Ai/Data Scientist - Python/R/Big Data Master Class

includes Data Science, Machine Learning-R/Python, Big Data-Hive, Flume,Sqoop, Pig and more.(Beginners To Expert Level)

4.28 (29 reviews)

Udemy

platform

English

language

Data Science

What you will learn

Analytics For Beginners: Learn the interdisciplinary concepts of analytics with the help of success stories.

Analytics For Beginners: Become familiar of other companies using analytics as a core part of success stories.

Analytics For Beginners: Understand why and how analytics is so important in every profession.

Analytics For Beginners: Do data exploration and manipulation like transpose, remove duplicates, pivot table, manipulate data with time & Filter using Excel

Advance data manipulation like Merge and Unmerge, cells Text To Column Function, Vlookup, Data Scaling, Consolidation, Conditional Operator If-Else and more.

Analytics For Beginners: Even perform Ai in excel using built-in predictive analytics.

Data Science: like Binomial, Poisson, Hyper Geometric, Negative Binomial, Geometric discrete probability distributions Normal distribution and T-dist

Data Science: Perform hypothesis testing with Normal Distribution and T-distribution using One-Tail and Two-Tail Directional hypothesis.

Data Science: Chi-square Test-Of-Association, Goodness-Of-Fit and more. Follow the program syllabus in our course curriculum to know more in detail.

Perform Anova for multiple levels with and without replication and for count and categorical data using Chi-square Test-Of-Association & Goodness-Of-Fit

Big Data Analytics: The architecture of Hadoop, Map Reduce, YARN, Hadoop Distribution File System, Name node check-pointing, Hadoop Rack Awareness in detail.

Master and perform big data analysis with on-demand big data tools like PIG, HIVE, Impala and automate to stream live data & workflow with Flume and Scoop.

Big Data Analytics: Control parallel processing and create User Define Functions to automate the scripting language without writing a line of code.

Master and perform External Table to share the data among different applications and even partition the table for faster processing.

R-programming: Learn and master how to manipulate data, impute missing values and visualization using base graphics, ggplot & geo-spatial plots.

R-programming: Learn and perform exploratory analysis and work with different file type & data sources.

Machine Leaning: Master how to create supervised models like linear and logistic regression, support vector machine and more to solve real world problems.

Also master to create unsupervised models like k-means and hierarchical clustering, decision trees, random forest to automate solutions for real world problems.

Learn and implement the concepts of Feature Engineering, Principle Component Analysis, Times and more.

NLP: Learn and master data transformation, create text corpus, remove spare terms with Tm package and manipulate text data using regular expression.

Sentiment analysis to negative or the positive response and topic modeling using LDA to identify the topics of 1000 documents without being going through each

Understand the connection of each words using Network analysis or cluster the words used to solve problems like search keywords used to arrive on the website

Bonus: Machine Learning, Deep Learning with Python - Premium Self Learning Resource Pack Free

Full Guide to Linear Regression, Polynomial Regression, Support Vector Regression, Decision Trees Regression, Random Forest Regression and more.

Full guide to knn, logistic, support vector machine, kernel svm, naive bayes, decision tree classification, random forest classification.

Let’s Develop Artificial Neural Network in 30 lines of code. Simple yet Complete Guide on how to apply ANN for classification

Let’s Develop Artificial Neural Network in 30 lines of code — II. Part — II Complete Guide to apply ANN for Regression with K-Fold Validation for accuracy.

Reinforcement Learning in 31 Steps. using Upper Confidence Bound(UCB) & Thompson Sampling for Social Media Marketing Campaign Click Through Rate Optimization

What is PCA and How can we apply Real Quick and Easy Way? Learn how to apply Principal Component Analysis (PCA) using python

What is Supervised Linear Discriminant Analysis(LDA) ~ PCA. Let’s understand and perform supervised dimensionality reduction

What is Kernel PCA? using R & Python. 4 easy line of codes to apply the most advanced PCA for non-linearly separable data.

Association Rule Learning using Apriori and Eclat (R Studio) to predict Shopping Behavior.

Multi-Layer Perception Time Series Apply State of the Art Deep Learning MLP models for predicting sequence of numbers/time series data.

LSTMs for regression. Quick and easy guide to solve regression problems with Deep Learnings’ different types of LSTMs

Uni-Variate LSTM Time Series Forecasting. Apply State Of The Art Deep Learning Time Series Forecasting with the help of this template.

Multi-variate LSTM Forecasting. Apply state of the art deep learning time series forecasting using multiple inputs together to give a powerful prediction.

Multi-Step LSTM Time Series Forecasting. Apply Advanced Deep Learning Multi-Step Time Series Forecasting with the help of this template.

Grid Search/Optuna/Halving/Hyperopt for ML & DL Models. Full guide on finding the best hyper parameters for our regular ml models to deep learning models

7 types of Multi*-Classification using python

LSTM MultiVariate MultiStep, Auto TS, Thymeboost, NeuralProphet, FbProphet

Parametric & Non-Parametric Hypothesis testing

Bias-Variance Decomposition & Statistical Comparison of 2 models via Paired ttest5x2

Time Series for non-linear data & Impute missing values for time series data

Chained and Multi-Label Classification & Regression

Huber, RANSAC, TheilSen Regressor & Classifier

Bagging Classifier, Boost Classifier, Calibrated Classifier via Isotonic, logistic regression and calibratedclassifierCV.

Synthetic Data Generation via, Gaussian Coupla, CouplaGAN, TVAE, and evaluate synthetic data

Next best alternative to Kmeans: Optics Clustering, Gaussian mixture model/GMM Clustering

Isolation Forest, LOF, OneClass SVM, Kernel Density Estimator, Genetic Algorithms, AutoML, Semi AutoML and more.