This course kicks off August 1, 2019. It will run for 6 to 8 weeks.
Download Course Outline in Excel
You will find much practicals and labs to play with. So take some time to go through the Course outline below and let us know what you think.
Note: Some of the sections may overlap and that is perfectly fine.
| PART 1 – INTRODUCTION | ||||
| 1 | Introduction to AI and Machine Learning | Introductory notes. Computer Specification. Explains how the course is arranged, the prerequisites. Relevant applications to be covered. Aspects and branches of AI. AI vs ML vs DL. Machine Learning vs Data Science vs Statistics. Relevance of Mathematics | Set expectations for the Course | |
| 2 | Review of Relevant Math topics | Applicable mathematics topics including: Set Theory, Matrices, Calculus(differentiation and integration), Linear and Non-linear algebra, Statistics | Ability to solve Math problems by hand | |
| 3 | Overview of Machine Learning and Some Basic Terms | What is Machine Learning? How Machine Learning is applied in problem solving. Conventional analysis and programming approaches versus machine learning. Illustration using the handwritten digits example. Some terms explained: training set, target vector, test set, validation set, feature set etc. Training or Learning process. Preprocessing. Feature extraction | Understand the applications areas of Machine learning and machine learning terms | |
| 4 | Practical 1 | Python Installation and Setup using Anaconda. Setup of RStudio. Introduction to Jupiter Notebook. Using Idle Python. Getting around in the Jupiter Notebook IDE. Performing Mathematical Operations in Python and R. | Understand how to setup relevant IDEs | Yes |
| PART 2 – PROGRAMMING | ||||
| 5 | Practical 2 | Installation and Setup of PyCharm (Optional). Programming basics with Python: Data Types, Variables, Loops, Conditional Statements, Lists, Sets, Tuples and Dictionaries. Introduction to Module | Be able to write and run Python Programs in an IDE | Yes |
| PART 3 – SUPERVISED LEARNING (REGRESSION) | ||||
| 6 | Classes of Machine Learning Problems | Supervised, Unsupervised and Reinforcement Learning. Further subdivisions. Give examples of each | Ability to differentiate between various classes of ML problems | |
| 7 | Practical 3 | Dataset Acquisition. Explore online dataset repositories. Import tabular data(txt, csv, xlsx) into Python. Export data from Python to spreadsheets. Getting datasets from R Libraries | Scholar should be able to acquire and manipulate datasets | Yes |
| 8 | Approach to Simple Linear Regression | Review of Linear Regression. Illustrate Regression using the Marketing Ads dataset. Deduce the Equation of a regression by parametric approach. States the types of Regression. Concept of Inference and Prediction | Be able to perform regression analysis | |
| 9 | Practical 4 | Plotting in Python: Types of Plots. | Scholar should be able to create different types of plots in Python | Yes |
| 10 | Analyzing the Equation of a Regression Line | Analyzing the Equation of a linear regression line (y = mx + c). Slope and Intercept | ||
| 11 | Practical 5 | Linear Regression demo in Python | Yes | |
| 12 | Polynomial Curve Fitting | Linear Regression Review. Polynomial function. Coefficient. Determination of w. Error function | Ability to use fit a polynomial curve | |
| 13 | Overfitting and Underfitting | Polynomial curve fitting review. Concept of model complexity. Order of Polynomial being too low or too high. Illustrate using graphs | Understand model complexity | |
| 14 | Practical 6 | Hands on on Overfitting and Underfitting in Python | Yes | |
| PART 4 – PROBABILITY THEORY | ||||
| 15 | Introduction to Probability Theory | Review of the concept of Probability. Simple examples. Random experiments and Random variables. Some common notations. Take home quiz | Be able to explain basic probability theory | Quiz |
| 16 | Rules of Probability and Bayes’ Theorem | Derivation of the rules of Probability. Marginal Probability. Joint Probability and Conditional Probability. (1) Sum Rule (2) Product Rule. Derivation of Bayes’ Theorem. Formulas | Derive Bayes’ theory from scratch | |
| 17 | Application of Bayes’ Theorem | Problem scenarios applying Bayes Theorem. Finding Marginal, Joint and Conditional probabilities. Concept of Posterior and Prior. Concept of Independence. The confusion matrix | Be able to apply Bayes’ theorem in real scenarios | |
| 18 | Understanding Probability Density | Continuous random variable versus discreet random variable. Probability Density and density functions. Probability distributions. | Be able to explain probability distributions | |
| PART 5 – SUPERVISED LEARNING (CLASSIFICATION) | ||||
| 19 | Practical 7 | Building a simple model for prediction using the Advertising Dataset | Yes | |
| 20 | Bias/Variance Trade-off | Clear explanation of Model Bias and Variance. The Trade-off. Analysing the Mean-Squared-Error function (MSE). Decomposition of the MSE. Model complexity vs flexibility. Analysis of the Bias/Variance trade-off graph. Review of Underfitting and Overfitting | Scholar should be able to analyse the Bias/Variance trade-off graph | |
| 21 | Quiz on Modules 1 to 20 | Quiz on Topics 1 to 20 | Quiz | |
| 22 | Introduction to Classification | What is classification? Review of Mean Squared Error in classification. Classification versus Regression. Error rate in classification. Typical classification problem. Cancer diagnosis example. Concept of Inference and Decision | Illustrate the and apply the concept of classification | |
| 23 | Practical 8 | Building a Machine Learning model for classification using the Iris Dataset. | Yes | |
| 24 | The Bayes’ Classifier and How it works | The Bayes’ Classifier and How it works | Clear understanding of Bayes’ classifier | |
| 25 | K-Nearest Neighbor Classifier | Overview of the K-Nearest Neighbors Classifier. How it works. Conpare and Contrast with the Bayes Classifier. Algorithm of the K-Nearest Neighbors classifier (KNN). Illustration/Animation | Gain knowledge of other classifiers (KNN) | |
| 26 | Classification Rate Minimization, ROC and AUC | Misclassification in Bayes’ classifier. How to minimize misclassification in Bayes’ classifier. Decision Boundaries. Review of Rules of Probability. Concept of False Positives and False Negatives. Receiver Operating Characteristics Curve (ROC) and Area Under the Curve (AUC) | Learn how to improve model performance. Understand ROC and AUC | |
| 27 | Practical 9 | Enterprise Data Analysis Attempt (Stock market data, Tweets etc) | Yes | |
| 28 | Introduction to Multiple Linear Regression | Examining the advertising dataset. Importing into Jupyter Notebook. Determination of How each predictor variable affects the response variable. Calculating Regression coefficients. How multiple linear regression works. Some Python practicals | Yes | |
| PART 6 – UNSUPERVISED LEARNING | ||||
| 29 | Introduction to Principal Components Analysis | Concept of Unlabeled data. Approaches to dimensionality reduction. Theoretical Overview of PCA. Review of Matrix operations (Multiplication). Determination of the number of components. Scree Plot. Basics of Singular Value Decomposition (SVD) | Be able to explain the concept of dimensionality reduction and perform PCA | |
| 30 | Practical 10 | Principal Components Analysis of the Wine Dataset | Yes | |
| 31 | Introduction to Factor Analysis | Approaches to Factor Analysis. Test for Sampling Adequacy. KMO Statistics. Scores and Loading. Interpreting Factor Analysis results | Gain good knowledge of Factor Analysis | |
| 32 | Practical 11 | Factor Analysis in Python/R | Yes | |
| 33 | Basics of Logistic Regression | Review of Classes of ML problems. Basics of Logistic Regression. Probability Distribution. The Logistic Function. Odd and Odds Ratio. Explanation. The log-odds | Be able to differentiate between step function and sigmoid function. | |
| 34 | Introduction to Artificial Neural Networks | Background of the Artificial Neural Networks. Review of Calculus(differentiation and partial derivatives). The Sigmoid Neuron. Concept of weights and biases. Network activation and activation function. Hidden layers. The Perceptron. Multilayer Perceptron. Network training. Backpropagation and Gradient decent. Network training algorithms. | Gain in-dept knowledge of Neural Networks. | |
| 35 | Practical 12 | Solving a Neural Network by hand | Yes | |
| 36 | Introduction to Clustering and Cluster Analysis | Review of KNN classifier. Types of Clustering: Hierarchical and agglomerative. K-Means clustering. K-nearest neighbor. The algorithms and the tradeoffs. | ||
| 37 | Practical 13 | Clustering demo in Python. Building a dendrogram | Yes | |
| 38 | Practical 14 | Non-linear modelling using the wage dataset | Yes | |
| 39 | Introduction to Tree-based Modelling | Background of decisions trees. Decision trees for classification and regression. Pruning. Assumptions. Splitting | Scholar should be able to build a decision tree for classification | |
| 40 | Review of all topics | |||
| 41 | Next Steps | |||
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You are doing a great job Sir, Please when is the next training in Data science/ML date?
Thanks for commenting.
We are looking at last from 4th week of November