Artificial Intelligence & Machine Learning
Become a job-ready AI & Machine Learning Engineer with Python, statistics, machine learning, deep learning, NLP, LLMs, and computer vision. Build real, portfolio-worthy AI projects from data to deployment. Start free, go job-ready, and launch your career in AI.
In short: This Artificial Intelligence & Machine Learning program is a 60–65 hour, fully online, job-ready course. You start with 15 hours of free foundations (Python, math & statistics, data handling, Git) and continue into a 45–50 hour paid program covering data analysis, supervised and unsupervised machine learning, deep learning, NLP and LLMs, computer vision, and MLOps deployment — finishing with a deployed capstone project, a portfolio, and full interview prep.
The Artificial Intelligence & Machine Learning course is designed to help learners master the complete AI/ML workflow — from data to trained models to real-world deployment — and become immediately employable as AI/ML Engineers and Data Scientists.
This course covers Python for data science, the essential math and statistics behind machine learning, data wrangling and exploratory data analysis, supervised and unsupervised learning, model evaluation and tuning, deep learning with neural networks, natural language processing and large language models (LLMs), computer vision, and MLOps for deploying models to production. Learners build real, portfolio-worthy AI projects at every stage using hands-on, project-based learning.
By completing this training, participants will be well-prepared for Machine Learning Engineer, AI Engineer, and Data Scientist roles, with a polished portfolio, a strong resume, and interview readiness.
Course Highlights
- End-to-end AI/ML coverage from Python foundations to production deployment
- Hands-on machine learning with scikit-learn on real datasets
- Deep learning with neural networks using TensorFlow & Keras
- Modern NLP, transformers, and large language models (LLMs)
- Computer vision, MLOps, and model deployment to the cloud
- Job-readiness: portfolio, resume, and interview prep
Learning Outcomes
- Analyze and visualize data with Python, NumPy, and Pandas
- Build, evaluate, and tune machine learning models
- Design and train deep neural networks
- Apply NLP and LLMs to real text problems
- Build computer vision models for image tasks
- Deploy machine learning models to production with MLOps
Job Opportunities
- Machine Learning Engineer
- Artificial Intelligence (AI) Engineer
- Data Scientist
- Data Analyst
- NLP / Computer Vision Engineer
- MLOps Engineer
What You'll Build (Portfolio Projects)
- An exploratory data analysis (EDA) report on a real-world dataset
- A predictive machine learning model (classification & regression)
- A deep learning image classifier with TensorFlow/Keras
- An NLP / LLM-powered text application
- A deployed end-to-end capstone ML project
Tools & Skills Covered
- Python, NumPy, Pandas, Matplotlib, Seaborn
- scikit-learn, TensorFlow, Keras
- Git, GitHub, Docker, and cloud deployment
Material Includes
- Class recordings
- Datasets & project starter code
- Certificate
Who Can Join
- IT Graduates
- Aspiring AI/ML Engineers
- Career Switchers
Related Courses
Free Foundation — 15 Hours
Start learning for free. Build your core Python, math, and data foundations before advancing to the job-ready AI/ML program.
Estimated Time: 5 Hours
Learning Objectives:- Write and run Python programs confidently
- Use core programming logic to prepare for data science
- Python setup, syntax, variables, and data types
- Conditionals, loops, functions, and modules
- Lists, dictionaries, and file handling for data
- Build Python scripts that read, clean, and summarize data
Estimated Time: 4 Hours
Learning Objectives:- Understand the math that powers ML algorithms
- Apply statistics to reason about data
- Linear algebra essentials (vectors, matrices)
- Probability and descriptive statistics
- Derivatives and gradients intuition for optimization
- Compute statistics and visualize distributions in Python
Estimated Time: 4 Hours
Learning Objectives:- Manipulate numerical data efficiently with NumPy
- Load, clean, and transform datasets with Pandas
- NumPy arrays and vectorized operations
- Pandas Series, DataFrames, indexing, and grouping
- Handling missing data and merging datasets
- Clean and analyze a real CSV dataset with Pandas
Estimated Time: 2 Hours
Learning Objectives:- Track code and notebooks with Git
- Collaborate and showcase work on GitHub
- Repositories, commits, and branches
- Merging, pull requests, and conflict resolution
- Building a professional data science GitHub profile
- Push your first notebooks and projects to GitHub
Paid Job-Ready Program — 45–50 Hours
Go from foundations to fully employable. Master machine learning, deep learning, NLP, LLMs, computer vision, and MLOps deployment — everything you need to land your first AI/ML job.
Estimated Time: 5 Hours
Learning Objectives:- Turn raw, messy data into analysis-ready datasets
- Uncover patterns through exploratory data analysis
- Data cleaning, encoding, and outlier handling
- Feature scaling and transformation
- Exploratory data analysis (EDA) techniques
- Produce a full EDA report on a real-world dataset
Estimated Time: 4 Hours
Learning Objectives:- Communicate insights with clear visualizations
- Build dashboards that tell a data story
- Matplotlib and Seaborn plotting
- Choosing the right chart for the message
- Interactive dashboards and reporting
- Build a visual data-story dashboard from a dataset
Estimated Time: 6 Hours
Learning Objectives:- Build classification and regression models
- Understand how core ML algorithms work
- Linear & logistic regression
- Decision trees, random forests, and gradient boosting
- k-NN, SVMs, and the scikit-learn workflow
- Train and compare predictive models on real data
Estimated Time: 4 Hours
Learning Objectives:- Discover structure in unlabeled data
- Reduce dimensionality for better models
- K-Means and hierarchical clustering
- Principal Component Analysis (PCA)
- Anomaly detection and customer segmentation
- Segment customers with clustering and PCA
Estimated Time: 5 Hours
Learning Objectives:- Measure and improve model performance
- Engineer features that boost accuracy
- Train/test splits, cross-validation, and metrics
- Bias-variance, overfitting, and regularization
- Hyperparameter tuning and feature engineering pipelines
- Tune and validate a model to production quality
Estimated Time: 7 Hours
Learning Objectives:- Design and train deep neural networks
- Apply deep learning to real problems
- Neurons, activation functions, and backpropagation
- Building networks with TensorFlow and Keras
- CNNs, RNNs, and training best practices
- Build and train a deep learning image classifier
Estimated Time: 5 Hours
Learning Objectives:- Process and model human language
- Build applications with modern LLMs
- Text preprocessing, embeddings, and sentiment analysis
- Transformers and how large language models (LLMs) work
- Prompting and building LLM-powered apps
- Build an NLP/LLM-powered text application
Estimated Time: 4 Hours
Learning Objectives:- Build models that understand images
- Apply transfer learning to vision tasks
- Image data, convolutions, and CNN architectures
- Image classification and object detection basics
- Transfer learning with pre-trained models
- Build an image classifier using transfer learning
Estimated Time: 4 Hours
Learning Objectives:- Deploy machine learning models to production
- Serve and monitor models reliably
- Packaging models and building prediction APIs (Flask/FastAPI)
- Containerizing with Docker and cloud deployment
- Model versioning, monitoring, and CI/CD basics
- Deploy a trained model as a live API
Estimated Time: 4 Hours
Learning Objectives:- Ship a complete, portfolio-ready AI/ML project
- Prepare for the AI/ML job search
- End-to-end capstone project planning and build
- Portfolio, GitHub, and resume optimization
- ML interview, case-study, and coding prep
- Build and deploy an end-to-end capstone ML project
- Mock interviews and resume review