Machine Learning Using PySpark Classroom Training Bangalore

Instructor Bangalore

Brief Info

6+ years of experienced & result oriented data analytics professional possessing a proven track record of successfully applying statistical modeling, algorithm development & machine learning techniques to solve key business problems. Highly skilled in applying advanced machine learning/statistical algorithms such as Neural Network, Random Forests, Ensemble Models, Clustering etc. to the real world problems using Python. Looking forward to applying the acquired a gamut of skills to a challenging role in the data analytics space.

Key Skills

Technical Skills

Data Science Tools: Python, R, Advanced Excel, PySpark, Basic Django Framework, Basic SQL Packages: Numpy,Scipy, Pandas, Seaborn, Matplotlib, Statsmodels, Scikit-Learn, NLTK, NLP, Django, BeautifulSoup, TFLearn, Keras Statistics/Machine Learning:

Agorithm:- Linear Regression, Logistic Regression , Association Rule Mining, K-Means Clustering, Artificial Neural Network , Support Vector Machine , Random Forest, Naive Bayes , Decision Tree. • CRISP-DM Methodology:CRISP-DM Methodology:- Dimensionality Reduction, Principal Component Analysis , Outlier treatment , Missing Value Treatment, Variance Inflation Factor. • Descriptive and Inferential Descriptive and Inferential

Statistics:- Central Tendency, Variance ,Standard Deviation, Confidence Level, p value, Hypothesis Testing , ANOVA ,Confusion Matrix, multicollinearity , autocorrelation ,KS table calculation , ROC,AUC. • Text Analytics :Text Analytics :- Auto Summarization, Sentiment Analysis , Lexical Resources , Regular Expression , Topic Analysis , web scraping. • Field Of Analytics: Field Of Analytics:- Customer Segmentation , Market Basket Analysis , RFM , Customer Feedback Analysis ,Repeat Purchase, Customer Churn. • Domain :Domain :- Retail, Telecom, BFSI, etc. • Deep Learning :Deep Learning :- Activation Function , DNN Classifier , tensorflow , DNN Regressor , Learning Rate , Convolution Neural Network(CNN).

In depth working experience in different statistical tools like Python with Jupyter Notebook, PySpark, Adv. Excel, etc. •Working on different machine learning techniques like Regression, Classification, Clustering , Association Rule Mining, Non-linear Classifier like (ANN, SVM, Random Forest, Naïve Bayesian), Data Mining Techniques like Missing Value and outlier analysis, PCA, Variable binning, etc using matplotlib ,numpy ,pandas, scikit-learn, statsmodel etc. • Experienced in different Big Data techniques like PySpark , Spark-ML , HDFS , Sqoop etc using Cloudera distribution. • Working on text analytics technique by using python package nltk , Beautiful Soup for web scraping, stop words removal, tokenization, lemmatization, frequency on textual data etc. and exposure on neural network , CNN using Tensorflow package.