Professional Certificate in Artificial Intelligence and Machine learning by IBM

Introduction to Machine Learning & Python

• Introduction to Machine Learning in Python
• Introduction to Python
• Flow Control, Iterative Operations & Functions in Python
• Basic Statistics & Maths
• Data Summary, Numerical and Visual EDA in Python
• Data Handling in Python using NumPy & Pandas
• Hypothesis Testing
• Basics of Machine Learning-1
• Basics of Machine Learning-2

Advanced Machine Learning Models in Python

• Generalised Linear Models in Python
• Decision Tree Models using Python
• Boosting Algorithms using Python
• Support Vector Machines (SVM), Naïve Bayes and KNN in Python
• Unsupervised learning in Python
• Neural Networks
• Text Mining in Python

Machine Learning Beyond Traditional Model Building

• Ensemble Methods for mixed algorithms
• Python pipelines and model in production
• Advanced Ideas on Feature Engineering, Model Interpretation, Parameter Tuning, Genetic Algorithm

Introduction to Deep Learning (Artificial Intelligence) And Tensorflow

• Introduction to AI and Deep Learning
• Introduction to parameter optimisation and gradient descent
• Getting Started with Tensorflow and Google Colab

Deep Learning Algorithms Using Tensorflow and Keras

• Deep Feed Forward & Convolutional Neural Networks
• Introduction to Keras
• Image data generators, transfer learning and Tensorboard
• Sequence to Sequence models with Recurrent Neural Networks, Long-Short Term Memory (LSTM) and Gated Recurrent Unit (GRU)
• Unsupervised Deep Learning

Applications of AI – Hands-on

• Object Detection and Localisation
• Face Detection and Recognition

Other Applications

• working with Audio Data
• Text summarisation, Image Captioning And Style Transfer

Natural Language Processing

• Deep Learning for Natural Language Processing
• Chatbots with Rasa