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Machine Learning Course - Python Machine Learning

A power packed Machine Learning Certification for job relevant skills from India’s top institute!

Rated among best Machine Learning courses in India, is ideal program for aspirants who want to pursue hands-on Machine Learning training. This machine learning online course can be attended from anywhere across the globe through fully interactive live online sessions and perfectly suits the bill if you are looking for workshop or classroom based machine learning training in Bangalore, Delhi (Gurgaon and Noida).

This program will help you to acquire practical and hands-on skills of Machine Learning and covers most widely used techniques. You will begin with introduction to ML, learn both supervised and unsupervised methods, including text mining and natural language programming. Key technique covered are linear & logistics regression, decision trees, ensemble learning, support vector machines, k-nearest neighbour, naïve Bayesian, neural network models, clustering and principal component analysis etc. (please refer to later sections for detailed curriculum). 

Being India’s top institute we take very meticulous approach to ensure that curriculum is abreast with latest industry requirements. This is one of first machine learning courses in India when first version was introduced in 2014 with R and since then this program has undergone several revisions. In current curriculum we use Python for Machine Learning, given that in recent years Python has emerged as a preferred industry choice when it comes to Big Data, AI ML and Artificial Intelligence. (If you are interested to learn Data Science and Machine Learning with R, please check out this course!)

If you are looking to learn Machine Learning but have no prior knowledge of Analytics and Python should consider Data Science using Python which also includes Machine learning using Python.

Considering the cost effectiveness (compared with machine learning course fees of other reputed institutes) and real-life application based curriculum, this is the best machine learning online course to learn from your own place or in classroom bootcamp mode. 

Machine Learning Specialization Course duration: 120 hours (At least 54 hours live training + Practice and Self-study, with ~10hrs of weekly self-study).

Delivery Formats:

1. Classroom machine learning training in Noida, Gurgaon & Bangalore

2. Fully interactive live online training (Global access)

3. Self-paced e-learning modules (Global access)

Useful Blogs:

1. Learn Machine Learning: Where to Start?

2. An ultimate guide to starting a career in Machine Learning

3. 5 Factors To Consider Before Investing In A Machine Learning Course

Who Should do this course?

Candidates with prior knowledge of Python and data analytics (using libraries like numPy and Pandas), who are looking for Machine Learning certification to up-skill with practical application of Machine Learning with Python.


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Course Duration 54 hours
Classes 18
Tools R, R-Studio
Learning Mode Live/Video Based

What will you get

Access to 24 hours of video classes along with doubt-clearing support

Refernce codes and study material in PDF format

Module wise case studies/ projects

Career guidance and career support post the completion of some selected assignments and case studies

Course Outline

  • Introduction to Predictive Modelling
  • Types of Business problems - Mapping of Techniques
  • Relevance in industry and need of the hour
  • Difference between jargons i.e. data science, data analysis, data analytics, data mining
  • What is Machine Learning?
  • What is the goal of Machine Learning?
  • Applications of ML (Marketing, Risk, Operations, etc.)
  • Key components of ML
  • Overall process of executing the ML project (Data Pre-processing, Sampling, Model Building, Validation)
  • Common mistakes done in ML project and how to overcome
  • Different terms to know for ML

  • Basic Statistics - Measures of Central Tendencies and Variance
  • What is probability distribution?
  • Important distributions (discrete & continuous distributions)
  • Deep dive of normal distributions and properties
  • Concept of sampling & types of sampling
  • Concept of standard error and central limit theorem
  • Inferential Statistics - Concept of Hypothesis Testing
  • Statistical Methods - Z/t-tests (One sample, independent, paired), ANOVA, Correlation and Chi- square
  • Important modules for statistical methods: NumPy, SciPy, Pandas

  • Major Classes of Learning Algorithms -Supervised vs. Unsupervised Learning vs. Semi supervised vs. Reinforcement Learning
  • Important Consideration like fitment of techniques
  • Concept of Over fitting and Under fitting (Bias-Variance Trade off) & Performance Metrics
  • Concept of optimization - Gradient descent algorithm
  • Concept of feature engineering
  • Regularization (LASSO, LARS, Elastic net and Ridge regression)
  • Types of Cross validation(Train & Test, K-Fold validation etc.)
  • Cost & optimization functions

  • Introduction - Applications
  • Assumptions of Linear Regression
  • Building Linear Regression Model
  • Important steps in Model building
  • Need of Data preparation
  • Data Audit Report and Its importance
  • Consolidation/Aggregation - Outlier treatment - Flat Liners - Missing values- Dummy creation - Variable Reduction
  • Variable Reduction Techniques - Factor & PCA Analysis
  • Understanding standard metrics (Variable significance, R-square/Adjusted R-square, Global hypothesis ,etc.)
  • Validation of Models (Re running Vs. Scoring)
  • Standard Business Outputs (Decile Analysis, Error distribution (histogram), Model equation, drivers etc.)
  • Interpretation of Results - Business Validation -
  • Implementation on new data

  • Introduction - Applications
  • Linear Regression Vs. Logistic Regression Vs. Generalized Linear Models
  • Building Logistic Regression Model
  • Important steps in model building
  • Understanding standard model metrics (Concordance, Variable significance, Gini, KS, Misclassification, etc.)
  • Validation of Logistic Regression Models (Re running Vs. Scoring)
  • Standard Business Outputs (Decile Analysis, ROC Curve, Probability Cut-offs, Lift charts, Model equation, Drivers, etc.)
  • Interpretation of Results - Business Validation
  • Implementation on new data and Tracking the model

  • Overview of Decision Trees
  • Types of decision trees (Regression Trees, Classification trees, Oblique Decision Trees)
  • Types of decision tree algorithms (CART vs. CHAID vs. C50 etc.)
  • Concept of objective segmentation
  • How to use decision trees to solve regression, classification & segmentation problems)
  • Rule Based Knowledge: Logic of Rules, Evaluating Rules, Rule Induction and Association Rules
  • Construction of Decision Trees through Simplified Examples; Choosing the "Best" attribute at each Non-Leaf node
  • Splitting criteria: Entropy; Information Gain, Gini Index, Chi Square; ANOVA)
  • Generalizing Decision Trees; Information Content and Gain Ratio; Dealing with Numerical Variables; other Measures of Randomness
  • Pruning decision tree
  • Cost as a consideration
  • Fine Tuning model using tuning parameters
  • Model validation
  • Overfitting - Best Practices to avoid
  • Implementation of Solution

  • What is concept of Ensemble learning (Stacking, Mixture of Experts)?
  • Types of ensemble models (homogeneous, heterogeneous)
  • Logic, Practical Applications
  • Ensemble learning techniques
  • Bagging
  • Random Forest
  • Boosting
  • AdaBoost
  • Gradient Boost
  • XGBoost
  • Fine tuning the model using tuning parameters

  • What is concept of Instance based learning?
  • What is KNN?
  • KNN method for regression & classification
  • KNN method for missing imputation
  • Computation of Distance Matrix
  • The Optimum K value
  • Model Building, validation & Evaluation of Model
  • Advantages & Disadvantages of KNN Models
  • Applications of KNN in collaborative filtering, digit recognition
  • KNN in collaborative filtering, digit recognition

  • Fundamentals of Probability; Conditional and Marginal Probability; Bayes Theorem and Its Applications
  • Probabilities - The Prior and Posterior Probabilities
  • Bayesian Belief nets, MAP, Naïve Rule and Naïve Bayes
  • Naïve Bayes for classification - Data Processing - Discretization of Features
  • Applications of Naïve Bayes in Text Mining, Spam Engines and Classifications
  • Model Building, Validation and Evaluation of model
  • Pros/Cons of Naïve Bayes Models

  • What is Support vector machines?
  • Understanding SVM
  • Concepts of Linearly separable vs. non separable data
  • Mathematical Intuition (Kernel Methods Revisited, Quadratic Optimization and Soft Constraints)
  • Train/Test/Tune the Model using SVM
  • Applications and Interpretation

  • What is forecasting?
  • Applications of forecasting
  • Time Series Components (Trend, Seasonality, Cyclicity and Level) and Decomposition
  • Types of Seasonality
  • Pattern based vs Pattern less
  • Important terminology: lag, lead, Stationary, stationary tests, auto correlation & white noise, ACF & PACF plots, auto regression, differencing
  • Moving Averages
  • Weighted moving averages
  • Exponential Smoothening
  • Double exponential smoothening (Holt’s Method)

  • Box Jenkins Methodology
  • AR, MA, ARMA Models

  • Understanding Forecasting Accuracy
  • Goodness Metrics: MSE, MAPE, RMSE, MAD

  • Introduction to Segmentation
  • Types of Segmentation (Subjective Vs. Objective, Heuristic Vs. Statistical)
  • Heuristic Segmentation Techniques (Value Based, RFM
  • Segmentation and Life Stage Segmentation)
  • Concept of Distance and related math background
  • Segmentation Techniques
  • K-Means/K-Medians Clustering
  • Density Based clustering (DBSCAN)
  • Identifying number of segments (Pseudo F-value, Silhouette score, elbow method etc.)
  • Cluster evaluation and profiling
  • Identifying the characteristics of segmentation
  • Interpretation of results - Implementation on new data
  • Overview of other unsupervised learning techniques (Factor analysis, Hidden Markov models, Gaussian mixture models etc.)

  • Unstructured vs. Semi-structured Data
  • Text Mining - characteristics, trends
  • Domain presentation - discussion of various areas and their applications
  • Programming languages designated for working on Text Mining analysis
  • Data Scientist - a profession comprising mainly of working with Text Mining
  • Social Media – Characteristics of Social Media
  • Applications of Social Media Analytics
  • Examples & Actionable Insights using Social Media Analytics

  • Text processing using string functions & methods  
  • Understanding regular expressions
  • Identifying patterns in the text using regular expressions

  • Getting Started with NLTK
  • Introduction to NLP & NLTK
  • Introduction to NLTK Modules (corpus, tokenize, Stem, collocations, tag, classify, cluster, tbl, chunk, Parse, ccg, sem, inference, metrics, app, chat, toolbox etc)

  • Fundamentals of information retrieval
  • Reading data from file folder/from text file, from the Internet & Web scrapping, Data Parsing
  • Cleaning and normalization of data
    Sentence Tokenize and Word Tokenize, Removing insignificant words,(“stop words”), Removing special symbols, removing bullet points and digits, changing letters to lowercase, stemming /lemmatisation /chunking
  • Creating Term-Document matrix
  • Finding associations
  • Removing rare terms (Sparse terms)
  • Measurement of similarity between documents and terms
  • Visualization of term significance in the form of word clouds
  • Tagging text with parts of speech
  • Word Sense Disambiguation

  • Sentiment analysis
  • Vocabulary approach, based on Bayesian probability methods
  • Name entity recognition (NER)
  • Methods of data visualization
  • Word length counts plot
  • Word frequency plots
  • Word clouds
  • Correlation plots
  • Letter frequency plot
  • Heat map
  • Grouping texts using different methods

  • Data-centric methods
  • K-means
  • Classification Models (spam detection, topic modelling)
  • K Nearest Neighbours
  • SVM (Linear Support Vector Classifier)
  • Naive Bayes
  • Decision tree
  • Semantic similarity between texts
  • Language Models and n-grams -- Statistical Models of Unseen Data (Smoothing)

  • Sentiment Analysis (Classification, weighted score etc)
  • Word cloud analysis (Examples)
  • Segmentation using K-Means/Hierarchical Clustering (Grouping the similar words)
  • Classification (Spam/Not spam)
  • Topic Modeling (LDA, LSA, Louvain etc)
  • Text Summarization


1 year post your course commencement. If needed, you can also repeat any number of classes you want in the next one year after course completion. Batch change policies will however, apply in this case.

In case required because any genuine reasons, the recordings access can be extended further for upto 1 year post the completion of one year validity. Please note that given the constant changes in the Analytics industry, our courses continue to be upgraded and hence old courses might no longer hold relevance. Hence, we do not promise lifetime access just for marketing purposes. 

No. Our recordings can be accessed through your account on LMS or stream them live online at any point of time though.

Recordings are integral part of AnalytixLabs intellectual property by Suo Jure. The downloading/distribution of these recordings in anyway is strictly prohibited and illegal as they are protected under copyright act. Incase a student is found doing the same, it will lead to an immediate and permanent suspension in the services, access to all the learning resources will be blocked, course fee will be forfeited and the institute will have all the rights to take strict legal action against the individual.

The sharing of LMS login credentials is unauthorized, and as a security measure, if the LMS is accessed by multiple places, it will flag in the system and your access to LMS can be terminated.

Yes. All our course are certified. As part of the course, students get weekly assignments and module-wise case studies. Once all your submissions are received and evaluated, the certificate shall be awarded.

We follow a comprehensive and a self-sustaining system to help our students with placements. This is a win-win situation for our candidates and corporate clients. As a pre-requisite for learning validation, candidates are required to submit the case studies and project work provided as a part of the course (flexible deadline). Support from our side is continuous and encompasses help in profile building, CV referrals (as and when applicable) through our ex-students, HR consultants and companies directly reaching out to us.

We will provide guidance to you in terms of what are the right profiles for you based on your education and experience, interview preparation and conducting mock interviews, if required. The placement process for us doesn’t end at a definite time post your course completion, but is a long relationship that we will like to build.

No institute can guarantee placements, unless they are doing so as a marketing gimmick! It is on a best effort basis.

In professional environment, it is not feasible for any institute to do so, except for a marketing gimmick. For us, it is on a best effort basis but not time – bound – in some cases students reach out to us even after 3 years for career support.

We provide both the options and for instructor led live online classes we use the gold standard platform used by the top universities across the globe. These video sessions are fully interactive and students can chat or even ask their questions verbally over the VoIP in real time to get their doubts cleared.

Through the LMS, students can always connect with the trainer or even schedule one-to-one time over the phone or online. During the course we also schedule periodic doubts-clearing classes though students can also ask doubts of a class in the subsequent class.

LMS also has a discussion forum where a lot of your doubts might get easily answered.

Incase you are having a problem still, repeat the class and schedule one-to-one time with the trainer.

For all the courses, we also provide the recordings of each class for their self-reference as well as revision in case you miss any concept in the class. In case you still have doubts after revising through the recordings, you can also take one-to-one time with the faculty outside classes during.

  • Video-based - 2 days

Not for this course. The instalment options are available only for our courses which are atleast 3 months long or live classes.

There is no particular system requirement for this course since R can be easily installed on almost every laptop with basic configuration available these days. However, if possible, it is recommended to have 64-bit operating system.

I learned Data Science, various analytic techniques, and their application to business from “Analytix labs”. Institute pays an individual attention to every student and provide much needed guidance to shape the career in analytics. Faculties paced the course well, and kept it extremely engaging and practical. They provide case studies and small projects which gave me a real-life flavor to my learning. Moreover their live sessions were quite interactive, the pre-recorded sessions offered flexibility for working professionals.

Anshul Joon
(Independent Model Review (Model Risk Management) at HSBC)

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