Complete machine learning course
Basics of machine learning,Linear Regression,Logistic Regression, Naïve Bayes ,KNN algorithm, K-means, PCA, Custering,
Machine Learning,Data Science and AI ML,Python,Cluster Analysis,Development,Data Science
Lectures -19
Duration -6 hours
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Course Description
We will look first into linear Regression, where we will learn to predict continuous variables and this will include details of Simple and Multiple Linear Regression, Ordinary Least Squares, Testing your Model, R-Squared, and Adjusted R-Squared.
We will get full details of Logistic Regression, which is by far the most popular model for Classification. We will learn all about Maximum Likelihood, Feature Scaling, The Confusion Matrix, Accuracy Ratios.... and you will build your very first Logistic Regression
We will look in to Naïve bias classifier which will give full details of Bayes Theorem, and implementation of Naïve bias in machine learning. This can be used in Spam Filtering, Text analysis, •Recommendation Systems.
Random forest algorithm can be used in regression and classification problems. This gives good accuracy even if data is incomplete.
A Decision Tree is a Supervised learning technique that can be used for both classification and Regression problems, but mostly it is preferred for solving Classification problems.
We will look in to KNN algorithm which will working way of KNN algorithm, compute KNN distance matrix, Makowski distance, live examples of implementation of KNN in industry.
We will look in to PCA, K-means clustering, and Agglomerative clustering which will be part of unsupervised learning.
Along all parts of machine-supervised and unsupervised learning , we will be following data reading , data prerprocessing, EDA, data scaling, preparation of training and testing data along machine learning model selection , implemention and prediction of models.
Goals
- Learner should be able to learn below mentioed topics of machine learning with live examples.
- Basics of machine learning
- Linear Regression
- Logistic Regression
- KNN alogrithm
- Clustering
- K-Means Clustering
- Principal component analysis
- Data preprocessing
- EDA
- The Machine Learning Process
- Naive Bayes Classifier
- Confusion Matrix
- Make Predictions
- Splitting your data into a Training set and a Test set
- Classification
- Decision Tree algorithm
- The person should have a good understanding with making ML models and he should be able to work as ML Engieer.
Prerequisites
- A computer installed with Jupitor notebook
- Wireless adapter with Monitor Mode support
- Minimum of 8GB RAM
- Internect connection
- Leaner should aware of basic programming skills of Python
- The learning should be techincal graduate
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Curriculum
Check out the detailed breakdown of what’s inside the course
Basics of machine learning
3 Lectures
-
Basics of machine learning, data in machine learning 13:57 13:57
-
Supervised learning, Unsupervised learning , advantages and disadvantages of ML 16:01 16:01
-
ML life cycle, Exploratory data analysis , ML Challenges and libraries 36:23 36:23
Linear Regression
3 Lectures
![Tutorialspoint](/market/public/assets/images/loader.gif)
Logistic regression
2 Lectures
![Tutorialspoint](/market/public/assets/images/loader.gif)
KNN Algorithm
2 Lectures
![Tutorialspoint](/market/public/assets/images/loader.gif)
Naïve Bayes Algorithm
2 Lectures
![Tutorialspoint](/market/public/assets/images/loader.gif)
Random forest algorithm
2 Lectures
![Tutorialspoint](/market/public/assets/images/loader.gif)
Decision tree algorithm
2 Lectures
![Tutorialspoint](/market/public/assets/images/loader.gif)
Unsupervised learning
1 Lectures
![Tutorialspoint](/market/public/assets/images/loader.gif)
PCA and live exercise on unsupervised learning
2 Lectures
![Tutorialspoint](/market/public/assets/images/loader.gif)
Instructor Details
![Satyendra singh](https://www.tutorialspoint.com/assets/profiles/657344/profile/200_398674-1692704965.jpg)
Satyendra singh
More than 25 years of experience in the industry and working in the stock market as an independent Investment Consultant, Trainer, and Trader
NCFM Certification:
Technical Analysis Module
Fundamental Analysis Module
Options Strategies Module
Investment Analysis and Portfolio Management
Post-graduation diploma: Computer science and Airticifical intelligence
Aside from lifetime certifications in blockchain, cyber security, and metaverse
Certified blockchain expert
Certified metaverse expert
Certified cyber security expert
NSIM Certification:
NSE Certified Research Analyst
Achievement in the Financial Market
NSE Academy Certified Market Professional (NCMP)- Level 1 Award
October 2019
I give coaching in the following areas and do consultancy in the financial market.
1. Technical Analysis
2. Fundamental Analysis
3. Options Strategies
4. Research Analysis
5. Intra Day and Swing Trading
6. Nifty and Bank Nifty Trading
7. Future Trading
8. Portfolio Management
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