Specialization Certificate in AI & Machine Learning
Specialization Certificate Program
The Specialization Certificate in AI & Machine Learning is an intensive, hands-on program designed to equip learners with cutting-edge skills in data-driven decision-making, predictive modeling, and intelligent system design. Spanning 200 hours, the program blends theoretical foundations with real-world applications using modern tools and frameworks. Learners will develop practical expertise in machine learning, deep learning, and AI deployment to solve complex business and societal challenges.
Duration
200+ hours
26 weeks @ 8 hours per week
OR
8 weeks @ 25 hours per week
Session Days
Weekdays
Weekends
Course Delivery
Classroom
Live Remote
Target Audience
Students, Professionals
Level
Foundation to Intermediate
Cost
USD 399
* Installments options are available
Program Features
Why to Study This Program?
Skills Learners Will Gain
Program Learning Outcomes
By the end of the program, learners will be able to:
Target Job Roles
Curriculum
▸ Introduction to Machine Learning
▸ Introduction to Machine Learning
▸ Types of ML (Supervised, Unsupervised, Reinforcement)
▸ ML lifecycle
▸ Probability for Machine Learning (Probability rules, distributions, Bayes theorem)
▸ Statistics for Machine Learning (Descriptive & inferential statistics, Hypothesis testing)
▸ Generalization & Bias-Variance Trade-off (Overfitting vs underfitting, Cross-validation)
▸ Evaluating Predictive Performance (Accuracy, precision, recall, F1-score, Confusion matrix)
▸ Advanced Evaluation (ROC curves, AUC, Model robustness)
▸ Python Programming Basics (Syntax, variables, data types)
▸ Control structures & Operators (Conditions, Loops)
▸ Functions & Error Handling
▸ File Handling (Reading/writing files)
▸ NumPy Fundamentals (Arrays, operations, matrix operations)
▸ Pandas for Data Analysis (DataFrames, cleaning, data wrangling)
▸ Data Visualization (Charts, plots)
▸ Nearest Neighbour Methods (k-NN algorithm)
▸ Decision Trees (Tree construction, pruning)
▸ Naive Bayes (Probabilistic classifiers)
▸ Logistic Regression (Classification techniques)
▸ Support Vector Machines (Hyperplanes, kernels, SVM classifier)
▸ Bayesian Optimization (Parameter tuning, Hyperparameter tuning)
▸ Unsupervised Learning (Clustering, K-means, Hierarchical)
▸ Principal Component Analysis (Dimensionality reduction)
▸ Introduction to Deep Learning
▸ Neural Networks (Forward/backpropagation)
▸ Hyperparameters (Learning rate, epochs)
▸ Convolutional Neural Network (CNN, Image processing, Image classifier)
▸ Biological Basis of CNNs (Inspiration from vision systems)
▸ Transparency & Interpretability (Explainable AI (XAI), Model interpretation)
▸ Reinforcement Learning (Agents, rewards)
▸ Hyperparameter Tuning (Grid search, random search)
Project Work
▸ Problem identification
▸ Data collection & preprocessing
▸ Model selection & training
▸ Evaluation & optimization
▸ Deployment-ready prototype
Tools
▸ Python, scikit-learn, TensorFlow
▸ Jupyter Notebook
▸ GitHub for version control
Deliverables
▸ Working AI model
▸ Project report
▸ Presentation & demo