Learn Hands On What you'll learn 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 "MatDescriptionlib" for visualization and Descriptionting, 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.
Requirements Mathematics Prerequisite : Basic concepts of Function & Curve tracking, basics of Multivariable Calculus : Partial Derivatives, Optimization : finding maxima and minima of a function, Linear Algebra: Vector & Matrices Statistics Prerequisite : Basic Concepts of frequency distribution and histogram Description, Cumulative frequency distribution and ogive, Basic understanding of probablity Python Prerequisite : Basic Idea, Data Type, Function, OOPS concepts Description This course will guide you to learn this thing: Installation of Anaconda Distribution and Jupyter Notebook Introduction to NumpyIntroduction to MatDescriptionlib Introduction to PandasProbablity Theory IntroductionExploratory Data Analysis Basic ConceptsDistribution of Variable Anyone interested in Machine learning can take the course. Here in the course, we are going to use Python as a Programming Language. Who this course is for: Anyone interested to learn Machine Learning with Python Homepage https://www.udemy.com/course/machine-learning-with-python-/