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  • Learning will get expertise in Machine learning and he will be able to work as data scientest/ ML engineer.

This course will cover following topics

1. Basics of machine learning

2. Supervised and unsupervised learning

3. Linear regression

4. Logistic regression

5. KNN Algorithm

6. Naïve Bayes Classifier

7.  Random forest Algorithm

8. Decision Tree Algorithm

7. Principal component analysis

8. K-means clustering

9. Agglomerative clustering

10. There will practical exercise based on Linear regression, Logistic regression ,Naive Bayes, KNN algorithm, Random forest, Decision tree, K-Means, PCA .

11. There will be quiz for each topics and total 200 Questions on machine learning course

 

We will look first in to linear  Regression, where we will learn to predict continuous variables and this will 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, 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.

 

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, Agglomerative clustering which will be part of unsupervised learning.

Along all part 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.

  • Person should know basic programming of Python. He should have laptop with good processing capacity along Anaconda software installed him laptop or disktop.
  • The students should be engineering graduate preferable done engineering in computor science or electronics and communication engineering. Course is good for those who wants to be datas scientest/ Machine learning engineer.
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  • Section 1 : Basics of machine learning 3 Lectures 00:52:24

    • Lecture 1 :
    • Basics of machine learning, data in machine learning Preview
    • Lecture 2 :
    • Supervised learning, Unsupervised learning , advantages and disadvantages of ML
    • This video will cover Supervised learning, Unsupervised learning , advantages and disadvantages of ML.
    • Lecture 3 :
    • ML life cycle, Exploratory data analysis , ML Challenges and libraries
    • This video will cover ML life cycle,  Exploratory data analysis , ML Challenges and libraries.
  • Section 2 : Linear Regression 3 Lectures 01:29:12

    • Lecture 1 :
    • Linear and multiple linear regression, cost function, gradient decent method
    • This video will cover Linear and multiple linear regression, cost function, gradient decent method.
    • Lecture 2 :
    • practical exercise - car price prediction model using linear regression
    • We will do predict car price using linear regression.
    • Lecture 3 :
    • Assumptions, Advantages and disadvantage, best practices, MAE, MAPE,MSE L regres
    • This video will cover Assumptions, Advantages and disadvantage, best practices, MAE, MAPE,MSE of linear regression.
  • Section 3 : Logistic regression 2 Lectures 00:46:50

    • Lecture 1 :
    • Logistic regression
    • Where to use logistic regression and its definition, types of logistic regression,Logistic Function,Application and Assumption in a Logistic Regression Algorithmof logistic regression,flow chart
    • Lecture 2 :
    • pratical exerice - Heart disease analysis using logistic regression
    • We will do predict heart disease prediction using Logistic regression.
  • Section 4 : KNN Algorithm 2 Lectures 00:37:03

    • Lecture 1 :
    • KNN Algorithm
    • KNN Algorithm, Working of KNN Algorithm,Selection of K value, When we use KNN algorithm,Compute KNN: distance metrics,Minkowski distance,Hamming distance,Pros and Cons of KNN
    • Lecture 2 :
    • Practical exercise using KNN Algorithm for Tumor classification
    • We will do predict Tumor classification using KNN Algorithm .
  • Section 5 : Naïve Bayes Algorithm 2 Lectures 00:20:38

    • Lecture 1 :
    • Naïve Bayes Algorithm
    • Lecture 2 :
    • Practical excerise using Navie Bayes for SPAMs
    • We will do predict SPAMs prediction using Navie Bayes Alogrithm
  • Section 6 : Random forest algorithm 2 Lectures 00:29:35

    • Lecture 1 :
    • Random forest alorgthim
    • RANDOM FOREST IN CLASSIFICATION AND REGRESSION,Working of Random Forest Algorithm,Pros and Cons of Random Forest,Random Forest Applications,Assumptions for Random forest,Reason for using Random forest,Random forest implementation steps,Random forest flow chart.
    • Lecture 2 :
    • Practical example using Random forest algorithm
    • We will do Climate Prediction using Random Forest algorithm.
  • Section 7 : decision tree algorithm 2 Lectures 00:21:28

    • Lecture 1 :
    • decision tree algorithm
    • Lecture 2 :
    • Practical example using decision tree algorithm
    • We will do exercise on disease prediction using decision tree algorithm.
  • Section 8 : Unsupervised learning 1 Lectures 00:22:04

    • Lecture 1 :
    • Unsupervised learning , type of unsupervised learning, adv and disadvantages etc
    • This video covers basics of unsupervised learning, difference between supervised and unsupervised learning, types of unsupervised learning, clustering types, basics of Hierarchical Clustering ,Agglomerative clustering, Dendrogram, K-nearest neighbors ,unsupervised learning applications and disadvantages of unsupervised learning, summary of unsupervised learning.
  • Section 9 : PCA and live exercise on unsupervised learning. 2 Lectures 00:42:42

    • Lecture 1 :
    • Principal component analysis
    • Lecture 2 :
    • live exercise on unsupervised learning
    • basics of PCA,PCA steps of implementation, Reason for using PCA and its applications, working of PCA,Terms used in PCA algorithm etc.
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More than 25 years of experience in  industry and working in stock market as 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 life time certifications on blockchain, cyber security and metaverse Certified blockchain expert Certified metaverse expert Certified cyber security expert NSIM Certification: NSE Certified Research Analyst     Achievement in financial Market NSE Academy Certified Market professional (NCMP)- Level 1 Award October 2019 I give coaching in following area and doing consultancy in 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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