Course Overview:
AI is transforming how we live, work, and play. By enabling new technologies like self-driving cars and recommendation systems or improving old ones like medical diagnostics and search engines, the demand for AI and machine learning expertise is growing. This course will enable you to take the first step toward solving important real-world problems and future-proofing your career.
Introduction to Artificial Intelligence with Python explores the concepts and algorithms at the foundation of modern artificial intelligence, diving into the ideas that give rise to technologies like game-playing engines, handwriting recognition, and machine translation. Through hands-on projects, students gain exposure to the theory behind graph search algorithms, classification, optimization, reinforcement learning, and other topics in artificial intelligence and machine learning as they incorporate them into their own Python programs. By the course’s end, students will emerge with experience in libraries for machine learning as well as knowledge of artificial intelligence principles that will enable them to design intelligent systems of their own.
Curriculum
- 8 Sections
- 0 Lessons
- 8 Weeks
- Module 1: Introduction to Python and AI Fundamentals1.1 Introduction to AI and Python's Role AI Development Overview of AI applications in real-world problems. Python libraries for AI (NumPy, Pandas, SciPy) Setting up the environment (Anaconda, Jupyter Notebooks) 1.2 Python Basics for AI Data types, loops, conditions, and functions Object-oriented programming (OOP) essentials Introduction to NumPy for efficient computations Hands-on Project: Build a simple AI-based chatbot0
- Module 2: Data Handling and Preprocessing2.1 Data Wrangling with Pandas Working with datasets Data cleaning and preparation 2.2 Data Visualization Introduction to Matplotlib and Seaborn Plotting and visualizing trends and relationships in data 2.3 Feature Engineering Handling missing data Feature scaling and normalization Hands-on Project: Create an AI-ready dataset from raw data (e.g., sales or medical records)0
- Module 3: Machine Learning with Python3.1 Introduction to Machine Learning Types of ML: Supervised, Unsupervised, Reinforcement Learning Introduction to Scikit-Learn 3.2 Regression Algorithms Linear Regression Decision Trees and Random Forest 3.3 Classification Algorithms Logistics Regression K-Nearest Neighbors (k-NN) Support Vector Machines (SVM) 3.4 Clustering Algorithms k-Means Clustering Hierarchical Clustering Hands-on Project: Develop a machine learning model to predict house prices or classify medical data0
- Module 4: Deep Learning with Python4.1 Introduction to Neural Networks Perception, activation functions, and forward/backpropagation Introduction to TensorFlow and Keras 4.2 Building Deep Learning Models Multi-layer perceptions (MLPs) Hyperparameter tuning and optimization 4.3 Convolutional Neural Networks (CNNs) CNN architecture for image processing Applications in computer vision 4.4 Lesson 4.4: Recurrent Neural Networks (RNNs) Time series and sequence prediction Long Short-Term Memory (LSTM) networks Hands-on Project: Build a deep learning model for image classification (e.g., handwritten digits recognition) or text classification (e.g., sentiment analysis)0
- Module 5: AI in Natural Language Processing (NLP)5.1: NLP Basics and Text Preprocessing Tokenization, stemming, and lemmatization Word embeddings (TF-IDF, Word2Vec) 5.2: Sentiment Analysis and Text Classification Building text classification models Sentiment analysis with Scikit-Learn and TensorFlow 5.3: Chatbots and NLP Applications Building simple AI-based chatbots with NLP processing Neural network models for NLP (transformers, GPT) Hands-on Project: Develop an AI-based sentiment analysis tool or chatbot0
- Module 6: AI in Computer Vision6.1: Introduction to Computer Vision Image processing techniques (OpenCV, PIL) Basic image transformations and filters 6.2: Object Detection and Recognition Applying CNNs for object recognition Transfer learning and pre-trained models (VGG, ResNet) Hands-on Project: Build an AI-based object detection system0
- Module 7: Reinforcement Learning with Python7.1: Introduction to Reinforcement Learning Key concepts: agents, environment, rewards, actions 7.2: Q-Learning and Policy Gradient Methods Building RL agents with Python Practical applications in game environments Hands-on Project: Create an AI that learns to play a simple game (e.g., Tic-Tac-Toe)0
- Module 8: Ethical Considerations and AI Future8.1: Ethical AI and Bias in AI Models Addressing bias and fairness in AI systems Privacy and data security concerns 8.2: The Future of AI AI trends and innovations AI in different industries (Healthcare, Finance, Robotics)0
