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CERTIFIED PROGRAM
INDUSTRY READY TRAINING
Build a Successful AI & Machine Learning Career in Just 6 Months
Master Python, Data Science, Machine Learning & Deep Learning with hands-on projects and real-world deployment.
Begin Your AI & ML Career Journey with Bexo.ed
01
Foundation (Month 0 - 1)
Learn Python basics, data structures, and data handling using NumPy & Pandas. Work on your first mini project with real datasets like IPL or COVID data.
02
Math & Visualization (Month 1 - 3)
Build strong foundations in Statistics, Linear Algebra & Calculus for ML. Create data visualizations using Matplotlib & Seaborn and develop an EDA dashboard.
03
Machine Learning Core (Month 3 - 6)
Understand regression, classification models, and ML workflows. Build projects like Housing Price Prediction and Customer Churn Analysis.
Professional AI & Machine Learning Training in Bexo.ed
The AI & Machine Learning Program at Bexo.ed is a career-focused training experience designed to build strong foundations in Python, data analysis, and machine learning. The program emphasizes hands-on learning with real-world projects and industry-relevant tools, helping students understand how intelligent systems are developed and deployed. By the end of the course, learners gain practical skills, project experience, and a professional portfolio that prepares them for real industry roles.
- Industry-oriented curriculum
- Hands-on projects every phase
- Real data, real case studies
- Portfolio-ready AI projects
AI & ML Library
Explore the essential tools and AI platforms that power modern machine learning workflows.
What you Learn in 12 Weeks?
In just 6 months, this intensive AI & ML program transforms you from a beginner into an industry-ready professional capable of building, training, and deploying intelligent systems.
Week 1-2 Python Programming & Data Foundations
- Python Basics: Variables, Data Types, Control Flow, Functions
- Core Programming Skills: Loops, File Handling, Error Handling
- Data Structures: Lists, Dictionaries, Sets, Tuple
- Working with Data: NumPy for arrays, Pandas for tabular data
- Mini Project: Data Cleaning & Analysis on a real dataset (e.g., IPL or COVID)
- Data Cleaning & Analysis: Handling missing values, grouping, sorting.
Week 3-4 Visualization & Mathematical Foundations
- Visualization Tools: Matplotlib, Seaborn (Bar, Line, Heatmaps, Pairplots)
- Statistics & Probability: Mean, Median, Mode, Variance, Distribution
- Linear Algebra: Vectors, Matrices, Matrix Multiplication
- Calculus for ML: Derivatives, Gradients, Chain Rule
- Mini Project: Exploratory Data Analysis (EDA) Dashboard
Week 5-6 Classical Machine Learning
- ML Workflow: Problem framing, Preprocessing, Splits, Pipelines
- Regression Models: Linear Regression, Polynomial Regression
- Classification Models: Logistic Regression, K-Nearest Neighbors
- Model Evaluation: Accuracy, Confusion Matrix, Precision, Recall, ROC-AUC
- Mini Project: Housing Price Predictor / Customer Churn Classifier
Week 7-8 Advanced ML & Deployment
- Tree-Based Models: Decision Trees, Random Forests, XGBoost
- Clustering Techniques: K-Means, DBSCAN, Hierarchical Clustering
- Dimensionality Reduction: PCA, t-SNE (Intro)
- Model Tuning: GridSearchCV, Cross-Validation
- Deployment Tools: Streamlit App, Pickle/Joblib, Render/ Vercel Hosting
- Mini Project: Streamlit-based ML App with Hosted Deployment
Week 9-10 Deep Learning & Neural Networks
- Intro to DL: Perceptrons, Neurons, Activation Functions
- TensorFlow & Keras Basics: Layers, Optimizers, Loss Functions
- CNNs: Image Processing, Filters, Pooling, Augmentation
- RNNs (Intro): Sequences, Time Series, Simple LSTM
- Mini Project: Image Classifier (e.g., CIFAR10 / MNIST)
Week 11-12 NLP & Final Capstone
- NLP Basics: Text Cleaning, Tokenization, Lemmatization
- Text Vectorization: Bag of Words, TF-IDF, Word2Vec
- NLP Applications: Sentiment Analysis, Chatbot (basic)
- Capstone Planning: Team/solo project scoping
- Final Project Week: Build, test, and deploy a complete AI/ ML system
- Demo & Portfolio: GitHub upload, Streamlit live link, Resume Tips
