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Basics of machine learning, Linear Regression, Logistic Regression, Naïve Bayes, KNN algorithm, K-means, PCA, Custering
Updated on Sep, 2026
Programming, Data Science, Machine Learning
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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 into the Naïve bias classifier, which will give full details of Bayes' Theorem, and the 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 the data is incomplete.
A Decision Tree is a Supervised learning technique that can be used for both classification and Regression problems, but it is mostly preferred for solving Classification problems.
We will look into to KNN algorithm, which working way of the KNN algorithm, compute the KNN distance matrix, the Makowski distance, and live examples of implementation of KNN in industry.
We will look into 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 preprocessing, EDA, data scaling, preparation of training and testing data, along with machine learning model selection, implementation and prediction of models.
Check out the detailed breakdown of what’s inside the course
More than 25 years of experience in the industry and working in the stock market as an independent Investment Consultant, Trainer, and Trader
Technical Analysis Module
Fundamental Analysis Module
Options Strategies Module
Investment Analysis and Portfolio Management
Post-graduation diploma: Computer science and Airticifical intelligence
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Achievement in the Financial Market
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2. Fundamental Analysis
3. Options Strategies
4. Research Analysis
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8. Portfolio Management
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