Download Rock Machine Learning with Python - Multiple LIVE Coding Course By 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.
- 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 plot, Cumulative frequency distribution and ogive, Basic understanding of probablity
- Python Prerequisite : Basic Idea, Data Type, Function, OOPS concepts
Machine Learning is everywhere from Tiktok video suggestion, Facebook friend suggestion, self-driving car to analyzing website data to get more profit ML is used everywhere you nowadays!
Do you want to ROCK the Machine Learning to get Boost in Your CV or to get a New Job?
Than is hands On Course is Just for You!
This course is designed for beginner level students who want to move into the amazing field of Machine Learning and want to Rock their Career!
In this beginner-friendly course you are going to Learn:
Statistics and Exploratory Data Analysis
Simple Linear Regression
Multiple Linear Regression
K-Nearest Neighbour Classifier (KNN)
Support Vector Machine Classifier (SVM)
Naive Bayes Classifier
Artificial Neural Network
Why Choose the Course?
Hands On Coding on Each Topic
Study Note for Each Lecture
Meet Your Instructor:
This Course is taught by Mr. Sourav who has 4 Years Of Experience in AI and ML and worked in different companies.
He has real-life Industry Experience in AI and ML.
This Course Comes with 30 Days Money Back Guarantee. If you are NOT satisfied anyhow you will get your FULL Money Back. No Question Asked.
Who this course is for:
- Anyone interested to learn Machine Learning with Python
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