This course is designed to cover maximum Concept of Machine Learning. Anyone can opt for this course. No prior understanding of Machine Learning is required. NOTE: Course is still under Development. You will see new topics will get added regularly. Now question is why this course?

Download 100+ Free Data Science, Machine Learning, and Artificial Intelligence Books from here. Books are 1. Artificial Intelligence A Modern Approach, 1st Edition 2. Natural Language Processing with Python 3. Bayesian Reasoning and Machine Learning.. 100 free data science books | best free books for data science | 10 free machine learning books | best free books for ml books | best free ai books

Let me paint a picture for you, you are a beginner to the field of Data Science and have started making your first ML model for predictions and found the accuracy using model.score() You are jumping around thinking that you nailed it and maybe it was your destiny to become a Data Scientist. Well, I don't want to burst the bubble but you can be horribly wrong. Do you know why? -- Because accuracy is a very poor metric to measure the classifier performance especially in the case of Unbalanced Dataset. And unbalanced datasets are prevalent in a multitude of fields and sectors. From fraudulent transactions, identifying rare diseases, electrical pilferage to classifying search-relevant items in an e-commerce site, data scientists come across them in many contexts.

This one picture shows what areas of calculus and linear algebra are most useful for data scientists. If you read any article worth its salt on the topic Math Needed for Data Science, you'll see calculus mentioned. Calculus (and it's closely related counterpart, linear algebra) has some very narrow (but very useful) applications to data science. If you have a decent algebra background (which I'm assuming you do, if you're a data scientist!) You don't usually need to know exactly how to take derivatives, minimize sums of squares or create clustering algorithms from scratch--there are calculators for that!

Let me paint a picture for you, you are a beginner to the field of Data Science and have started making your first ML model for predictions and found the accuracy using model.score() You are jumping around thinking that you nailed it and maybe it was your destiny to become a Data Scientist. Well, I don't want to burst the bubble but you can be horribly wrong. Do you know why? -- Because accuracy is a very poor metric to measure the classifier performance especially in the case of Unbalanced Dataset. And unbalanced datasets are prevalent in a multitude of fields and sectors.

End-users' trust in automated agents is important as automated decision-making and planning is increasingly used in many aspects of people's lives. In real-world applications of planning, multiple optimization objectives are often involved. Thus, planning agents' decisions can involve complex tradeoffs among competing objectives. It can be difficult for the end-users to understand why an agent decides on a particular planning solution on the basis of its objective values. As a result, the users may not know whether the agent is making the right decisions, and may lack trust in it. In this work, we contribute an approach, based on contrastive explanation, that enables a multi-objective MDP planning agent to explain its decisions in a way that communicates its tradeoff rationale in terms of the domain-level concepts. We conduct a human subjects experiment to evaluate the effectiveness of our explanation approach in a mobile robot navigation domain. The results show that our approach significantly improves the users' understanding, and confidence in their understanding, of the tradeoff rationale of the planning agent.

Finding the best model that describes a high dimensional dataset, is a daunting task. For binary data, we show that this becomes feasible, if the search is restricted to simple models. These models -- that we call Minimally Complex Models (MCMs) -- are simple because they are composed of independent components of minimal complexity, in terms of description length. Simple models are easy to infer and to sample from. In addition, model selection within the MCMs' class is invariant with respect to changes in the representation of the data. They portray the structure of dependencies among variables in a simple way. They provide robust predictions on dependencies and symmetries, as illustrated in several examples. MCMs may contain interactions between variables of any order. So, for example, our approach reveals whether a dataset is appropriately described by a pairwise interaction model.

Transfer learning considers a learning process where a new task is solved by transferring relevant knowledge from known solutions to related tasks. While this has been studied experimentally, there lacks a foundational description of the transfer learning problem that exposes what related tasks are, and how they can be exploited. In this work, we present a definition for relatedness between tasks and identify foliations as a mathematical framework to represent such relationships.

For supervised classification problems involving design, control, other practical purposes, users are not only interested in finding a highly accurate classifier, but they also demand that the obtained classifier be easily interpretable. While the definition of interpretability of a classifier can vary from case to case, here, by a humanly interpretable classifier we restrict it to be expressed in simplistic mathematical terms. As a novel approach, we represent a classifier as an assembly of simple mathematical rules using a non-linear decision tree (NLDT). Each conditional (non-terminal) node of the tree represents a non-linear mathematical rule (split-rule) involving features in order to partition the dataset in the given conditional node into two non-overlapping subsets. This partitioning is intended to minimize the impurity of the resulting child nodes. By restricting the structure of split-rule at each conditional node and depth of the decision tree, the interpretability of the classifier is assured. The non-linear split-rule at a given conditional node is obtained using an evolutionary bilevel optimization algorithm, in which while the upper-level focuses on arriving at an interpretable structure of the split-rule, the lower-level achieves the most appropriate weights (coefficients) of individual constituents of the rule to minimize the net impurity of two resulting child nodes. The performance of the proposed algorithm is demonstrated on a number of controlled test problems, existing benchmark problems, and industrial problems. Results on two to 500-feature problems are encouraging and open up further scopes of applying the proposed approach to more challenging and complex classification tasks.

Understanding consumer behavior is an important task, not only for developing marketing strategies but also for the management of economic policies. Detecting consumption patterns, however, is a high-dimensional problem in which various factors that would affect consumers' behavior need to be considered, such as consumers' demographics, circadian rhythm, seasonal cycles, etc. Here, we develop a method to extract multi-timescale expenditure patterns of consumers from a large dataset of scanned receipts. We use a non-negative tensor factorization (NTF) to detect intra- and inter-week consumption patterns at one time. The proposed method allows us to characterize consumers based on their consumption patterns that are correlated over different timescales.