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Machine Learning with Python Classroom Training Mumbai, India

UPCOMING TRAININGS SCHEDULE
Date Days Timings
OCT 12th Sat & Sun | 7 Weeks 08:00 AM - 11:00 AM IST
OCT 26th Sat & Sun | 7 Weeks 10:00 AM - 01:00 PM IST
NOV 11th Mon - Fri | 5 Weeks 07:00 AM - 09:00 AM IST
NOV 16th Sat & Sun | 7 Weeks 07:00 AM - 10:00 AM IST
NOV 30th Sat & Sun | 7 Weeks 09:00 AM - 12:00 PM IST

Overview of Course

The Machine Learning with Python course dives into the basics of Machine learning using a convenient and well-known programming language Python.

Machine learning, a vital topic in Artificial Intelligence has become a spark in today’s technology. This field may offer a greater opportunity, and starting a career in this domain is not as hard as it seems to be. The most essential part of your journey of success is purely your interest and enthusiasm to learn all those things

In your Machine Learning course journey, it’s required to select the specific coding language right from the initial stage, as your option will determine the bright future. Python is the best choice for aspirants to make your focus strong in order to jump into the spotlight field i.e Machine Learning.

If you are fresher, you don’t where to start learning and why do you need Machine learning and why it is gaining more popularity, then you at the right platform. NearnLearn is providing the Machine Learning with Python Classroom and Online training in Bangalore, for beginners with all the needed information and useful resources to make them expert within a short span of time.


What You Will Learn?

Statistical Learning

In this module, you will learn the basics of mathematics in the field of analysis and Machine learning like mean, median and mode. Without a minor knowledge of math, you can’t deal with Machine learning projects.

Topics :

  • Statistical Analysis concept
  • Descriptive Statistics
  • Linear Algebra: Scalars, Vectors, Matrices
  • Mathematical Analysis: Derivatives and Gradients
  • Gradient Descent: Building a simple Neural network from start
  • Introduction to Probability

Python for Machine Learning

Python is obtaining high popularity in different areas of software development and has gained a foremost position in Machine Learning domain because of simplicity, shorter development code, and Consistent syntax. Aspirants will learn about different variables, functions, sets and conditional statements. At the end of the module, you will learn how to imagine data using python libraries

Topics :

  • Python Overview
  • An extensive set of Libraries(Pandas, NumPy, Scikit-learn, Seaborn, and Matplotlib)
  • Numpy for Statistical Analysis

Why You Should Learn Machine Learning?

  • Machine learning offers you bright career opportunities

    • Machine learning career is blowing up, because the ML smart algorithms are used everywhere from email to mobile apps. And if you are in dilemma in choosing the most demanding and exiting domain, then gear up yourself with sparkling machine learning technology.

  • Machine learning Engineers can earn pretty high

    • Position yourself at the peak, as the cost of world-class machine learning professionals can be related best salaries.

  • ML jobs on the top rise

    • The job market for machine learning engineers is blistering, as top tech companies are in hire of people who can build a unique algorithm in ML and can grow their company. From the recent survey in Bengaluru IT Hub, the number of Machine learning jobs is steadily increased.

  • Machine learning is directly connected to data science

    • Machine learning technology assist you with two career opportunity, one is machine learning engineer job and other is data scientist job, as machine learning act as a shadow for data science. Data Scientist is elected as one of the most exciting job of 21th century.

Why Choose NearLearn?

  • Offers you both Classroom training and online training on weekends and weekdays, as per your convenience.
  • We are well- known for industrial relevant courses and interactive training.
  • We help students to build the excellent skills and empower them to come up as proficient experts who are prepared to chase the challenges in the machine learning field.
  • Well certified faculties who have years of experience assure you with best guidance to fulfil your dream goal
  • Live projects to make the students to understand practically.
  • We have tie up with top MNC’s, consultancies globally to provide future placement to our students.
  • 50 no’s of Data sets, 2 solid domain specific live project, Q&A with 100% placement assistance
  • One of the Best Machine Learning Institute In Bangalore

Our Pricing


Instructor Mumbai


Brief Info

A Tech Enthusiast with expertise in Artificial Intelligence, Machine Learning, Internet of Things, Deep Learning and grip on R and Python.

Deepak is a Mathematics and Computing graduate from IIT-BHU, Varanasi in 2010. He has an industry experience of 7+ years with specialization in web technologies, cloud infrastructure development and databases. He always is on a lookout for latest technologies and how their use can help us solve real world problems.

Recent advancements in field of Internet of Things and Machine Learning have caught his eye. He likes the field and is actively involved in learning, developing and letting others know about the immense potentials of it.


Educational Qualification

Graduation: Indian Institute of Technology, BHU, Varanasi


Professional Experience
  • Worked as Senior Software developer at Oracle India for 5 year on Web Development and Cloud Infrastructure Team.
  • Currently works as a freelance developer and trainer on IoT, Machine Learning & Artificial Intelligence since 4 years.
  • Trained Corporate on various technologies like IoT, Machine Learning, Python, Artificial Intelligence.

    • Classroom FAQ’s

    Machine Learning is a highly interdisciplinary subject. Hence, basic understanding of the following Concepts is highly useful:

    • Some background in programming (basic familiarity with variables, functions, loops, etc in any language) is helpful.
    • Linear algebra
    • Probability theory
    • Statistics & hypothesis testing
    • Optimization methods

    NearLearn has a dedicated job Assistance Team, who work with candidates on individual basis in assisting for right Machine Learning job.

    Payment can be made via Cheque / DD / Online Funds transfer / Cash Payment.

    Cheque should be drawn in favour of “Near And Learn Pvt. Ltd.” payable at Mumbai NEFT Payment:

    Account Name: Near And Learn Pvt. Ltd,

    Account Number: 201002051650

    IFSC Code: INDB0000008

    Branch: IndusInd Bank Limited Bangalore

    A/c Type: Current

    09.30am – 06.00pm Weekend(Saturday-Sunday)

    Quick Enquiry


    Courses Curriculum

    Lecture1.1 Data science & its importancePreview

    Lecture1.2 Key Elements of Data Science

    Lecture1.3 Data Warehousing

    Lecture1.4 Business Intelligence

    Lecture1.5 Data Visualization

    Lecture1.6 Data Mining

    Lecture1.7 Machine Learning

    Lecture1.8 Artificial Intelligence

    Lecture1.9 Cloud Computing

    Lecture1.10 Big Data

    Lecture1.11 Artificial Intelligence: A previewPreview

    Lecture1.12 What is Artificial Intelligence & its importance

    Lecture1.13 Artificial Intelligence vs Machine Learning

    Lecture2.1 What is Machine Learning (ML)?Preview

    Lecture2.2 How machines learn Preview

    Lecture2.3 Types of learning: Supervised, Semi-supervised, Unsupervised, Reinforcement

    Lecture2.4 Basics of Classification, Regression and Clustering algorithms

    Lecture2.5 Creating your first Prediction Model

    Lecture2.6 Training & Model evaluation

    Lecture2.7 Choosing Machine Learning algorithm

    Lecture3.1 A quick refresh on basic intermediate maths

    Lecture3.2 Linear Algebra (Vectors, Matrix, Eigen Values)

    Lecture3.3 Probability and Statistics

    Lecture3.4 Hypothesis testing

    Lecture3.5 Optimization

    Lecture4.1 A quick crash course on basics of PythonPreview

    Lecture4.2 Python Object and Data Structure

    Lecture4.3 Python Comparison Operators

    Lecture4.4 Python Statements

    Lecture4.5 Methods and Functions

    Lecture4.6 Milestone Project – 1

    Lecture4.7 Object-Oriented Programming

    Lecture4.8 Modules and Packages

    Lecture4.9 Errors and Exception Handling

    Lecture4.10 Milestone Project – 2

    Lecture4.11 Built-in Functions

    Lecture4.12 Python Decorators

    Lecture4.13 Python Generators

    Lecture4.14 Final Capstone Python Project

    Lecture4.15 Advanced Python Modules

    Lecture4.16 Advanced Python Objects and Data Structures

    Lecture4.17 Advanced OOP

    Lecture4.18 Parallel Processing

    Lecture4.19 Python Packages [Statsmodels, Sci-kit Learn, Scipy, Numpy, Pandas, Matplotlib, Seaborn, etc.]

    Lecture5.1 Data Collection & Preparation

    Lecture5.2 Data Mugging

    Lecture5.3 Outlier Analysis

    Lecture5.4 Missing value treatment

    Lecture5.5 Feature Engineering

    Lecture5.6 Data TransformationPreview

    Lecture5.7 Normalization vs Standardization

    Lecture5.8 Creating Dummies

    Lecture5.9 Dimensionality Reduction

    Lecture5.10 Principal Component AnalysisPreview

    Lecture6.1 Supervised Machine Learning algorithms

    Lecture6.2 Linear RegressionPreview

    Lecture6.3 Logistic RegressionPreview

    Lecture6.4 Decision/Classification

    Lecture6.5 Tree Ensemble Models

    Lecture6.6 Bagging

    Lecture6.7 Boosting

    Lecture6.8 Random Forest

    Lecture6.9 K-Nearest Neighbours (KNN)

    Lecture6.10 Naive Bayes

    Lecture6.11 Neural Network (Deep Learning)

    Lecture6.12 Support Vector Machine

    Lecture6.13 Unsupervised Machine Learning algorithms

    Lecture6.14 Clustering with K-means Clustering

    Lecture6.15 Bias-Variance Trade off

    Lecture6.16 Regularization

    Lecture6.17 Parameter tuning & grid search optimization

    Lecture7.1 Real life cases with Python

    Lecture8.1 Students would be given challenging real life cases to solve – just to augment their learning skills.

    Classroom Location

    (*tentative)