Yellow taxi Demand prediction Newyork city

₹15,000.00
  • Python for Data Science Introduction 0/0

  • Python for Data Science: Data Structures 0/6

    • Lecture2.1
      Lists 38 min
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      Tuples part-1 10 min
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      Tuples part-2 04 min
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      Sets 16 min
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      Dictionary 21 min
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      Strings 16 min
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      Types of functions 25 min
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      Function arguments 10 min
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      Lambda functions 08 min
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      Modules 07 min
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      Packages 06 min
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      Space and Time Complexity: Find largest number in a list 20 min
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      Binary search 17 min
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      Find elements common in two lists 06 min
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      Find elements common in two lists using a Hashtable/Dictionary 12 min
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    exploratory data analysis (EDA) is an approach to analyzing data sets to summarize their main characteristics, often with visual methods. A statistical model can be used or not, but primarily EDA is for seeing what the data can tell us beyond the formal modeling or hypothesis testing task.

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    • Lecture10.1
      Introduction to Probability and Statistics 17 min
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      Population and Sample 07 min
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      Gaussian/Normal Distribution and its PDF(Probability Density Function) 27 min
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      CDF(Cumulative Distribution function) of Gaussian/Normal distribution 11 min
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      Kernel density estimation 07 min
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      Q-Q plot:How to test if a random variable is normally distributed or not? 23 min
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      Discrete and Continuous Uniform distributions 13 min
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      How to randomly sample data points (Uniform Distribution) 10 min
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      Bernoulli and Binomial Distribution 11 min
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      Log Normal Distribution 12 min
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      Power law distribution 12 min
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      Box cox transform 12 min
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      Co-variance 14 min
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      Pearson Correlation Coefficient 13 min
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      Spearman Rank Correlation Coefficient 07 min
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      Correlation vs Causation 03 min
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      Confidence interval (C.I) Introduction 08 min
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      Computing confidence-interval given distribution 11 min
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      C.I for mean of a normal random variable 14 min
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      Confidence interval using bootstrapping 17 min
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      Hypothesis testing methodology, Null-hypothesis, p-value 16 min
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      Resampling and permutation test 15 min
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      K-S Test 06 min
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      K-S Test for similarity of two distributions 15 min
  • Dimensionality reduction and Visualization: 0/10

    In machine learning and statistics, dimensionality reduction or dimension reduction is the process of reducing the number of random variables under consideration, via obtaining a set of principal variables. It can be divided into feature selection and feature extraction.

    • Lecture11.1
      What is dimensionality reduction? 05 min
    • Lecture11.2
      Row Vector and Column Vector 04 min
    • Lecture11.3
      Represent a dataset: D= {x_i, y_i} 04 min
    • Lecture11.4
      Represent a dataset as a Matrix 07 min
    • Lecture11.5
      Data preprocessing: Column Normalization 30 min
    • Lecture11.6
      Mean of a data matrix 06 min
    • Lecture11.7
      Data preprocessing: Column Standardization 16 min
    • Lecture11.8
      Co-variance of a Data Matrix 30 min
    • Lecture11.9
      MNIST dataset (784 dimensional) 20 min
    • Lecture11.10
      Code to load MNIST dataset. 12 min
  • Principal Component Analysis 0/10

    • Lecture12.1
      Why learn PCA? 04 min
    • Lecture12.2
      Geometric intuition of PCA 14 min
    • Lecture12.3
      Mathematical objective function of PCA 13 min
    • Lecture12.4
      Alternative formulation of PCA: distance minimization 10 min
    • Lecture12.5
      Eigenvalues and eigenvectors 23 min
    • Lecture12.6
      PCA for dimensionality reduction and visualization 10 min
    • Lecture12.7
      Visualize MNIST dataset 05 min
    • Lecture12.8
      Limitations of PCA 05 min
    • Lecture12.9
      PCA Code example using Visualization 19 min
    • Lecture12.10
      PCA for dimensionality reduction (not-visualization) 15 min
  • T-distributed stochastic neighborhood embedding (t-SNE) 0/7

    • Lecture13.1
      What is t-SNE? 07 min
    • Lecture13.2
      Neighborhood of a point, Embedding 07 min
    • Lecture13.3
      Geometric intuition of t-SNE 09 min
    • Lecture13.4
      Crowding problem 08 min
    • Lecture13.5
      How to apply t-SNE and interpret its output (distill.pub) 38 min
    • Lecture13.6
      t-SNE on MNIST 07 min
    • Lecture13.7
      Code example of t-SNE 09 min
  • Real world problem: Predict rating given product reviews on Amazon 0/17

    • Lecture14.1
      Dataset overview: Amazon Fine Food reviews(EDA) 23 min
    • Lecture14.2
      Data Cleaning: Deduplication 15 min
    • Lecture14.3
      Why convert text to a vector? 14 min
    • Lecture14.4
      Bag of Words (BoW) 18 min
    • Lecture14.5
      Text Preprocessing: Stemming, Stop-word removal, Tokenization, Lemmatization. 15 min
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      uni-gram, bi-gram, n-grams. 09 min
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      tf-idf (term frequency- inverse document frequency) 09 min
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      Why use log in IDF? 14 min
    • Lecture14.9
      Word2Vec. 16 min
    • Lecture14.10
      Avg-Word2Vec, tf-idf weighted Word2Vec 09 min
    • Lecture14.11
      Bag of Words( Code Sample) 19 min
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      Text Preprocessing( Code Sample) 11 min
    • Lecture14.13
      Bi-Grams and n-grams (Code Sample) 05 min
    • Lecture14.14
      TF-IDF (Code Sample) 06 min
    • Lecture14.15
      Word2Vec (Code Sample) 12 min
    • Lecture14.16
      Avg-Word2Vec and TFIDF-Word2Vec (Code Sample) 02 min
    • Lecture14.17
      Exercise: t-SNE visualization of Amazon reviews with polarity based color-coding 06 min
  • Classification And Regression Models: K-Nearest Neighbors 0/32

    • Lecture15.1
      How “Classification” works? 10 min
    • Lecture15.2
      Data matrix notation 07 min
    • Lecture15.3
      Classification vs Regression (examples) 06 min
    • Lecture15.4
      K-Nearest Neighbors Geometric intuition with a toy example 11 min
    • Lecture15.5
      Failure cases of KNN 07 min
    • Lecture15.6
      Distance measures: Euclidean(L2) , Manhattan(L1), Minkowski, Hamming 20 min
    • Lecture15.7
      Cosine Distance & Cosine Similarity 19 min
    • Lecture15.8
      How to measure the effectiveness of k-NN? 16 min
    • Lecture15.9
      Test/Evaluation time and space complexity 12 min
    • Lecture15.10
      KNN Limitations 09 min
    • Lecture15.11
      Decision surface for K-NN as K changes 23 min
    • Lecture15.12
      Overfitting and Underfitting 12 min
    • Lecture15.13
      Need for Cross validation 22 min
    • Lecture15.14
      K-fold cross validation 17 min
    • Lecture15.15
      Visualizing train, validation and test datasets 13 min
    • Lecture15.16
      How to determine overfitting and underfitting? 19 min
    • Lecture15.17
      Time based splitting 19 min
    • Lecture15.18
      k-NN for regression 05 min
    • Lecture15.19
      Weighted k-NN 08 min
    • Lecture15.20
      Voronoi diagram 04 min
    • Lecture15.21
      Binary search tree 16 min
    • Lecture15.22
      How to build a kd-tree 17 min
    • Lecture15.23
      Find nearest neighbours using kd-tree 13 min
    • Lecture15.24
      Limitations of Kd tree 13 min
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      Extensions 03 min
    • Lecture15.26
      Hashing vs LSH 10 min
    • Lecture15.27
      LSH for cosine similarity 40 min
    • Lecture15.28
      LSH for euclidean distance 13 min
    • Lecture15.29
      Probabilistic class label 08 min
    • Lecture15.30
      Code Sample:Decision boundary . [./knn/knn.ipynb and knn folder] 23 min
    • Lecture15.31
      Code Sample:Cross Validation 13 min
    • Lecture15.32
      Exercise: Apply k-NN on Amazon reviews dataset 05 min
  • Classification algorithms in various situations 0/19

    • Lecture16.1
      Introduction 05 min
    • Lecture16.2
      Imbalanced vs balanced dataset 23 min
    • Lecture16.3
      Multi-class classification 12 min
    • Lecture16.4
      k-NN, given a distance or similarity matrix 09 min
    • Lecture16.5
      Train and test set differences 22 min
    • Lecture16.6
      Impact of outliers 07 min
    • Lecture16.7
      Local outlier Factor (Simple solution :Mean distance to Knn) 13 min
    • Lecture16.8
      K-Distance(A),N(A) 04 min
    • Lecture16.9
      Reachability-Distance(A,B) 08 min
    • Lecture16.10
      Local reachability-density(A) 09 min
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      Local outlier Factor(A) 21 min
    • Lecture16.12
      Impact of Scale & Column standardization 12 min
    • Lecture16.13
      Interpretability 12 min
    • Lecture16.14
      Feature Importance and Forward Feature selection 22 min
    • Lecture16.15
      Handling categorical and numerical features 24 min
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      Handling missing values by imputation 21 min
    • Lecture16.17
      Curse of dimensionality 27 min
    • Lecture16.18
      Bias-Variance tradeoff 24 min
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      Best and worst cases for an algorithm 06 min
  • Performance measurement of models 0/8

    • Lecture17.1
      Accuracy 15 min
    • Lecture17.2
      Confusion matrix, TPR, FPR, FNR, TNR 25 min
    • Lecture17.3
      Precision and recall 10 min
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      Receiver Operating Characteristic Curve (ROC) curve and AUC 19 min
    • Lecture17.5
      Log-loss 12 min
    • Lecture17.6
      R-Squared/Coefficient of determination 14 min
    • Lecture17.7
      Median absolute deviation (MAD) 05 min
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      Distribution of errors 07 min
  • Logistic Regression 0/18

    • Lecture18.1
      Geometric intuition 31 min
    • Lecture18.2
      Sigmoid function: Squashing 37 min
    • Lecture18.3
      Mathematical formulation of Objective function 24 min
    • Lecture18.4
      Weight vector 11 min
    • Lecture18.5
      L2 Regularization: Overfitting and Underfitting 26 min
    • Lecture18.6
      L1 regularization and sparsity 11 min
    • Lecture18.7
      Probabilistic Interpretation: Gaussian Naive Bayes 19 min
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      Loss function interpretation 24 min
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      Hyperparameter search: Grid Search and Random Search 16 min
    • Lecture18.10
      Column Standardization 05 min
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      Feature importance and Model interpretability 14 min
    • Lecture18.12
      Collinearity of features 14 min
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      Test/Run time space and time complexity 10 min
    • Lecture18.14
      Real world cases 11 min
    • Lecture18.15
      Non-linearly separable data & feature engineering 28 min
    • Lecture18.16
      Code sample: Logistic regression, GridSearchCV, RandomSearchCV 23 min
    • Lecture18.17
      Exercise: Apply Logistic regression to Amazon reviews dataset. 06 min
    • Lecture18.18
      Extensions to Logistic Regression: Generalized linear models 09 min
  • Linear Regression 0/4

    • Lecture19.1
      Geometric intuition 13 min
    • Lecture19.2
      Mathematical formulation 14 min
    • Lecture19.3
      Real world Cases 08 min
    • Lecture19.4
      Code sample for Linear Regression 13 min
  • Solving optimization problems : Stochastic Gradient Descent 0/12

    • Lecture20.1
      Differentiation 29 min
    • Lecture20.2
      Online differentiation tools 08 min
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      Maxima and Minima 12 min
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      Vector calculus: Grad 10 min
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      Gradient descent: geometric intuition 19 min
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      Learning rate 10 min
    • Lecture20.7
      Gradient descent for linear regression 08 min
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      SGD algorithm 09 min
    • Lecture20.9
      Constrained Optimization & PCA 14 min
    • Lecture20.10
      Logistic regression formulation revisited 06 min
    • Lecture20.11
      Why L1 regularization creates sparsity? 17 min
    • Lecture20.12
      Exercise: Implement SGD for linear regression 06 min
  • Decision Trees 0/15

    • Lecture21.1
      Geometric Intuition: Axis parallel hyperplanes 17 min
    • Lecture21.2
      Sample Decision tree 08 min
    • Lecture21.3
      Building a decision Tree:Entropy 19 min
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      Building a decision Tree:Information Gain 10 min
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      Building a decision Tree: Gini Impurity 07 min
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      Building a decision Tree: Constructing a DT 21 min
    • Lecture21.7
      Building a decision Tree: Splitting numerical features 08 min
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      Feature standardization 04 min
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      Building a decision Tree:Categorical features with many possible values 07 min
    • Lecture21.10
      Overfitting and Underfitting 08 min
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      Train and Run time complexity 07 min
    • Lecture21.12
      Regression using Decision Trees 09 min
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      Cases 12 min
    • Lecture21.14
      Code Samples 09 min
    • Lecture21.15
      Exercise: Decision Trees on Amazon reviews dataset 03 min
  • Ensemble Models 0/19

    • Lecture22.1
      What are ensembles? 06 min
    • Lecture22.2
      Bootstrapped Aggregation (Bagging) Intuition 17 min
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      Random Forest and their construction 15 min
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      Bias-Variance tradeoff 07 min
    • Lecture22.5
      Bagging :Train and Run-time Complexity. 09 min
    • Lecture22.6
      Bagging:Code Sample 06 min
    • Lecture22.7
      Extremely randomized trees 08 min
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      Random Tree :Cases 06 min
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      Boosting Intuition 17 min
    • Lecture22.10
      Residuals, Loss functions and gradients 13 min
    • Lecture22.11
      Gradient Boosting 10 min
    • Lecture22.12
      Regularization by Shrinkage 08 min
    • Lecture22.13
      Train and Run time complexity 06 min
    • Lecture22.14
      XGBoost: Boosting + Randomization 14 min
    • Lecture22.15
      AdaBoost: geometric intuition 07 min
    • Lecture22.16
      Stacking models 09 min
    • Lecture22.17
      Cascading classifiers 15 min
    • Lecture22.18
      Kaggle competitions vs Real world 09 min
    • Lecture22.19
      Exercise: Apply GBDT and RF to Amazon reviews dataset. 04 min
  • Featurization and Feature engineering. 0/18

    • Lecture23.1
      Introduction 17 min
    • Lecture23.2
      Moving window for Time Series Data 25 min
    • Lecture23.3
      Fourier decomposition 22 min
    • Lecture23.4
      Deep learning features: LSTM 08 min
    • Lecture23.5
      Image histogram 23 min
    • Lecture23.6
      Keypoints: SIFT. 10 min
    • Lecture23.7
      Deep learning features: CNN 04 min
    • Lecture23.8
      Relational data 10 min
    • Lecture23.9
      Graph data 12 min
    • Lecture23.10
      Indicator variables 07 min
    • Lecture23.11
      Feature binning 14 min
    • Lecture23.12
      Interaction variables 08 min
    • Lecture23.13
      Mathematical transforms 04 min
    • Lecture23.14
      Model specific featurizations 09 min
    • Lecture23.15
      Feature orthogonality 11 min
    • Lecture23.16
      Domain specific featurizations 04 min
    • Lecture23.17
      Feature slicing 10 min
    • Lecture23.18
      Kaggle Winners solutions 07 min
  • Taxi demand prediction in New York City 0/18

    • Lecture24.1
      Business/Real world problem Overview 09 min
    • Lecture24.2
      Objectives and Constraints 11 min
    • Lecture24.3
      Mapping to ML problem :Data 08 min
    • Lecture24.4
      Mapping to ML problem :dask dataframes 11 min
    • Lecture24.5
      Mapping to ML problem :Fields/Features. 06 min
    • Lecture24.6
      Mapping to ML problem :Time series forecasting/Regression 08 min
    • Lecture24.7
      Mapping to ML problem :Performance metrics 06 min
    • Lecture24.8
      Data Cleaning :Latitude and Longitude data 04 min
    • Lecture24.9
      Data Cleaning :Trip Duration. 07 min
    • Lecture24.10
      Data Cleaning :Speed. 05 min
    • Lecture24.11
      Data Cleaning :Distance. 02 min
    • Lecture24.12
      Data Cleaning :Fare 06 min
    • Lecture24.13
      Data Cleaning :Remove all outliers/erroneous points 03 min
    • Lecture24.14
      Data Preparation:Clustering/Segmentation 19 min
    • Lecture24.15
      Data Preparation:Time binning 05 min
    • Lecture24.16
      Data Preparation:Smoothing time-series data. 05 min
    • Lecture24.17
      Data Preparation:Smoothing time-series data part2 02 min
    • Lecture24.18
      Data Preparation: Time series and Fourier transforms. 13 min
  • Base line models 0/5

    • Lecture25.1
      Ratios and previous-time-bin values 09 min
    • Lecture25.2
      Simple moving average 08 min
    • Lecture25.3
      Weighted Moving average. 05 min
    • Lecture25.4
      Exponential weighted moving average 06 min
    • Lecture25.5
      Results. 04 min
  • Regression models: 0/6

    • Lecture26.1
      Train-Test split & Features 03 min
    • Lecture26.2
      Linear regression. 03 min
    • Lecture26.3
      Random Forest regression. 04 min
    • Lecture26.4
      Xgboost Regression 02 min
    • Lecture26.5
      Model comparison. 06 min
    • Lecture26.6
      Assignment. 06 min

Statement:

Predict the pick up density of yellow cabs at a given particular time and a location in new york city.

Yellow Taxi: Yellow Medallion Taxicabs

These are the famous NYC yellow taxis that provide transportation exclusively through street-hails. The number of taxicabs is limited by a finite number of medallions issued by the TLC. You access this mode of transportation by standing in the street and hailing an available taxi with your hand. The pickups are not pre-arranged.

In this project we are considering only the yellow taxis for the year of 2015

The data used in the attached datasets were collected and provided to the NYC Taxi and Limousine Commission (TLC)

Data:

Data type: CSV files

Train data: train.csv

  • pick-up and drop-off dates/times,
  • pick-up and drop-off locations,
  • trip distances,
  • itemized fares,
  • rate types,
  • payment types,
  • driver-reported passenger counts

Total number of records in train data: 146 million

Data Size: 12GB

 

Key Points:

  1. Validity of this course is 240 days( i.e Starts from the date of your registration to this course)
  2. Expert Guidance, we will try to answer your queries in atmost 24hours
  3. 10+ machine learning algorithms will be taught in this course.
  4. No prerequisites– we will teach every thing from basics ( we just expect you to know basic programming)
  5.  Python for Data science is part of the course curriculum.

 

Target Audience:

We are building our course content and teaching methodology to cater to the needs to students at various levels of expertise and varying background skills. This course can be taken by anyone with a working knowledge of a modern programming language like C/C++/Java/Python. We expect the average student to spend at least 5 hours a week over a 6 month period amounting to a 145+ hours of effort. More the effort, better the results. Here is a list of customers who would benefit from our course:

    1. Undergrad (BS/BTech/BE) students in engineering and science.
    2. Grad(MS/MTech/ME/MCA) students in engineering and science.
    3. Working professionals: Software engineers, Business analysts, Product managers, Program managers, Managers, Startup teams building ML products/services.

Course Features

  • Lectures 308
  • Quizzes 0
  • Duration 70+ hours
  • Skill level All levels
  • Language English
  • Students 10
  • Assessments Yes
QUALIFICATION: Masters from IISC Bangalore PROFESSIONAL EXPIERENCE: 9+ years of Experience( Yahoo Labs, Matherix Labs Co-founder and Amazon)
₹15,000.00

    2 Comments

  1. M.N.L.Kashyap
    March 15, 2018

    sir,
    Will you help in executing the project and do we get lectures on how to implement this particular project ?
    Can we expect implementation ?

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