Machine learning course been one of our flagship course across different chapters and different countries we are operating.
5080 participants work on machine learning projects. 100150 people join for machine learning.
We’re very much glad to share our success stories of our participants on machine learning. Please look into few success stories.
ABOUT OUR COURSE:
Machine learning course been one of the highly opted course across different industries and different levels of experience. Machine learning Definition: Machine learning is the branch of Artificial intelligence which helps the computers to make decisions without human help and with less amount of programming.
Machine learning Applications: Machine learning implemented across different industries like finance, healthcare, manufacturing, IT, Tech, Banking e.t.c. Some of the examples like self driving cars, face unlock, text suggestions, text to speech, speech to text, facebook tagging a friend, Iphone Siri e.t.c. are some of the examples of Machine learning
How to start? Anyone who is interested on Machine learning can start by reading some machine learning pdf or reading some machine learning introduction or one can download some free machine learning books or read various machine learning blogs. If above ways seem difficult to learn you can opt for Machine learning course. Once check machine learning job descriptions for more skillset on machine learning.

Our course data science with machine learning and NLP with python is crafted in such a way that even a layman can crack job on data science.

Our course contains Statistics, linear algebra, Probability, Python Programming, Machine learning and Data Visualization

A data scientist is very known for building prediction models using machine learning techniques like forecasting, regression and classification.

Here in our training you gonna learn nearly 13+ machine learning algorithms like decision trees, assembling methods, nonparametric methods e.t.c.

Each and every algorithm was ended up with one real – time project and it’s applications and creating documentation for each project.

Nearly 15+ live real – time projects you people going to work in our training.
BASIC AND ADVANCED STATISTICS

PURPOSE OF STATISTICS

DESCRIPTIVE STATISTICS

INFERENTIAL STATISTICS

WHAT IS DATA?

DIFFERENT TYPES OF DATA

PROBABILITY DISTRIBUTION

NORMAL DISTRIBUTION

SKEWNESS & KURTOSIS

HYPOTHESIS TESTING

NULL HYPOTHESIS TESTING, ALTERNATE HYPOTHESIS

STATISTICAL TESTING

1. ANOVA TEST

2. 2SAMPLET TEST

3. 2 – PROPORTION TEST

4. CHISQUARE TEST
PYTHON AND R PROGRAMMING

INTRODUCTION TO PYTHON

1. PURPOSE OF PYTHON

2. VALUES, VARIABLES, FUNCTIONS & LIBRARIES

LIST, TUPLE, SET AND DICTIONARY

USER DEFINED FUNCTIONS

DATA CLEANING WITH PYTHON USING PANDAS, SCIKIT LEARN, NUMPY

INTRODUCTION TO R

WORKING ON DATA VISUALIZATION WITH “R”

DATA CLEANING WITH R

PERFORMING STATISTICAL TESTING ON ‘R’ & PYTHON

DATA HANDLING ON HUGE SERVERS LIKE HADOOP, SQL E.T.C.

PROJECTS ON IMPORTING DATA FROM LARGE SERVERS & SHAPING THE

DATA FOR ANALYSIS
MACHINE LEARNING ALGORITHMS PART 1

DIFFERENCE BETWEEN MACHINE LEARNING & DEEP LEARNING.

DIFFERENCE BETWEEN A.I. & C.I

REGRESSION vs CLASSIFICATION

BUILD LINEAR REGRESSION MANUALLY

CODING LINEAR REGRESSION ON PYTHON

VALIDATION TECHNIQUES LIKE

R – SQUARED , MSE, RMSE, AIC, BIC E.T.C.

ASSUMPTIONS OF L.R

REAL TIME BUSINESS CASE EXPLANATION ON L.R

REAL TIME PROJECT – HEALTH CARE DOMAIN ON L.R

MATHS BEHIND LOGISTIC REGRESSION

BUILDING LOGISTIC REGRESSION MANUALLY

CODING LOGISTIC REGRESSION ON PYTHON

VALIDATION TECHNIQUES LIKE CONFUSION MATRIX

ROC, AIC E.T.C.

ASSUMPTIONS ON LOGISTIC REGRESSION

BUSINESS CASE EXPLANATION ON LOGISTIC

REGRESSION

BUSINESS CASE ON LOGISTIC REGRESSION FROM BANKING

K – MEANS CLUSTERING

H. CLUSTERING

MARKET SEGMENTATION USING K – MEANS CLUSTERING

CLUSTERING FOR DATA IMPUTATION

CLUSTERING IN CYBER SECURITY

H . CLUSTERING TO UNDERSTAND THE PURCHASE PATTERN

WORKING ON A REAL TIME PROJECT BASED ON ABOVE 3

ALGORITHMS
ADVANCED MACHINE LEARNING ALGORITHMS

DECISION TREE – INTERNAL MECHANISM

ENTROPY & GINI

HYPER PARAMETER TUNING

CONTROLLING THE OVER FITTING

BAGGING & BOOSTING FOR DECISION TREE AND MANUALLY BUILDING A

BASIC DECISION TREE FOR A SAMPLE DATA

HOW TO BUILD A POC ?

EXAMPLE POC BUILDING.

BUILD A POC ON DECISION TREE/RANDOM FOREST/ GRADIENT BOOSTING

FROM (AIRLINES INDUSTRY)

SUPPORT VECTOR MACHINES

MATHS BEHIND SUPPORT VECTOR MACHINES

SUPPORT VECTOR REGRESSION & SVC

HINGE LOSS

CHOOSING DECISION BOUNDARIES
ADVANCED NATURAL LANGUAGE PROCESSING

POS TAGGING

SEMANTIC ANALYSIS

ENTITY RECOGNITION

WORD CLOUD

LEXICAL ANALYSIS

SENTIMENTAL ANALYSIS

NAVIES BAYES FOR NLP MODEL BUILDING

BUILDING A PROJECTS ON NLP USING NLTK & SPACY

LIMITATIONS OF NAIVE BAYES OVER RNN & LSTM
PICK ONE ELECTIVE TOPIC

CONVOLUTION NEURAL NETWORK(IMAGE RECOGNITION)

DEPLOYMENT OF MACHINE LEARNING MODELS

BUILDING REINFORCEMENT LEARNING MODELS

HIDDEN MARKOV CHAINS
How do you choose a machine learning model?
 Collect data.
 Check for anomalies, missing data and clean the data.
 Perform statistical analysis and initial visualization.
 Build models.
 Check the accuracy.
 Present the results.
Which machine learning algorithm is best?
 Naïve Bayes Classifier Algorithm.
 K Means Clustering Algorithm.
 Support Vector Machine Algorithm.
 Apriori Algorithm.
 Linear Regression.
 Logistic Regression.
 Artificial Neural Networks.
 Random Forests.
What are the types of machine learning?
Examples of Supervised Learning: Regression, Decision Tree, Random Forest, KNN, Logistic Regression etc