machine learning

Machine learning course been one of our flagship course across different chapters and different countries we are operating.

50-80 participants work on machine learning projects. 100-150 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, non-parametric 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. 2-SAMPLE-T TEST
  • 3. 2 – PROPORTION TEST
  • 4. CHI-SQUARE 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
INTRODUCTION TO LINEAR REGRESSION
1. SIMPLE LINEAR REGRESSION
2. MULTIPLE LINEAR REGRESSION
3. MATHS BEHIND L.R
4. OLS
5. BASED ON SLOPE AND INTERCEPT EQUATIONS
6. GRADIENT DESCENTS
  • 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?

  1. Collect data.
  2. Check for anomalies, missing data and clean the data.
  3. Perform statistical analysis and initial visualization.
  4. Build models.
  5. Check the accuracy.
  6. 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?

Broadly, there are 3 types of Machine Learning Algorithms..

 Examples of Supervised Learning: Regression, Decision Tree, Random Forest, KNN, Logistic Regression etc