BEPEC'S ACHIEVEMENTS
Machine learning course been one of our flagship course across different chapters and different countries we are operating. Nearly 15-20 people get placed on machine learning every month.
50-80 participants work on machine learning projects. 100-150 people join for machine learning tutorial with BEPEC.
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.
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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.
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Our course contains Statistics, linear algebra, Probability, Python Programming, Machine learning and Data Visualization
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Each and every concept is carried with real time example as well as with an assignment to work
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A data scientist is very known for building prediction models using machine learning techniques like forecasting, regression and classification.
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Here in our training you gonna learn nearly 13+ machine learning algorithms like decision trees, ensembling methods, non-parametric methods e.t.c.
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Each and every algorithm was ended up with one real - time project and it's applications and creating documentation for each project.
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Nearly 15+ live real - time projects you people going to work in our trainings.
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After the training we prepare your resume and market it across different industries and test you with various mock interviews and projects until you crack job on Data Science.
AGENDA:
BASIC AND ADVANCED STATISTICS
- PURPOSE OF STATISTICS
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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. 2-SAMPLE-T TEST -
3. 2 - PROPORTION TEST -
4. CHI-SQUARE TEST
PYTHON AND R PROGRAMMING
- INTRODUCTION TO PYTHON
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1. PURPOSE OF PYTHON -
2. VALUES, VARIABLES, FUNCTIONS & LIBRARIES -
LIST, TUPLE, SET AND DICTIONARY -
USER DEFINED FUNCTIONS -
FOR LOOP, WHILE LOOP, CASE -
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
- WHAT IS SUPERVISED LEARNING ?
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WHAT IS UNSUPERVISED LEARNING ? -
WHAT IS SEMI - SUPERVISED LEARNING ? -
WHAT IS REINFORCEMENT LEARNING -
DIFFERENCE BETWEEN MACHINE LEARNING & DEEP LEARNING. -
DIFFERENCE BETWEEN A.I. & C.I -
REGRESSION vs CLASSIFICATION
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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
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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
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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)
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DEPLOYMENT OF MACHINE LEARNING MODELS -
BUILDING REINFORCEMENT LEARNING MODELS -
HIDDEN MARKOV CHAINS
SCHEDULE:
Certification
How do i earn my Machine learning - Best Practices Certification?
Pre-Requisites for Certified Machine learning Consultant - Best Practices
1. Must complete one project on Machine learning with Project documentation and Source Code
2. Need to attend 16 Hours of Best Practices on Machine learning Training from BEPEC Global
3. Need to book exam with BEPEC Global
4. Must gain 60% to get awarded with CMLA - Best Practices Certification
5. Answer sheet going to be corrected by Artificial Neural Networks
6. Aggregated by Independent Global Certification (IGC)
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FAQ'S:
What to master for Data Science?
Learning statistics, mathematics, programming, machine learning and data visualization pretty important to start with Data Science Career.
Which programming language is best Python or R?
There are bunch of programming languages like R, Python, Java, Matlab, SAS, Weka, C, C++ e.t.c. But due to huge Machine learning libraries building ML Models made easy with R and Python.
What is Machine learning? What is Data Mining?
What companies are expecting from Machine learning Engineers?
Solving unsolved problems using Machine learning is one of the major requirement and building various products and solutions across different domains using Machine Learning.
Podcasts
Machine Learning Definition:
Machine learning Course is a part of artificial intelligence (AI) that provides computers with the capacity to learn without being heavily programmed. Machine learning concentrates on the improvement of computer projects that can change when presented to new data. Machine learning is used in Facebook for tagging a particular person using deep face. Where there are about 13 machine learning algorithms for better understanding. Machine learning with python increases the speed of training and with the new advent of Graphical processing units(GPUs). Our special Machine Learning Course builds you and make you to work on Artificial Intelligence.
Our Hiring Partners
Our Services
20+ TIEDUP COMPANIES
MOCK INTERVIEWS
JOB SPECIFIC TRAINING
HIGH IN CLASS RESUME BUILDING
100% JOB SUPPORT
LIFE TIME E-LEARNING ACCESS
LIVE PROJECT EXPERIENCE
