands-on Coding Challenges (Weekly)

- Doubt Clarification & Live Q&A Sessions

- Resume Preparation & Job Interview Guidance

- Project Discussion: Building AI/ML Models from Scratch

Additional Notes

✔ Flexibility: Each module’s duration is approximate and may be adjusted depending on class progress and practical sessions.

✔ Hands-On Focus: Real-world projects, coding challenges, and live case studies are integrated throughout the course.

✔ Interactive Learning: Q&A sessions, group

discussions, and practical assignments ensure active participation.