Rating 4.35 out of 5 (24 ratings in Udemy)

• 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 For ML & Deep Learning Models. Full guide to grid search 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, Synthetic Data Evaluation, OSS, NCR, SMOTE, CouplaGAN, TVAE, A-Z Clustering
• Isolation Forest, LOF, Bagging Classifier, Boost Classifier, Auto-TS-Ensemble, Calibrated Classifier, Genetic Algorithms, AutoML, Semi AutoML and more.