Certificate in Artificial Intelligence and Machine Learning

• Python Basics and Control Structures

• NumPy for Numerical Computations

• Pandas for Data Manipulation

• Data Visualisation with Matplotlib and Seaborn

• Data Cleaning: Handling missing values, outliers, and duplicates

• Data Transformation: Scaling, normalisation, and encoding categorical variables

• Exploratory Data Analysis (EDA): Visualisation techniques and summary statistics

• Linear Regression: Concepts, assumptions, and implementation

• Evaluation Metrics: Mean Absolute Error, Mean Squared Error, and R-squared

• Logistic Regression: Binary classification, sigmoid function, and cost function

• Evaluation Metrics: Accuracy, precision, recall, F1-score, and ROC-AUC

• Decision Trees: Basics, advantages, and limitations.

• k-Nearest Neighbors (k-NN): Concept and implementation.

• Support Vector Machines (SVM): Basic understanding and applications.

• Model Validation Techniques: Cross-validation and train-test split.

• Hyperparameter Tuning: Basics of grid search and random search.

• Ensemble Methods: Introduction to bagging and boosting.

• K-Means Clustering: Algorithm, choosing k, and evaluation.

• Hierarchical Clustering: Basics and applications.

• Principal Component Analysis (PCA): Concept and implementation.

• t-Distributed Stochastic Neighbor Embedding (t-SNE): Basics and visualisation.

• Neural Networks Basics: Structure, activation functions, and forward propagation.

• Training Neural Networks: Introduction to backpropagation and gradient descent.

• Deep Learning Frameworks: Introduction to TensorFlow and Keras.

• Building Simple Neural Networks: Hands-on practice with basic neural networks.

• Text Preprocessing: Tokenisation, stemming, lemmatisation

• Basic NLP Models: Bag of Words and TF-IDF

• Basic Concepts: Time series data, trend, seasonality, and noise

• Simple Models: Moving averages and exponential smoothing

• Applications: Forecasting and anomaly detection in time series data

• Basic Concepts: Overview of recommender systems and their importance

• Collaborative Filtering: User-based and item-based collaborative filtering

• Content-Based Filtering: Using item features for recommendations

• Hybrid Methods: Combining collaborative and content-based approaches

• Introduction to Generative AI

• Generative Models Overview

• Generative Adversarial Networks (GANs)

• Variational Autoencoders (VAEs)

• Applications of Generative Models

• Setting Up the Environment - Installing necessary Python libraries (TensorFlow, Keras, PyTorch)

• Implementing GANs

• Implementing VAEs

• Image Generation with GANs

• Text Generation with VAEs

• Introduction to Data Augmentation

• Data Augmentation with GANs

• Data Augmentation with VAEs

• Case Studies - Real-world examples of data augmentation in image classification and text analysis

• Impact of Data Augmentation

• Introduction to Synthetic Data

• Generating Synthetic Data with GANs

• Generating Synthetic Data with VAEs

• Case studies on using synthetic data in healthcare, finance, and other industries

• Best Practices and Considerations when using Synthetic data

The programme is delivered in a live online format, featuring interactive sessions with VIT faculty and industry experts. Learners gain practical experience through one of the most engaging machine learning and artificial intelligence courses, working with Python for machine learning and exploring foundational topics like the introduction to artificial intelligence and machine learning. Hands-on projects and case studies ensure real-world skill development in this comprehensive AI and ML course online.

Yes, a career in artificial intelligence and machine learning offers immense potential. With the growing demand for professionals skilled in areas like machine learning using Python, individuals with the right expertise can secure high-paying roles across industries. Completing AI ML certification courses equips you with the technical and strategic capabilities needed to thrive in the evolving tech landscape and explore leadership positions in AI-driven fields.

This artificial intelligence and machine learning programme is ideal for tech professionals, data scientists, and aspiring AI leaders seeking to enhance their skills. It's also suitable for those aiming to transition into AI-focused roles or learn advanced concepts like machine intelligence and Python for machine learning.

This artificial intelligence and machine learning course covers Python, Pandas, Matplotlib, Seaborn, NumPy, and Jupyter Notebook.