AI & Machine Learning with Python

About Course

This intermediate-level course is designed to help learners gain practical, industry-ready skills in Artificial Intelligence. Starting with foundational Python programming and data analysis skills, it guides you through the core concepts of machine learning, deep learning, and natural language processing (NLP). Throughout the course, you’ll work on hands-on projects, from building simple models like linear regression and classification algorithms to deploying deep learning models and creating NLP applications.

By the end of the course, you’ll have the knowledge and skills to:

Understand and apply machine learning algorithms (both supervised and unsupervised).
Work with deep learning models for real-world applications.
Preprocess and clean data for efficient model building.
Develop simple AI applications that can be used in industries like healthcare, finance, and retail.
Gain practical experience in deploying AI models.

Whether you are looking to start your career in AI or enhance your current skill set, this course offers a comprehensive, hands-on approach to becoming industry-ready in AI.

Understand AI Fundamentals: Differentiate between AI, Machine Learning, and Deep L-earning, and explore real-world applications.
Master Data Preprocessing: Learn data cleaning, feature selection, and feature engineering to prepare datasets for machine learning models.
Build Machine Learning Models: Implement regression and classification algorithms like Linear Regression, Logistic Regression, K-Means, and Decision Trees.
Understand Deep Learning: Learn the basics of neural networks and how to build and train models using TensorFlow and Keras.
Work with NLP: Get hands-on experience with text processing techniques and build a simple sentiment analysis model.
Optimize Models: Understand cross-validation, hyperparameter tuning, and other techniques to improve model performance.
Deploy AI Applications: Apply your knowledge to create simple AI-driven applications ready for real-world deployment.
Develop Industry-Relevant Projects: Build end-to-end projects that mimic real-world AI applications (e.g., recommendation systems, classification models, etc.).

Course Content

Introduction to Artificial Intelligence (AI)
What is AI?: Basic understanding of Artificial Intelligence, its history, and its impact. AI vs. Machine Learning vs. Deep Learning: Differentiating between these terms and their applications in industry. Applications of AI: Basic examples from industries like healthcare (diagnosis), finance (fraud detection), and retail (recommendation systems). Real-World AI Examples: Demonstrations of AI applications that you can easily interact with (like chatbots, recommendation systems).

Setting Up Python for AI
Basic Python for AI: Introduction to libraries like pandas for data manipulation, matplotlib and seaborn for data visualization, and numpy for numerical operations. Data Structures in Python: Lists, dictionaries, tuples, and sets with examples relevant to data science. Working with Datasets: Loading data into pandas DataFrames, exploring and cleaning data, handling missing values, and basic data visualization techniques. Hands-On Exercise: Analyze a simple dataset using pandas and visualize it using matplotlib.

Basics of Machine Learning
What is Machine Learning?: An introduction to machine learning and how it's different from traditional programming. Types of Machine Learning: Supervised, unsupervised, and reinforcement learning. Supervised Learning Algorithms: Linear Regression: Understanding the simplest form of regression, how to predict continuous values. Classification Algorithms: Logistic Regression for binary classification (e.g., spam detection). Model Evaluation: Introduction to basic metrics like accuracy, precision, recall, and confusion matrix. Hands-On Exercise: Implement a basic linear regression model and evaluate its performance on a small dataset.

Data Preprocessing and Feature Engineering
Data Cleaning: Removing duplicates, handling missing data, and basic feature engineering (e.g., scaling, encoding categorical data). Feature Selection: Understanding the importance of selecting the right features to improve model performance. Splitting Data: Training vs. Testing data, overfitting, and underfitting concepts. Hands-On Exercise: Preprocess a dataset (e.g., housing prices dataset) and split it for model training.

Unsupervised Learning Basics
What is Unsupervised Learning?: Understanding how unsupervised learning works to find hidden patterns in data. Clustering Algorithms: K-Means: A simple algorithm for grouping similar data points together. Hierarchical Clustering: How it works and when to use it. Dimensionality Reduction: Introduction to Principal Component Analysis (PCA) to reduce the complexity of high-dimensional data. Hands-On Exercise: Implement K-Means clustering on a dataset and visualize clusters.

Introduction to Deep Learning
What is Deep Learning?: Basic understanding of neural networks and their difference from traditional machine learning algorithms. Neural Networks: Introduction to artificial neural networks, neurons, activation functions (ReLU, Sigmoid). Building Simple Neural Networks: Introduction to TensorFlow and Keras for creating neural networks. Hands-On Exercise: Build and train a simple neural network for a classification task (e.g., handwritten digit classification with MNIST).

Natural Language Processing (NLP)
Introduction to NLP: What is Natural Language Processing? Use cases in industry (e.g., sentiment analysis, text classification). Text Preprocessing: Tokenization, removing stopwords, stemming, and lemmatization. Basic NLP Techniques: Introduction to TF-IDF (Term Frequency-Inverse Document Frequency) for feature extraction from text. Hands-On Exercise: Build a simple sentiment analysis model using basic text processing and machine learning algorithms.

Model Evaluation and Tuning
Understanding Overfitting and Underfitting: The importance of balancing bias and variance. Cross-Validation: Introduction to K-fold cross-validation to improve model performance. Hyperparameter Tuning: Basics of grid search and random search to find the best parameters for machine learning models. Hands-On Exercise: Use cross-validation and grid search on a classification task to improve the model's performance.

AI/ML – Industry Project Development & Application
AI in Real-World Applications: A closer look at how AI is applied in various sectors like healthcare, finance, and retail. AI in Decision Making: Using AI to aid in business decisions (e.g., sales forecasting, predictive analytics). Challenges in Deploying AI: Understanding issues like data quality, model interpretability, and scalability. Ethical AI: A brief introduction to ethical concerns in AI (bias, fairness, and privacy). Case Study: Walkthrough of a case study of an AI application in industry (e.g., a recommendation system for an e-commerce platform).

Final Project – Building a Simple AI Application
Project Objective: Using everything learned in the course, students will build a simple AI application. Example projects: A recommendation system, a sentiment analysis tool, or a simple image classifier. Project Steps: Step 1: Data collection and preprocessing. Step 2: Model selection and training. Step 3: Model evaluation and tuning. Step 4: Deployment and testing. Final Project Presentation: Students will present their projects, showcasing their AI models and the results they obtained.

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