Rating 3.62 out of 5 (8 ratings in Udemy)

• Machine Learning, Deep Learning, AI and Data Science Basic Concepts
• Applications of ML/AI/DS and Job prospects
• Supervised, Un-supervised Learning
• Environment Setup : Anaconda and Jupyter Notebook
• Python package “Numpy” for numerical computation, Python package “Matplotlib” for visualization and plotting, Python package “pandas” for data analysis
• Basics of Probability Theory
• Understanding different types of data
• Examining distribution of the variables
• Examining relationship among variables
• Exploratory data analysis using Python
• Linear regression model / hypothesis
• Linear regression on bi-variate data
• Multivariate Regression
• Polynomial regression
• Python implementation of Gradient descent algorithm for regression.
• Using in-built Python libraries for solving linear regression problem.
• Logistic regression for binary classification problem.
• Logistic regression for multiclass classification problem.
• Python implementation of Gradient Descent update rule for logistic regression.
• Using Python built in library for logistic regression problem.
• K-Nearest Neighbour Classifier, Naïve Bayes Classifier, Decision Tree Classifier, Support Vector Machine Classifier, Random Forest Classifier (We shall use Python built-in libraries to solve classification problems using above mentioned classification algorithms)
• High dimensionality in data set and its problems.
• Linear Algebra Review: Eigen value decomposition.
• Feature Selection and Feature Extraction techniques
• Principal Component Analysis (PCA)
• Implementation of PCA in python.
• k-Means clustering algorithm and its limitation
• Implementation of k-Means clustering algorithm in python
• Hierarchical Clustering.
• Implementation of Hierarchical clustering in Python.
• Perceptron and its learning rule and its limitations.
• Multi-layered Perceptron (MLP) and its architecture.
• Learning Rule : Back-Propagation
• Building an MLP in Python.