Python for AI/ML – Complete Syllabus AI ML projects for beginners BIT UCSC UoM Colombo Moratuwa

Python for AI/ML – Complete Syllabus

1. Python Foundations (Weeks 1–2)

Before touching AI, get strong in Python basics.

1.1 Basics & Syntax

• Python installation & setup (Anaconda, venv, Jupyter)

• Comments, indentation

• Variables & naming rules

• Constants

1.2 Data Types & Type Conversion

• int, float, str, bool, list, tuple, set, dict, NoneType

• Type casting (int(), float(), str(), bool())

1.3 Operators

• Arithmetic

• Comparison

• Logical

• Assignment

• Membership (in, not in)

• Identity (is, is not)

1.4 Strings

• String indexing, slicing

• Common string methods

• f-strings & formatting

1.5 Input/Output

• input() & print()

• Formatting output

---

2. Control Flow (Weeks 2–3)

2.1 Conditionals

• if, elif, else

• Nested conditions

2.2 Loops

• for loops

• while loops

• break & continue

• range()

• Loop over iterables

2.3 Comprehensions

• List comprehensions

• Dict comprehensions

• Set comprehensions

💡 AI/ML relevance: Loops for batch processing, conditions for decision making in models.

---

3. Functions & Modules (Week 3)

3.1 Functions

• Defining & calling functions

• Parameters & arguments

• Default arguments

• Return values

• Scope (local/global)

3.2 Modules & Packages

• Importing built-in modules (math, random, etc.)

• Installing & importing external packages (pip install)

• Creating your own modules

---

4. Data Structures & File Handling (Week 4)

4.1 Lists, Tuples, Sets, Dictionaries

• Creation, access, modification

• Iteration

• Nesting

4.2 File Handling

• Reading/writing text files

• CSV files

• JSON files

• Using with context

💡 AI/ML relevance: Reading datasets, saving model results.

---

5. Error Handling & Debugging (Week 5)

• try, except, finally

• Raising exceptions

• Common Python errors

---

6. Python for Data Science (Weeks 6–8)

6.1 NumPy

• Arrays & indexing

• Array operations & broadcasting

• Statistical methods

6.2 Pandas

• Series & DataFrame

• Loading CSV, Excel, JSON

• Selecting, filtering, sorting

• Grouping & aggregating

• Handling missing values

6.3 Matplotlib & Seaborn

• Line, bar, scatter plots

• Histograms, boxplots

• Heatmaps

💡 AI/ML relevance: Data cleaning, preprocessing, visualization.

---

7. Introduction to AI/ML Concepts (Weeks 9–10)

• What is AI, ML, Deep Learning

• Types of ML (supervised, unsupervised, reinforcement)

• Features, labels, datasets

• Training & testing

• Model evaluation metrics

---

8. Machine Learning with Scikit-learn (Weeks 11–14)

• Loading datasets (load_iris, load_digits, CSV files)

• Data preprocessing

• Train/test split

• Classification algorithms

• Regression algorithms

• Clustering (KMeans)

• Model evaluation (accuracy, precision, recall, F1-score)

• Saving/loading models (joblib, pickle)

---

9. Deep Learning Foundations (Weeks 15–18)

9.1 Neural Networks Basics

• Perceptron & activation functions

• Forward & backward propagation

• Loss functions

9.2 TensorFlow & Keras

• Creating neural networks

• Image classification (MNIST, CIFAR-10)

• Text classification (sentiment analysis)

• Model tuning & regularization

• Using GPUs

---

10. Specialized AI/ML Topics (Weeks 19–24)

• Natural Language Processing (NLP) – NLTK, spaCy

• Computer Vision – OpenCV, image preprocessing

• Time Series Analysis – ARIMA, LSTM

• Reinforcement Learning – Basics with OpenAI Gym

• Large Language Models (LLMs) – Hugging Face Transformers

---

11. AI Project Development (Weeks 25–28)

Real-world projects to combine skills:

1. House Price Prediction (Regression)

2. Spam Email Classifier (NLP)

3. Image Recognition App (Computer Vision)

4. Stock Price Prediction (Time Series)

5. Chatbot with AI (LLM Integration)

---

12. Deployment & Best Practices (Weeks 29–30)

• Saving & loading models

• Creating APIs with Flask/FastAPI

• Deploying to cloud (AWS, Azure, GCP, Hugging Face Spaces)

• Version control with Git & GitHub

• Virtual environments for deployment

---

13. AI Career Skills (Ongoing)

• Writing clean, documented code

• Using GitHub portfolio

• Understanding ML papers

• Participating in Kaggle competitions

• Building LinkedIn projects

---

🚀 Kickstart Your AI/ML Journey with Python! 🤖💡

Ever dreamed of creating your own AI applications but didn’t know where to start?
Here’s your chance to dive into fun & practical beginner projects — perfect for learning real-world machine learning skills! 🖥✨

🔥 Beginner-Friendly AI/ML Projects You Can Build:
🎯 Classification:

• 🌸 Iris Flower Classification – Identify flower species using classic datasets.

• ✍ MNIST Digit Recognition – Classify handwritten digits with ML or neural networks.

• 📧 Spam Email Detection – Teach AI to spot unwanted emails using NLP.

• 🚢 Titanic Survival Prediction – Predict survival chances using historical data.

📊 Regression:

• 🏠 House Price Prediction – Estimate property values with regression models.

• 🍷 Wine Quality Prediction – Predict wine ratings from chemical properties.

💬 Other Cool Projects:

• 🤝 Simple Chatbot – Build your first conversational AI.

• 🎬 Recommendation System – Suggest movies or products like Netflix & Amazon.

• ❤️ Sentiment Analysis – Detect emotions in text reviews or social media posts.

🛠 Tools You’ll Learn:
Python, Scikit-learn, Pandas, NumPy, Matplotlib, Seaborn, NLTK, spaCy, TensorFlow/Keras.

💡 Whether you’re a student, developer, or tech enthusiast, these projects will give you hands-on experience to stand out in the AI/ML world.

📞 Call / WhatsApp: 0777337279
🎓 Learn from Sri Lanka’s best AI/ML project training programs — Online & In-Person!

✨ Stop scrolling. Start building your AI future today! 🚀

---