Yes you can. Our course doesn’t require a prior knowledge of Python programming. We devote the first few sessions in giving an in depth and hands on understanding of Python. In fact most of our students don’t know Python before they join our course. This course is ideal for students who do not have any programming experience. There are very few courses that teach machine learning from scratch. We are proud to be one of the few training courses that teach machine learning from scratch.

Our course will take you from zero to machine learning hero in 42 days. It is designed in such a way that you don’t need to know any programming language before joining this course. We divide the training in two parts. In the first part we teach Python with detailed classroom sessions, hand on exercises, sharing our simple and easy to follow handouts, home assignments and lots of videos from our previous classes. Once you complete our instructor led classroom course you will become a pro in Python, machine learning and data science.

Performing machine learning using Python is the best choice. Python programming language is designed for data analysis. It is the language of choice for data scientists around the world.Machine learning using python should be done in two steps. In first step one should pay attention to learning relevant Python skills that power you with good data handling and manipulation skills.In the second step, one should pick up the scikit-learn library and start using its data sets and algorithms to practice machine learning. We recommend that you install Anaconda distribution as it will get you Python, scikit-learn and many other data science libraries on your computer in easy one step installation.Our instructor led online classroom course specializes in teaching students cutting edge machine learning algorithms using Python. For more questions you can email us at ml@mcal.in

Python programming language is cross platform. It’s an interpreted language and its community has built interpreters for all the popular operating systems. We have trained students in Machine learning with different operating systems like Windows, Mac, Linux, Ubuntu etc.If your machine has 4GB of RAM and a free space of 0.5 GB on your hard disk, you are good to go.One more thing we recommend is a Mic. You will use it to interact with the instructor during the live classroom sessions. However if you don’t have a mic, you can still attend the course and use the chat feature of our online classroom.

Yes.Before the training starts we send out a detailed step by step guide on what to do before your first class. Our handbook will give all the links, resources and guide necessary to install the relevant software on all the popular operating systems. It takes about 15 minutes to go through the instructions and make the necessary installations before the class starts.Sometimes students face issues installing and we help them get over their issue. In short you will not have any issue in getting setup before the training starts.

Python is one of the Top 3 programming languages of the world. The main reason for it to hold this spot is its simplicity and ease of use. If there is any programming language that somebody wants to pick for understanding software, Python is a good choice. It is simple, data friendly and last but not the least open source.In our machine learning course students join from all over the world who don’t have any programming knowledge. We help them become ready to code in Python in 2-3 weeks as part of our machine learning course.

There are many reasons why Python is ideal for machine learning. Of the many the most important ones are as follows:One of the top 5 programming languages of the world – You can do a google search for top programming languages and you will find Python in the top 5 list. Its a program that is immensely popular among data scientists. Its the default programming language for machine learning, data analysis and data munging. All the leading libraries of today provide Python extensions. For example Apache Spark can be accessed using Python.Simple and easy to learn – Python is a simple programming language. It is easy to setup and get started on it quickly. Its syntax is simple and easy to understand. It can be picked up quickly by any new programmer within hours.Open Source – It’s an open source programming language. It’s freely available and you don’t have to pay any licensing fees. Cross Platform – It is an interpreted language and it can be run on all major operating systems. We have students that use Mac, Windows, Linux, Ubuntu etc and it works exactly the same in all the platforms. There are many more reasons that make Python a great choice for machine learning but the ones above are the most important ones that come to our mind.

The most important machine learning scientific libraries are numPy, Pandas, MatplotLib & scikit-learn. numPy and Pandas makes data loading and data manipulation a breeze.MatplotLib gives intuitive out of the box charts that makes data visualization a treat to the eyes. Scikit-learn is packed with all the important supervised and unsupervised learning algorithms.In our course we cover these libraries in detail to get you ready for real world machine learning.

Important machine learning scientific libraries are numPy, Pandas, MatplotLib & scikit-learn. They help you build a pipeline for data ingestion, data cleanup, data manipulation, data visualization and finally machine learning and predictive analysis. We recommend you to install Anaconda distribution of Python. In this one install, all the data science libraries of Python gets installed in one shot. In our course we teach each of these libraries in great detail and teach special techniques for effectively using these libraries.

Data scientists use many different kinds of machine learning algorithms to discover patterns in big data that lead to actionable insights. At a high level, these different algorithms can be classified into two groups based on the way they “learn” about data to make predictions: supervised and unsupervised learning.Supervised machine learning is the more commonly used between the two. It includes such algorithms as linear and logistic regression, multi-class classification, and support vector machines. Supervised learning is so named because the data scientist acts as a guide to teach the algorithm what conclusions it should come up with. It’s similar to the way a child might learn arithmetic from a teacher. Supervised learning requires that the algorithm’s possible outputs are already known and that the data used to train the algorithm is already labeled with correct answers. For example, a classification algorithm will learn to identify animals after being trained on a dataset of images that are properly labeled with the species of the animal and some identifying characteristics.

Logistic regression was developed by statistician David Cox in 1958.The binary logistic model is used to estimate the probability of a binary response based on one or more predictor (or independent) variables (features). It allows one to say that the presence of a risk factor increases the odds of a given outcome by a specific factor.Logistic regression measures the relationship between the categorical dependent variable and one or more independent variables by estimating probabilities using a logistic function Logistic regression is applicable, for example, if: 1. we want to model the probabilities of a response variable as a function of some explanatory variables, e.g. “success” of admission as a function of gender. 2. we want to perform descriptive discriminate analyses such as describing the differences between individuals in separate groups as a function of explanatory variables, e.g. student admitted and rejected as a function of gender 3. we want to predict probabilities that individuals fall into two categories of the binary response as a function of some explanatory variables, e.g. what is the probability that a student is admitted given she is a female 4. we want to classify individuals into two categories based on explanatory variables, e.g. classify new students into “admitted” or “rejected” group depending on their gender.

A decision tree is a flowchart-like diagram that shows the various outcomes from a series of decisions. It can be used as a decision-making tool, for research analysis, or for planning strategy. A primary advantage for using a decision tree is that it is easy to follow and understand.Decision trees have three main parts: a root node, leaf nodes and branches. The root node is the starting point of the tree, and both root and leaf nodes contain questions or criteria to be answered. Branches are arrows connecting nodes, showing the flow from question to answer. Each node typically has two or more nodes extending from it. For example, if the question in the first node requires a “yes” or “no” answer, there will be one leaf node for a “yes” response, and another node for “no.”

Naïve Bayes is a probabilistic classifier based on applying Bayes’ Theorem with strong (naïve) independence assumptions between the features. Naïve Bayes was developed the 1960s and was primarily used for text categorization. It is still an apt method for judging documents as belonging to one category or another. Naïve Bayes is highly scalable and training Naïve Bayes model takes linear time.Ex: Classifying a document sentiment as either positive or negative

Cross-validation is a model valuation technique for assessing how the results of a statistical analysis generalize to an independent data set. It is mainly used in settings where the goal is prediction and one wants to estimate how well a predictive model works on independent data. Cross-validation is implemented by dividing dataset into n-equal parts and repeatedly testing the model accuracy on one part of the dataset. This process ensures an optimal model and avoids overfitting. Sample Description

An error in a machine learning algorithm prediction can result from a high bias or high variance. High bias is an error from wrong assumptions in the learning algorithm. High bias can cause the model to miss out on the relation between target variable and features. The variance is an error from sensitivity to small fluctuations in the training set. High variance causes overfitting in the model by tailoring the model specifically for seen data. Bias-Variance tradeoff is the problem of simultaneously minimizing these two sources of error that prevents prediction algorithms from generalizing beyond their training dataset.

Bootstrap aggregating, also called bagging, is a machine learning ensemble algorithm designed to improve the stability and accuracy of machine learning algorithms. Bagging was proposed by Leo Breiman in 1994 to improve classification by combining classification of randomly generated training sets. The process involves training classifiers on m-random sets of data taken from original dataset with replacement, and taking the output result as the average of the classifier results. Bagging significantly reduces variance in prediction and their by overfitting and is widely used in building inherently unstable decision trees.

Bootstrap aggregating, also called bagging, is a machine learning ensemble algorithm designed to improve the stability and accuracy of machine learning algorithms. Bagging was proposed by Leo Breiman in 1994 to improve classification by combining classification of randomly generated training sets. The process involves training classifiers on m-random sets of data taken from original dataset with replacement, and taking the output result as the average of the classifier results. Bagging significantly reduces variance in prediction and their by overfitting and is widely used in building inherently unstable decision trees.

Similar to bagging, random forest is an ensemble learning technique for classification, regression, and other tasks, by building a multitude of decision trees at training time and outputting the average of these trained classifiers (mode of the results for classification and average of the results for regression). Random forest differs from bagging by taking only a subset of the features each time whereas in bagging, we consider all the features.

Boosting is a machine learning ensemble algorithm developed on the principle that a set of weak-learners, when taken together, can create a strong learning algorithm. This process involves iteratively learning weak classifiers with respect to a distribution and adding them to a final strong classifier. This adding is weighted in accordance with weak learner’s accuracy.

Deep learning is part of a broader family of machine learning methods based on learning data representations as opposed to traditional task-specific algorithms. This involves in developing a cascade of multiple layers of nonlinear processing units for feature extraction and transformation (ex: neural networks). Layers that have been used in deep learning include hidden layers of an artificial neural network and sets of propositional formulas.

raditional programs take data as input and produces data as output. However a machine learning algorithm takes data as input but produces a program as an output. This machine generated program can now take new data, process it and produce output data. Machine learning algorithms automate the process of creating programs using historical data. In simple words, it gives computers the capability to extract knowledge from data and store it for future judgement. Wikipedia defines machine learning as… “Machine learning is the subfield of computer science that, according to Arthur Samuel, gives “computers the ability to learn without being explicitly programmed”.

Data scientists use many different kinds of machine learning algorithms to discover patterns in big data that lead to actionable insights. At a high level, these different algorithms can be classified into two groups based on the way they “learn” about data to make predictions: supervised and unsupervised learning. unsupervised machine learning is more closely aligned with what some call true artificial intelligence — the idea that a computer can learn to identify complex processes and patterns without a human to provide guidance along the way. Although unsupervised learning is prohibitively complex for some simpler enterprise use cases, it opens the doors to solving problems that humans normally would not tackle. Some examples of unsupervised machine learning algorithms include k-means clustering, principal and independent component analysis, and association rules.

k-means is one of the simplest unsupervised learning algorithms that solve the well known clustering problem. The procedure follows a simple and easy way to classify a given data set through a certain number of clusters (assume k clusters) fixed apriori. The main idea is to define k centers, one for each cluster. These centers should be placed in a cunning way because of different location causes different result. So, the better choice is to place them as much as possible far away from each other. The next step is to take each point belonging to a given data set and associate it to the nearest center. When no point is pending, the first step is completed and an early grouping is done. At this point we need to re-calculate k new centroids of the clusters resulting from the previous step. After we have these k new centroids, a new binding has to be done between the same data set points and the nearest new center. A loop has been generated. As a result of this loop we may notice that the k centers change their location step by step until no more changes are done or in other words centers do not move any more. Finally, this algorithm aims at minimizing an objective function know as squared error function.

The typical duration of the course is 5-6 Weeks. It’s the right amount of time that lets you absorb the theory and do the practice exercises. This structure is best suited to absorb the challenging concepts of machine learning using Python.

The course will have 3 types of hands on activities: 1. The instructor will do exercises in the classes live to show key concepts 2. During the live classroom the instructor will give out assignments to students and have them work on it. Students are asked to present their solution to other students during these exercise which further bolsters the understanding of the student 3. We give detailed and well researched home assignments that students work on during the week to revise the concepts taught in the class.

Yes this course is designed with working professionals in mind. It is conducted on weekends to make sure that it doesn’t interfere with the existing work schedule of working professionals. The live classroom sessions are conducted on Saturday and Sunday.

While we like all of our students to attend all the sessions, sometimes there are are some students that are not able to attend one or two class. We make sure we record all our sessions in high definition and share the videos in our state of the art learning management system for them to access it for lifetime. Together with session videos we will also share home assignments, handouts and other reference materials for you to keep and use for your life time.

All our classroom sessions are recorded in high definition. The session videos are processed in our state of the art lab and made available to students for them to keep for lifetime. Together with session videos we will also share home assignments, handouts and other reference materials for you to keep and use for your life time

When the instructor is giving the lecture to the class, the session will be recorded. After the class the recorded video will be uploaded to our state of the art e-learning portal and its access will be given to you for lifetime. Together with session videos we will also share home assignments, handouts and other reference materials for you to keep and use for your life time.

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After successfully finishing this course you will be able to: 1. Use Python and R effectively for developing machine learning algorithms 2. Use Python and R for working with data, including loading, cleaning and storing of data 3. Understand the theory and usage of various machine learning algorithms 4. Learn and apply the following machine learning algorithms. a. Supervised learning: Linear & polynomial regression, Logistic regression, Decision Trees (including Bagging, Random Forest, Boosting), Naïve Bayes & Support Vector Machine b. Unsupervised learning: K-Means clustering, Principal Component Analysis c. Deep Learning 5. Understand bias-variance trade off in all of the above machine learning algorithms there by making smart choices in using a machine learning algorithm. 6. Use cross validation to tune the accuracy of the model 7. Understand concepts of Natural Language Processing and perform sentiment analysis 8. Introduction to Deep Learning 9. Laid the foundation for learning Spark You can download our detailed curriculum fromhttps://mcalglobal.com/portfolio/python-machine-learning/

We are one of the only training companies that stay in touch with our alumni even after the course is over. We always encourage our students to reach out to us if they need any help. We constantly update our training course. We make sure to share those with our old students so that they are updated with the latest changes in the course. We have a blog where we put valuable material for our community to use and benefit from. You can access our blog at: https://www.mcal.in/blog/ We have created a Facebook group for our course and we give exclusive access to it for our students. Anyone can ask any question they have and the whole community helps out. You can request to join our Facebook group at: https://www.facebook.com/groups/mcalglobal

We do create separate batches for groups. Sometimes there are working professionals who would like to have the timings tweaked to fit their needs. There are companies we train who like to have a customized curriculum created to suit their needs. If you have any customized need, we urge you to contact us at ml@mcal.in so that we talk and look for ways to best serve you.