[100% Off] 52 Week - Certified Applied Ai And Data Science Program
Master Python, data science, machine learning, GenAI, agents, MLOps, governance, and deployment through applied projects
What you’ll learn
- Explain the complete data science lifecycle and frame business problems as practical AI and analytics projects.,Use Python fundamentals
- functions
- modules
- data structures
- files
- exceptions
- and reusable programming practices.,Work efficiently with Jupyter notebooks
- Git
- version control
- reproducibility
- and collaborative development workflows.,Manipulate numerical data using NumPy arrays
- vectorized operations
- indexing
- slicing
- and broadcasting.,Load
- clean
- transform
- join
- aggregate
- and analyze datasets using pandas.,Create effective charts
- dashboards
- and data stories using modern visualization principles.,Apply descriptive statistics
- probability
- statistical inference
- hypothesis testing
- and A/B testing.,Collect data from public datasets
- APIs
- databases
- and web-based sources.,Write SQL queries involving filtering
- grouping
- joins
- subqueries
- and analytical operations.,Design databases
- relational models
- data warehouses
- and end-to-end data pipelines.,Build supervised machine learning models for regression and classification problems.,Evaluate models using cross-validation
- confusion matrices
- precision
- recall
- ROC-AUC
- residual analysis
- and other metrics.,Apply decision trees
- random forests
- gradient boosting
- k-nearest neighbors
- and Naive Bayes.,Perform feature engineering
- categorical encoding
- scaling
- feature selection
- and leakage prevention.,Apply clustering
- PCA
- t-SNE
- UMAP
- recommender systems
- time-series forecasting
- and anomaly detection.,Interpret model behavior using feature importance
- SHAP concepts
- partial dependence
- error analysis
- and model cards.,Build foundational neural networks and understand activation functions
- backpropagation
- optimizers
- and regularization.,Develop introductory computer vision
- natural language processing
- and transformer-based workflows.,Use generative AI
- prompt engineering
- retrieval-augmented generation
- vector databases
- and hallucination-reduction techniques.,Design AI agents with tool use
- function calling
- planning
- routing
- memory
- and multi-step workflows.,Package
- deploy
- serve
- monitor
- version
- and maintain models using MLOps practices.,Apply cloud
- container
- orchestration
- security
- privacy
- responsible AI
- and governance principles.,Develop an enterprise AI strategy
- prioritize use cases
- assess data readiness
- and plan solution architecture.,Complete and present a portfolio-ready capstone project supported by documentation
- evaluation
- deployment
- and governance reviews.
Requirements
- No previous artificial intelligence
- machine learning
- or data science experience is required.,The program begins with foundational concepts and gradually advances toward enterprise-level implementation.,Basic computer
- file-management
- web-browsing
- and problem-solving skills are recommended.,A computer capable of running Python
- Jupyter notebooks
- and standard data science libraries is required.,A reliable internet connection is useful for installing tools
- accessing datasets
- and completing research.,No advanced mathematics
- statistics
- or programming background is required before beginning.,Learners should be prepared to practice coding regularly and complete weekly labs
- reviews
- and projects.,Familiarity with spreadsheets or basic business data can be helpful but is not mandatory.,Access to a code editor
- GitHub account
- and notebook environment will support portfolio development.,Cloud access may be useful for selected deployment exercises
- but many concepts can be practiced locally.,Students should be willing to troubleshoot errors
- document their work
- review feedback
- and improve projects iteratively.,Consistency
- curiosity
- and a commitment to completing the year-long learning journey are the most important prerequisites.
Description
This course contains the use of artificial intelligence.
52-Week Certified Applied AI and Data Science Program is a comprehensive, year-long learning journey designed to take students from complete beginner to confident applied AI practitioner. Through daily lessons, hands-on labs, weekly reviews, practical projects, and a portfolio-ready capstone, you will develop the technical and business skills required to build modern data and artificial intelligence solutions.
The program begins with Python programming, development environments, Jupyter notebooks, Git, reproducibility, and collaborative workflows. You will then learn NumPy, pandas, data cleaning, feature creation, visualization, descriptive statistics, probability, statistical inference, hypothesis testing, and A/B testing.
You will develop practical data engineering and analytics skills by collecting information from datasets and APIs, writing SQL queries, understanding relational databases, designing data warehouses, and creating ingestion, transformation, validation, storage, and monitoring pipelines.
The machine learning portion introduces supervised learning, regression, classification, cross-validation, feature engineering, model selection, and performance evaluation. You will work with logistic regression, decision trees, random forests, gradient boosting, k-nearest neighbors, and Naive Bayes. You will also explore unsupervised learning, clustering, dimensionality reduction, recommender systems, anomaly detection, and model interpretation.
Specialized modules cover time-series forecasting, neural networks, deep learning, computer vision, natural language processing, embeddings, attention, BERT, GPT, and transformer workflows. These topics provide a strong foundation for understanding modern AI applications.
The program then moves into generative AI, prompt engineering, retrieval-augmented generation, vector databases, hallucination reduction, and agentic AI. You will learn how agents use tools, call functions, plan tasks, maintain memory, and complete multi-step workflows.
Production readiness is addressed through MLOps, experiment tracking, model packaging, versioning, deployment, APIs, batch and real-time inference, monitoring, alerting, containers, workflow orchestration, scalable processing, GPUs, and cloud cost awareness.
You will also study AI security, data privacy, prompt injection, fairness, explainability, responsible AI, governance frameworks, risk registers, controls, auditability, and compliance. Enterprise strategy modules connect technical implementation with use-case prioritization, stakeholder alignment, operating models, solution architecture, and business value.
During the final eight weeks, you will design, build, test, document, deploy, and present a complete capstone solution. You will also create a GitHub portfolio, case study, resume narrative, LinkedIn story, and career roadmap.
By graduation, you will have a broad and practical foundation in data science, machine learning, deep learning, generative AI, AI agents, MLOps, governance, and enterprise AI delivery.
