Machine Learning Training With Python

  • This course will take you from zero to Python & Machine Learning hero in 45 days.
  • Learn Python from scratch and apply it to real Machine Learning problems.
  • Training Spread over 6 weekends to give you  all required time for exercises and theory.
  • Training delivered in a “Live Online” session by a very experienced trainer from the United States.
  • After every weekend get a comprehensive assignment to further solidify your learning.
  • Lifetime access to recorded training session videos, so learning stays with you.
  • In-depth learning and practicals of Supervised & Unsupervised Learning.
  • Once you finish this course you would have taken a giant leap towards the future of data analysis.

Live Online Training

For 6 Weekends

Extensive Practical Exercises

Every Week​​​​

Access To Recorded Training

Sessions Life Time


Want To Know More?














Hurry up to avail special Thanks Giving & Year End Offer

DEC

Sat - Sun ( 6 Weeks )

08:00 AM 11:00 AM

( IST )

 9,505 Off

 29,500

 19,995

( INCLUDING TAXES )

Hurry up to avail special Thanks Giving & Year End Offer

2 Dec Sat - Sun ( 6 Weeks )

08:00 AM 11:00 AM( IST )

 9,505 Off        29,500        19,995

( INCLUDING TAXES )

TRAINING FEATURES


Instructor-led Live Sessions

Instructor led live online classroom training delivered through best in the class technology available. Total duration of the training is 30 hours i.e. 12 sessions of 2.5 hours each for 6 weekends.

Real-life Case Studies

3 projects using real life data during the training to cement the knowledge and to provide intensive hands-on.

Assignments

Assignments post every weekend to further practice and to get more hands-on practicals.


Lifetime Access

Life time access to recorded training sessions, training presentations, exercises and quizzes  for future reference.


24 x 7 Expert Support

Life time access to our our experts to resolve your technical queries over email and phone.

Certification

Earn MCAL Global certification for "Python Machine Learning Training" based on your performance during project work and training.

WHO CAN ATTEND?


Developers

Developers of Java, .Net, Javascript, Mainframes, Hadoop, Scala, Swift or any other programming language can learn Python from Scratch and also master Machine Learning with practical hands-on exercises to increase job prospects many fold.

Business/Data Analysts

Business and System Analyst professionals can take advantage of this course by acquiring hands-on knowledge of Python as well as Machine Learning to successfully take up Data Science and Machine Learning projects.

 Testers

Testing Jobs are decreasing every passing day. This course provides a great chance to transform your testing career by acquiring sought-after skill set .i.e. Python and Machine Learning. 

Project Leads/Managers

Professional leading IT teams as Project Lead and Project Managers will benefit by investing in new technologies like Python and Machine Learning to enhance their technical skill set and get a chance to lead and work with Python & machine Learning teams.

BI & Data Visualisation Prof.

This Course is extremely beneficial for BI and Data Visualisation professionals who are working on tools like Tableau Qlikview, FusionCharts, Highcharts, Datawrapper, Plotly, Sisense, Dundas, IntelliFront BI, Domo, Style Intelligence, Looker.

Students

Fresh BE/B. TECH/MCA/BCA/ B Sc. IT graduates will gain competitive edge by learning Python and Machine Learning at the very start of their career. It will give them immense benefits over other fresh graduates in today’s competitive Job Market.

CLIENT TESTIMONIALS


Training conducted by MCAL Global for "Business Analysis", "Big Data Analytics & Hadoop and Analytics for Business Using R" have received excellent feedback from my team and hopefully it will help them in delivering more challenging projects in feature. Based on my interaction with my team, I recommended MCAL as a dependable training partner and if required hope to leverage your training in the future.

Lokesh Jha

Chief Technical Officer (CTO), Capgemini (India)

The training was extremely informative & valuable. The handsone & practical approach helped in the overall Understanding of the subject .Attending the training as a good start to getting deeper into subject as concepts are clear.

Amita Iyer

Senior Project Manager, Fiserv Global

Very well structured with good hands on experience. Knowledge of trainer is excellent.

Saibal Gupta

Consultant, Fractal Analytics (CPG)

COURSE CURRICULUM


Module 1 : Python

WEEK : 1

Module 1: Introduction
  • What is their expectation from course?
  • Module 1.1 Python Basics
    • Python history
    • Development environment setup
    • First python program
    • Keywords, Statements, Data types, Operators
    • Exercise 1
    Module 1.2 Python Flow Control
  • If else, For loop, While loop
  • Exercise 2
  • WEEK : 2

    Module 1.3 Python Functions
  • Functions, Argument, Modules, Packages
  • Exercise 3
  • Module 1.4 Native Data Types
    • Numbers, List, Tuples, Strings, Sets, Dict.
    • Exercise 4

    WEEK : 3

    Module 1.5 File Handling
  • File operations, File Directory, Exceptions
  • Exercise 5
  • Module 1.6 Scientific Python Packeges
  • Numpy, Pandas, Matplotlib,  sciklit-learn
  • Exercise 6
  • Module 2 : Machine Learning

    WEEK : 4

    Module 2.1: Introduction
    • What is Machine Learning
    • Common use of machine learning (spam, house price,stock price-6 examples)
    • Different machine learning techniques

    Module 2.2 Supervised Learning
  • What is supervised learning?
  • Regression vs Classification

  • Module 2.2.1 Linear Regression
  • What is linear regression?
  • Case Study
  • Model Tuning
  • Predictions
  • WEEK : 5

    Module 2.2.2  Regularization
    • What is Regularization?
    • LASSO Regression
    • Ridge Regression
    Module 2.2.3 Logistic Regression
  • What is logistic regression?
  • Case study
  • Model Tuning
  • Predictions
  • Module 2.2.4 Decision Trees & K-Nearest Neighbors 
  • What is Decision Trees?
  • Case study
  • Model Tuning
  • Predictions
  • WEEK : 6

    Module 2.3 Unsupervised Learning
  • What is Unsupervised Learning?
  • Different Unsupervised Algorithms?
  • K-Means Clustering
  • Principal Component Analysis
  • Linear Discriminant Analysis
  • Module 2.3.1 K-means Clustering
  • What is K-means Clustering 
  • Case Study
  • Model Tuning
  • Predictions
  • Conclusion
  • Summary 
  • Next Steps
  • DOWNLOAD OUR BROCHURE


    PEOPLE WE HAVE TRAINED FROM


    MCAL-Global-Clients_1
    MCAL-Global-Clients_2
    MCAL-Global-Clients_3
    MCAL-Global-Clients_4

    Key numbers describing our journey in last 7 years


    10000+

    Professionals Trained

     Globally

    50+

    International & Domestic

    Clients

    95%

    Excellent & Good

    Feedback's

    150+

    Years Of Collective

    Training & Mentoring Exp

    100%

    In-house Domain & Technology Expertise

    FREQUENTLY ASKED QUESTIONS



    1. What is Machine Learning?

    Traditional 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”.


    2. I am not a programmer, can I enroll in this course?

    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.


    3. I do not know Python, will you teach Python too in the program?

    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.


    4. How to learn Machine Learning using Python?

    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


    5. What machine do I require to set up the Python on my Laptop/Desktop?

    Python programming language is cross platform. Its 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.


    6. Will I get assistance to set up and Install Python before training starts?

    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.


    7. How easy is it to learn Python to implement Machine Learning?

    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.


    8. Why is Python most suitable programming language for Machine Learning?

    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 - Its an open source programming language. Its 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.


    9. What are the Machine Learning scientific libraries in Python?

    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.


    10. I do not know Python, will you teach Python too in the program?

    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.


    11. What is Supervised Learning?

    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.


    12. What is Logistic Regression?

    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.


    13. What are Decision Trees?

    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."


    14. What is unsupervised Learning?

    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.


    15. What is K-means Clustering?

    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.


    16. Who will train me this course from the United States?

    Our instructors are professionals with more than 15 years of experience and are currently working on projects for fortune 500 companies.
    Our instructors not only bring the knowledge of machine learning to the course but with specially crafted assignments bring the latest and greatest in machine learning being done in the biggest companies in US.
    Our course will not only make you machine learning champ but will make you ready to work on real life project.


    17. What is the duration of the program?

    The typical duration of the course is 5-6 Weeks. Its 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.


    18. What kind of exercise and hands-on will be there in the course?

    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.


    19. I am a working professional. is this course taught on weekends?

    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.


    20. What if I miss a session due to some engagement?

    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.


    21. Do I get recordings of the sessions for future reference?

    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.


    22. For what period I will able to access the recorded sessions?

    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.


    23. Do I get help in resume preparation after attending this course?

    We have an award winning and popular infographic resume builder. Its very easy to use and its premium features are available to our students free of charge.
    Using our resume builder you can create stunning resumes. You can view some samples using the following link: https://mcalglobal.com/socialresume/user/signup.htm


    24. What will I be able to do in Machine Learning on my own after completion of this Course?

    After successfully finishing this course you will be able to:
    1.   Use Python as a Programming language
    2.   Use Python to perform machine learning
    3.   Understand the overview of Machine learning
    4.   Understand Supervised and Unsupervised Machine learning
    5.   Understand Regression, Classification & Clustering
    6.   Use Python to process data and build machine learning models
    7.   Use cross validation to tune the accuracy of the model
    8.   Laid the foundation for learning Spark
    You can download our detailed curriculum from https://mcalglobal.com


    25. When required, will I be guided after the completion of the course?

    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://mcalglobal.com/mcalglobal-blogpost/ We have created a Facebook group for our course and we give exclusive access to it for our students. Any one 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


    26. We are a group of people, can you set up a separate batch for us?

    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.


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