Certified Machine Learning Professional

Course details

• What is Machine Learning
• Types of Machine Learning
• How does a Machine Learning Algorithm Works
• Parametric and Non-Parametric Algorithms
• Regression and Classification
• Clustering
• Preparing Your Data
• Outliers
• Problem of Underfitting and Overfitting
• The Bias-Variance trade-off
• Intro to jupyter notebooks
• Data Science packages

• What is Regression Analysis
• Linear Regression
• Cost Function
• Gradient Descent
• Polynomial Regression
• Logistic Regression
• Cost Function for Logistic Regression
• Regularization
• Evaluating a Machine Learning Model

• Introduction to Conditional Probability
• Bayes Rule
• Bayesian Learning
• Naïve Bayes Algorithm
• Test your understanding of Bayes Theorem
• Solution – Test Your Understanding of Bayes Theorem
• Bayes Net
• Markov Chains

• Decision Trees
• Gini Index
• ID3 Algorithm – Entropy
• ID3 Algorithm – Information Gain
• Practice Example – Information Gain

• House Price Predictions

• SMS Spam Classifier

• What is Ensemble Learning
• Bagging
• Random Forest Algorithm
• Boosting

• Introduction to Support Vector Machines
• Support Vectors
• Kernel
• Hyperparameters in SVMs

• What is Instance Based Learning
• K-Nearest Neighbours Algorithm
• Dimensionality Reduction
• Principle Component Analysis
• Feature Scaling
• K-Means Algorithm

• Breast Cancer Prediction

• Introduction to Deep Learning
• Perceptron
• Perceptron Exercise
• Solution – Perceptron Exercise
• Deep Neural Networks
• Deep Neural Networks – 2
• Activation Functions - 1
• Activation Functions - 2
• Backpropagation Algorithm
• Convolutional Neural Nets

• Image Classification using Deep Learning