Unique Online ai educator Classes & Pre-Recorded Courses

Having a strong background in the fundamentals of AI/ML is crucial for building real-world applications that automate and optimize your lifestyle. Artificial Intelligence and Machine Learning precisely describe how to train our application to learn from the data, analyze it, and gain decisive abilities. 

Lesson 1: Introduction to Artificial Intelligence and Machine Learning

• What is AI? What is ML? Differences between AI, ML, DL, and Data Science
• Real-world applications
• History and evolution of AI
• Types of ML: Supervised, Unsupervised, Reinforcement Learning
• Tools setup: Python, Jupyter Notebooks, Google Colab

Lesson 2: Python for AI/ML

• Python basics recap: variables, data types, functions, control flow
• Libraries for ML: NumPy, pandas, matplotlib
• Hands-on with data loading, preprocessing, and visualization

Lesson 3: Mathematics for ML - Part 1

• Linear Algebra essentials: vectors, matrices, dot product
• Applications in ML
• Numpy-based matrix operations

Lesson 4: Mathematics for ML - Part 2

• Probability and Statistics basics
• Mean, median, variance, standard deviation
• Distributions, correlation vs. causation

Lesson 5: Supervised Learning - Regression

• Linear regression (Simple and Multiple)
• Cost function, gradient descent
• Evaluation metrics: MAE, MSE, RMSE, R² score
• Hands-on: House price prediction dataset

Lesson 6: Supervised Learning - Classification

• Logistic regression
• Decision trees
• Confusion matrix, accuracy, precision, recall, F1 score
• Hands-on: Email spam classifier

Lesson 7: Unsupervised Learning - Clustering

• K-means clustering
• Evaluation: Elbow method, silhouette score
• Hands-on: Customer segmentation

Lesson 8: Unsupervised Learning - Dimensionality Reduction

• Principal Component Analysis (PCA)
• Feature selection vs. extraction
• Hands-on: Visualizing high-dimensional data

Lesson 9: Model Evaluation and Tuning

• Train/test/validation split
• Cross-validation
• Hyperparameter tuning (Grid Search, Random Search)
• Avoiding overfitting/underfitting

Lesson 10: Introduction to Deep Learning

• What is Deep Learning? Neural Networks
• Perceptron, forward/backward propagation
• Brief intro to TensorFlow/Keras or PyTorch

Lesson 11: Capstone Project Work

• Students form groups or work individually
• Choose a dataset, define a problem, build and evaluate a model
• Instructor mentorship

Lesson 12: Project Presentations + Wrap-Up

• Students present their projects
• Peer review
• Course recap, next steps, career guidance, resources