<h3><strong>Welcome to Machine Learning 101</strong></h3>
Please find the course outline below. Get the videos as well
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<td><strong>Lecture #</strong></td>
<td><strong>Description</strong></td>
<td><strong>Watch Video</strong></td>
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<td>1</td>
<td><strong><a href=”https://www.kindsonthegenius.com/machine-learning-introduction-to-machine-learning-course/”>Introduction to Machine Learning 101 Course</a></strong> – Welcome to Machine Learning 101! Prerequisite for this course. How the course is arranged. Applications you need. Nuggets. Procedure based.</td>
<td>1. <a href=”https://lnkd.in/dA9AMhR”>https://lnkd.in/dA9AMhR</a></td>
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<td>2</td>
<td><strong><a href=”https://www.kindsonthegenius.com/machine-learning-101-overview-of-machine-learning-and-some-basic-terms/”>Overview of Machine Learning and Some Basic Terms</a></strong> – What is Machine Learning? Application areas of Machine learning. How Machine learning works. Traditional programming vs machine learning. Hand-written digits example. Some terms: training set, target vector. Training or Learning. Test data set. Preprocessing, Feature extraction</td>
<td>2. <a href=”https://lnkd.in/etaqdy5″>https://lnkd.in/etaqdy5</a></td>
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<td>3</td>
<td><a href=”https://www.kindsonthegenius.com/machine-learning-101-classes-of-machine-learning-problems/”><strong>Classes of Machine Learning Problems</strong></a> – Supervised Learning, Unsupervised Learning and Reinforcement Learning. Classification and Regression. Clustering, Density estimation and dimensionality reduction. Credit Assignment</td>
<td>3.<a href=”https://lnkd.in/dbYidEm”> https://lnkd.in/dbYidEm</a></td>
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<td>4</td>
<td><a href=”https://www.kindsonthegenius.com/machine-learning-101-simple-regression-problem/”><strong>How to Solve Simple Regression Problem</strong></a> – What is Regression? Marketing Ads and profits example. Regression by inspection. Types of Regression. How to plot in Python</td>
<td>4.
4a. </td>
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<td>5</td>
<td><a href=”https://www.kindsonthegenius.com/machine-learning-101-equation-for-a-line-and-regression-line/”><strong>Equation of a Regression Line</strong> </a>- Regression example. Determining trend in data. Equation of a straight line. Slope and intercept. Regression in Python</td>
<td>5. <a href=”https://youtu.be/O0PNXhipbf4″>https://youtu.be/O0PNXhipbf4</a></td>
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<td>6</td>
<td><strong><a href=”https://www.kindsonthegenius.com/machine-learning-101-polynomial-curve-fitting/”>Polynomial Curve Fitting</a></strong> – Review of regression. Polynomial coefficients, w. Determining w. Error function</td>
<td>6</td>
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<td>7</td>
<td><a href=”https://www.kindsonthegenius.com/machine-learning-101-overfitting-and-underfitting/”><strong>Overfitting and Underfitting</strong></a> – Reveiw of polynomial curve fitting. Increasing the order M, of the polynomial. How M relates to the error. M being too low vs too high. Trade-off point. Python hands-on</td>
<td>7
7a.</td>
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<td>8</td>
<td><a href=”https://www.kindsonthegenius.com/machine-learning-101-introduction-to-probability-theory/”><strong>Introduction to Probability Theory</strong></a> – Basics of probability. Simple example. Experiments and Random variable. Some common notations. Some quiz.</td>
<td>8.</td>
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<td>9</td>
<td><a href=”https://www.kindsonthegenius.com/machine-learning-101-rules-of-probability-bayes-theorem/”><strong>Rule of Probability and Bayes’ Theorem</strong></a> – Marginal Probability. Joint Probability. Conditional Probability. Sum Rule. Product rule. Deriving Bayes’ theorem. Formulas</td>
<td>9.</td>
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<td>10</td>
<td><a href=”https://www.kindsonthegenius.com/machine-learning-101-application-of-bayes-theorem/”><strong>Application of Bayes’ Theorem in Real Scenario</strong></a> – Scenario problem of finding conditional, marginal and joint probabilities. Using sum rule, product rule and Bayes’ Rule. Exercises. Prio probability. Posterior probability. Independence</td>
<td>10.
10a.</td>
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<td>11</td>
<td><a href=”https://www.kindsonthegenius.com/machine-learning-101-what-is-probability-density/”><strong>What is Probability Density</strong></a> – Continuous variable versus discreet variable, the differences. Probability distribution. Random variable. Probability density. Probability Density Function. CDF and PMF.</td>
<td>11. <a href=”https://bit.ly/2ZvSAIU”>https://bit.ly/2ZvSAIU</a></td>
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<td>12</td>
<td><a href=”https://www.kindsonthegenius.com/machine-learning-101-bias-variance-trade-off/”><strong>Bias/Variance Trade-off</strong></a> – What is Bias-Variance Trade-off? Mean-Square-Error(MSE) formula. Decomposition of MSE. Meaning of Bias and Variance. Model complexity and flexibility. Bias/Variance Trade-off graph. Review of Overfitting and Underfitting</td>
<td>12. <a href=”https://bit.ly/2ZutnOT”>https://bit.ly/2ZutnOT</a></td>
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<td>13</td>
<td><strong><a href=”https://www.kindsonthegenius.com/machine-learning-101-introduction-to-classification/”>Introduction to Classification</a> </strong>- What is Classification? Mean-Square-Error(MSE). Error Rate. Typical classification problem. Cancer diagnosis. Inference an decision. Short Quiz.</td>
<td>13.<a href=”https://youtu.be/_qjvpJiYPB8″> https://youtu.be/_qjvpJiYPB8</a>
13.<a href=”https://youtu.be/A87RIwngDIg”> https://youtu.be/A87RIwngDIg</a></td>
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<td>14</td>
<td><a href=”https://www.kindsonthegenius.com/machine-learning-101-k-nearest-neighbors-classifier/”><strong>The Bayes’ Classifier and How it Works</strong></a> – How the Bayes’ Classifier works. Bayes’ theorem.</td>
<td>14.<a href=”https://youtu.be/vKh8bbxplcg”>https://youtu.be/vKh8bbxplcg</a></td>
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<td>15</td>
<td><a href=”https://www.kindsonthegenius.com/machine-learning-101-k-nearest-neighbors-classifier/”><strong>K-Nearest Neighbors Classifier</strong> </a>- How K-nearest neighbor classifier works. Contrast with Bayes’ classifier. Illustration of KNN. Algorithm of KNN.</td>
<td><a href=”https://youtu.be/GNgo1846F7U” target=”_blank” rel=”noopener noreferrer”>https://youtu.be/GNgo1846F7U</a></td>
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<td>16</td>
<td><a href=”https://www.kindsonthegenius.com/machine-learning-101-minimizing-misclassification-rate-in-bayes-classifier/”><strong>Minimizing Misclassification Rate in Bayes’ Classifier</strong> </a>- Review of Bayes’ Classifier. Review of cancer diagnosis example in classification. Review of Conditional probability. Misclassification and misclassification rate. Minimizing misclassification. Decision boundaries. Review of product rule in probability</td>
<td><a href=”https://youtu.be/Q0WyK89bTLY” target=”_blank” rel=”noopener noreferrer”>https://youtu.be/Q0WyK89bTLY</a></td>
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<td>17</td>
<td><a href=”https://www.kindsonthegenius.com/machine-learning-101-basics-of-logistic-regression/”><strong>Basics of Logistic Regression</strong></a> – Review of Classes of Machine Learning Problems. What is Logistic Regression? Probability Distribution. Logistic Function. Odds and Odds Ratio. Meaning of odds. Logit or log-odds.</td>
<td><a href=”https://youtu.be/HYcXgN9HaTM” target=”_blank” rel=”noopener noreferrer”>https://youtu.be/HYcXgN9HaTM</a></td>
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<td>18</td>
<td><strong><a href=”https://www.kindsonthegenius.com/lecture-17-multiple-linear-regression/”>Introduction to Multiple Linear Regression</a></strong> – The advertising dataset. Importing data to Jupyter Notebook. Determining how predictor variables affect the response variable. Calculating regression coefficients. How multiple regression works. Some python practicals.</td>
<td><a href=”https://youtu.be/qZEZBvBRs98″ target=”_blank” rel=”noopener noreferrer”>https://youtu.be/qZEZBvBRs98</a></td>
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