AI / ML

Course Objective:

• Learn Artificial Intelligence and Machine Learning from beginner to professional level.
• Build a strong foundation in Python programming, mathematics and statistics.
• Learn data analysis, data preprocessing, visualization and exploratory data analysis.
• Understand supervised, unsupervised and reinforcement learning algorithms.
• Learn Deep Learning using neural networks, TensorFlow and PyTorch.
• Learn Natural Language Processing, Computer Vision and Generative AI.
• Understand Large Language Models, Transformers, embeddings and AI applications.
• Learn model deployment, MLOps, Docker, APIs, cloud platforms and production practices.
• Build real-world AI/ML projects from beginner to professional level.
• Prepare for AI Engineer, Machine Learning Engineer, Data Scientist and AI Developer roles.

1

Introduction to AI & Machine Learning

• What is Artificial Intelligence?
• What is Machine Learning?
• What is Deep Learning?
• AI vs ML vs Deep Learning.
• Types of Artificial Intelligence.
• Narrow AI.
• General AI Concepts.
• Machine Learning Workflow.
• AI Applications.
• Real-world AI Systems.
• AI Career Paths.
• Machine Learning Engineer.
• AI Engineer.
• Data Scientist.

2

Python Programming

• Introduction to Python.
• Python Installation.
• Python Syntax.
• Variables.
• Data Types.
• Operators.
• Conditional Statements.
• Loops.
• Functions.
• Lambda Functions.
• Lists.
• Tuples.
• Sets.
• Dictionaries.
• Strings.
• Exception Handling.
• File Handling.
• Modules and Packages.

3

Advanced Python

• Object-Oriented Programming.
• Classes and Objects.
• Inheritance.
• Polymorphism.
• Encapsulation.
• Abstraction.
• Decorators.
• Generators.
• Iterators.
• List Comprehensions.
• Dictionary Comprehensions.
• Regular Expressions.
• Virtual Environments.
• pip.
• requirements.txt.
• Python Project Structure.

4

Mathematics for Machine Learning

• Mathematical Foundations.
• Numbers and Functions.
• Linear Algebra.
• Vectors.
• Matrices.
• Matrix Operations.
• Matrix Multiplication.
• Transpose.
• Determinants.
• Eigenvalues.
• Eigenvectors.
• Probability Basics.
• Conditional Probability.
• Bayes Theorem.
• Statistics.

5

Statistics for AI / ML

• Descriptive Statistics.
• Mean.
• Median.
• Mode.
• Variance.
• Standard Deviation.
• Percentiles.
• Probability Distributions.
• Normal Distribution.
• Binomial Distribution.
• Sampling.
• Hypothesis Testing.
• Confidence Intervals.
• Correlation.
• Covariance.
• Statistical Significance.

6

NumPy

• Introduction to NumPy.
• NumPy Arrays.
• Array Creation.
• Array Indexing.
• Array Slicing.
• Array Reshaping.
• Array Operations.
• Broadcasting.
• Mathematical Operations.
• Statistical Functions.
• Random Module.
• Linear Algebra with NumPy.
• Matrix Operations.
• Performance Optimization.

7

Pandas & Data Analysis

• Introduction to Pandas.
• Series.
• DataFrames.
• Reading CSV Files.
• Reading Excel Files.
• Data Selection.
• Filtering.
• Sorting.
• Grouping.
• Aggregation.
• Missing Values.
• Duplicate Data.
• Data Cleaning.
• Data Transformation.
• Data Export.

8

Data Visualization

• Data Visualization Fundamentals.
• Matplotlib.
• Line Charts.
• Bar Charts.
• Pie Charts.
• Histograms.
• Scatter Plots.
• Box Plots.
• Seaborn.
• Heatmaps.
• Distribution Plots.
• Correlation Visualization.
• Interactive Visualization.
• Data Storytelling.

9

Exploratory Data Analysis

• What is EDA?
• Understanding Datasets.
• Data Profiling.
• Missing Value Analysis.
• Outlier Detection.
• Univariate Analysis.
• Bivariate Analysis.
• Multivariate Analysis.
• Correlation Analysis.
• Feature Relationships.
• Data Distribution.
• Data Quality.
• EDA Project.

10

Machine Learning Fundamentals

• What is Machine Learning?
• Machine Learning Workflow.
• Training Data.
• Validation Data.
• Testing Data.
• Features.
• Labels.
• Model.
• Training.
• Prediction.
• Overfitting.
• Underfitting.
• Bias and Variance.
• Generalization.

11

Supervised Learning

• Supervised Learning Concepts.
• Regression.
• Classification.
• Linear Regression.
• Multiple Linear Regression.
• Polynomial Regression.
• Logistic Regression.
• Decision Trees.
• Random Forest.
• Gradient Boosting.
• XGBoost.
• Support Vector Machines.
• K-Nearest Neighbors.

12

Unsupervised Learning

• Unsupervised Learning Concepts.
• Clustering.
• K-Means Clustering.
• Hierarchical Clustering.
• DBSCAN.
• Dimensionality Reduction.
• PCA.
• Feature Representation.
• Anomaly Detection.
• Customer Segmentation.
• Clustering Evaluation.

13

Scikit-Learn

• Introduction to Scikit-Learn.
• Datasets.
• Train Test Split.
• Preprocessing.
• StandardScaler.
• MinMaxScaler.
• Encoding Categorical Data.
• Feature Selection.
• Pipelines.
• Model Training.
• Model Prediction.
• Cross Validation.
• Hyperparameter Tuning.
• Grid Search.
• Random Search.

14

Machine Learning Evaluation

• Model Evaluation.
• Regression Metrics.
• MAE.
• MSE.
• RMSE.
• R-Squared.
• Classification Metrics.
• Accuracy.
• Precision.
• Recall.
• F1 Score.
• Confusion Matrix.
• ROC Curve.
• AUC.

15

Feature Engineering

• What is Feature Engineering?
• Feature Creation.
• Feature Transformation.
• Feature Scaling.
• Normalization.
• Standardization.
• One Hot Encoding.
• Label Encoding.
• Feature Selection.
• Feature Extraction.
• Handling Missing Data.
• Handling Outliers.
• Feature Importance.

16

Deep Learning Fundamentals

• What is Deep Learning?
• Artificial Neural Networks.
• Neurons.
• Layers.
• Weights.
• Bias.
• Activation Functions.
• Forward Propagation.
• Backpropagation.
• Loss Functions.
• Gradient Descent.
• Learning Rate.
• Epochs.
• Batches.

17

TensorFlow & Keras

• Introduction to TensorFlow.
• Keras.
• Tensors.
• Tensor Operations.
• Sequential Models.
• Functional API.
• Dense Layers.
• Activation Functions.
• Loss Functions.
• Optimizers.
• Callbacks.
• Model Training.
• Model Evaluation.
• Model Saving and Loading.

18

PyTorch

• Introduction to PyTorch.
• Tensors.
• Autograd.
• Neural Networks.
• Datasets.
• DataLoaders.
• Training Loops.
• Optimizers.
• Loss Functions.
• GPU Training.
• Model Saving.
• Model Deployment.

19

Advanced Deep Learning

• Convolutional Neural Networks.
• Recurrent Neural Networks.
• LSTM.
• GRU.
• Autoencoders.
• Transfer Learning.
• Batch Normalization.
• Dropout.
• Regularization.
• Learning Rate Scheduling.
• Early Stopping.
• Model Optimization.

20

Natural Language Processing

• Introduction to NLP.
• Text Processing.
• Tokenization.
• Stop Words.
• Stemming.
• Lemmatization.
• Text Cleaning.
• Bag of Words.
• TF-IDF.
• Word Embeddings.
• Sentiment Analysis.
• Text Classification.
• Named Entity Recognition.

21

Computer Vision

• Introduction to Computer Vision.
• Digital Images.
• Image Processing.
• OpenCV.
• Image Reading.
• Image Resizing.
• Image Filtering.
• Edge Detection.
• Image Classification.
• Object Detection.
• Image Segmentation.
• Face Detection.
• Computer Vision Projects.

22

Transformers & Modern NLP

• Introduction to Transformers.
• Attention Mechanism.
• Self Attention.
• Multi-Head Attention.
• Encoder Architecture.
• Decoder Architecture.
• Transformer Architecture.
• BERT.
• GPT Concepts.
• Tokenization.
• Embeddings.
• Hugging Face.
• Pre-trained Models.

23

Generative AI

• What is Generative AI?
• Generative Models.
• Large Language Models.
• Prompt Engineering.
• Prompt Design.
• System Prompts.
• Few-Shot Prompting.
• Zero-Shot Prompting.
• AI Text Generation.
• AI Image Generation.
• AI Application Development.
• Generative AI APIs.

24

Large Language Models

• LLM Fundamentals.
• LLM Architecture.
• Tokens.
• Context Windows.
• Embeddings.
• Inference.
• Fine-Tuning Concepts.
• Instruction Tuning.
• Model Parameters.
• Temperature.
• Top-K.
• Top-P.
• LLM Evaluation.

25

RAG & Vector Databases

• What is RAG?
• Retrieval Augmented Generation.
• Document Processing.
• Text Chunking.
• Embeddings.
• Vector Search.
• Similarity Search.
• Vector Databases.
• FAISS.
• Chroma.
• Metadata Filtering.
• RAG Pipelines.
• Question Answering Systems.

26

AI Agents

• Introduction to AI Agents.
• Agent Architecture.
• Tools.
• Function Calling.
• Tool Use.
• Memory.
• Planning.
• Reasoning Concepts.
• Agent Workflows.
• Multi-Step Tasks.
• AI Agent Applications.
• Agent Evaluation.

27

ML Model Deployment

• Model Serialization.
• Pickle.
• Joblib.
• REST APIs.
• Flask.
• FastAPI.
• Request Validation.
• Model Prediction API.
• API Documentation.
• Production Model Serving.
• Inference Optimization.

28

MLOps Fundamentals

• What is MLOps?
• ML Lifecycle.
• Experiment Tracking.
• Model Versioning.
• Data Versioning.
• Model Registry.
• ML Pipelines.
• Model Monitoring.
• Data Drift.
• Model Drift.
• Model Retraining.
• Production Monitoring.

29

Docker for AI / ML

• What is Docker?
• Containers.
• Images.
• Dockerfile.
• Docker Commands.
• Docker Compose.
• Python Containers.
• ML API Containers.
• Model Containers.
• Environment Variables.
• Docker Volumes.
• Production Containers.

30

Cloud AI / ML

• Cloud Computing Fundamentals.
• AWS Fundamentals.
• Azure AI Concepts.
• Google Cloud AI Concepts.
• Cloud Storage.
• Cloud Compute.
• GPU Instances.
• Model Deployment.
• Cloud APIs.
• Serverless AI Concepts.
• Cloud Security.
• Production AI Infrastructure.

31

AI / ML System Design

• AI System Architecture.
• ML System Design.
• Data Pipelines.
• Feature Stores.
• Model Serving.
• Batch Prediction.
• Real-Time Prediction.
• Scalable Inference.
• Load Balancing.
• Caching.
• Message Queues.
• High Availability.
• Fault Tolerance.

32

Responsible & Secure AI

• AI Safety Fundamentals.
• Responsible AI.
• Bias in Machine Learning.
• Fairness.
• Model Explainability.
• Interpretability.
• Privacy.
• Data Protection.
• AI Security.
• Prompt Injection Concepts.
• Model Security.
• AI Governance.

33

Advanced AI Engineering

• Production AI Architecture.
• Advanced Model Optimization.
• Inference Optimization.
• Distributed Machine Learning.
• GPU Computing Concepts.
• Model Quantization.
• Model Compression.
• Fine-Tuning.
• Parameter Efficient Fine-Tuning.
• AI Pipelines.
• Advanced MLOps.
• Production AI Systems.

34

AI / ML Interview Preparation

• Python Interview Questions.
• Statistics Questions.
• Mathematics Questions.
• Machine Learning Questions.
• Deep Learning Questions.
• NLP Questions.
• Computer Vision Questions.
• Generative AI Questions.
• LLM Questions.
• RAG Questions.
• MLOps Questions.
• System Design Questions.
• Project-Based Questions.
• Machine Learning Coding Challenges.

35

Real-World AI / ML Projects

• House Price Prediction.
• Student Performance Prediction.
• Customer Churn Prediction.
• Loan Approval Prediction.
• Fraud Detection System.
• Recommendation System.
• Customer Segmentation.
• Sentiment Analysis.
• Spam Detection.
• Chatbot Application.
• Image Classification.
• Object Detection.
• Face Recognition Application.
• AI Resume Analyzer.
• Document Question Answering System.
• RAG Chatbot.
• AI Personal Assistant.
• AI Agent Application.
• Production-Ready AI Platform.

Skills You'll Gain:

• Strong understanding of Artificial Intelligence and Machine Learning.
• Strong Python programming skills for AI and Machine Learning.
• Understanding of Mathematics, Probability and Statistics.
• Ability to perform data cleaning, preprocessing and exploratory data analysis.
• Strong knowledge of NumPy, Pandas, Matplotlib and data visualization.
• Ability to build supervised and unsupervised Machine Learning models.
• Knowledge of Scikit-Learn and Machine Learning pipelines.
• Understanding of feature engineering and model evaluation.
• Ability to develop Deep Learning models using TensorFlow and PyTorch.
• Knowledge of Natural Language Processing and Computer Vision.
• Understanding of Transformers, Large Language Models and Generative AI.
• Ability to build RAG applications using embeddings and vector databases.
• Understanding of AI Agents and modern AI application architecture.
• Ability to deploy Machine Learning models using APIs and cloud platforms.
• Knowledge of Docker, MLOps, model monitoring and CI/CD concepts.
• Understanding of AI system design and scalable production architecture.
• Ability to build production-ready AI/ML applications.
• Preparation for AI Engineer, Machine Learning Engineer, Data Scientist and AI Developer career paths.

Duration:

Typically ranges from 28 to 36 weeks, depending on the course intensity, mathematics practice, coding practice, projects and learning format.

Certification:

Earn a certificate of completion that can be added to your resume, LinkedIn profile and professional portfolio.