All programsAenexz Tech
ISO 9001 Certified
4.9
AI & Machine Learning
Python, NumPy, Pandas, Scikit-learn, TensorFlow, Keras, NLP and MLOps — 10+ micro-projects and one capstone with real datasets like MNIST, CIFAR and IMDB.
Duration
8 Weeks
Level
Beginner → Intermediate
Prerequisites
Basic Computer Skills
Curriculum
Week 11 Week
ML Fundamentals & Python Essentials
Set up your ML toolkit and get comfortable with data in Python.
- What is ML — supervised, unsupervised, reinforcement
- Python essentials: functions, comprehensions, OOP
- NumPy arrays and vectorised operations
- Pandas: DataFrames, cleaning, joins, aggregations
- Matplotlib and Seaborn for EDA
Week 21 Week
Core Algorithms — Supervised Learning
Regression and classification with hands-on Scikit-learn labs.
- Linear and Multiple Regression
- Logistic Regression for classification
- K-Nearest Neighbours (KNN)
- Train/test split, cross-validation
- Confusion matrix, accuracy, precision, recall, F1
Week 31 Week
Unsupervised Learning & Evaluation
Discover structure in unlabelled data and evaluate model quality.
- K-Means clustering and elbow method
- Hierarchical clustering and DBSCAN
- PCA and dimensionality reduction
- ROC-AUC and regression error metrics
- Bias–variance trade-off
Week 41 Week
Feature Engineering & Model Selection
Boost model accuracy with better features and ensemble methods.
- Feature scaling, encoding and handling missing data
- Feature selection techniques
- Grid Search and RandomizedSearch tuning
- Random Forest and Bagging
- XGBoost and Gradient Boosting
Week 51 Week
Deep Learning with TensorFlow & Keras
Build your first neural networks with modern deep-learning frameworks.
- Perceptrons, activation functions and back-propagation
- Building dense networks in Keras
- Optimisers: SGD, Adam, RMSProp
- Loss functions and regularisation (Dropout, BatchNorm)
- TensorBoard for monitoring training
Week 61 Week
CNNs & RNNs Applications
Image and sequence models on real datasets.
- Convolution, pooling and CNN architectures
- Image classification on MNIST and CIFAR-10
- RNNs, LSTMs and GRUs
- Text classification on IMDB reviews
- Transfer learning basics
Week 71 Week
Advanced ML — NLP, Time Series & MLOps
Language models, forecasting and production-readiness.
- NLP pipeline: tokenisation, embeddings, TF-IDF
- Transformers, BERT and GPT overview
- Recommender systems
- Time-series forecasting with ARIMA and Prophet
- MLOps intro: model versioning, monitoring, deployment
Week 81 Week
Capstone Project & Showcase
End-to-end project from proposal to deployment.
- Choose: House Price, Churn, Resume Screener, Sales Forecast, Content Generator
- Project proposal and data exploration
- Model build, evaluation and iteration
- Flask / Streamlit deployment
- Final presentation and mentor review
Tools & Technologies
PythonNumPyPandasMatplotlibSeabornScikit-learnTensorFlowKerasOpenAI APIsJupyterGoogle ColabAnacondaFlaskStreamlitGitHub
Capstone Projects
Capstone
House Price / Churn / AI Resume Screener
Micro
