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

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. Show moreShow less.