JOINT NGSGDS UPSKILLING GATE DSAI 2025 PREPARATION 100h Sessions
BIOINFORMATICS SHARMA
#BOTH Recordings as well as #Govt_Of_India Certification Provided !!!
Apart from Strictly adhering to the #GATE_2025_DSAI ( Data Science and Artificial Intelligence) Syllabus enclosed below, the #NGS_GDS ( Next Generation Sequencing + Genomics Data Science) Modules shall Cover the following Topics, Conceptual & Practical
Interested People can #REGISTER with their NAME, EMAIL, MOBILE NR. @ this URL // LINK === https://topmate.io/bioinformatics/1197361
https://topmate.io/bioinformatics/1197361
#List_Of_100_Hours_JOB_ORIENTED_UPSKILLING_Topics_Covered :-
Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), Natural Language Processing (NLP) & Big Data Analytics (BDA), Python3 basics, Advanced #Py3 ( Functions, Methods, Modules, Packages, Dictionary, #FP: Functional Programming- Lambda, Iterators & Iterables, Generators, List Comprehensions, #DS with Python: NumPy, Pandas ( Series and Dataframe), #scikit_learn [ Linear, Polynomial and #RBF Gaussian Kernels & Soft Margin Classification), Visualization using Matplotlib, Seaborn, Bokeh and Altair; Importing #DATAsets from Flat files, Excel, JSON, XML, STATA & SAS, HDF5, RDBMS ( #Eg: SQLite) files, web-Scraping with urllib & requests Packages, Reading HTML with BeautifulSoup, #API ( Application Programming Interfaces) for Instance Movie, Wikipedia, Twitter; Cleaning Data by #ETL ( Extraction, Transformation, Loading), Supervised #ML: k-NN, Linear Models, #SVM, Pre-processing ML input data, Tree-based Models, Unsupervised ML using k-Means/ K-Medoids Clustering; #DL: Deep Learning ( Fwd Propogation, Activation functions, Deeper Networks & Model Optimization, Gradient Descent & Backpropogation, Creating #keras Regression & Classification Models (Validation & Capacity), #CNN ( Convolutional Neural Networks), #NLP: REGEX, Advanced Tokenization, Charting word-length with #nltk , Bag of Words & Counting, Natural Text Pre-processing, Tf-idf with Gensim, Named Entity Recognition, SpaCy, Multi-lingual NER with polyglot, "fake-news" Classifier Example, Dialog flow and RASA NLU, #AI : History, Use-cases, Working Principle, Introduction to Probability Distributions ( Bernoulli, Uniform, Binomial, Normal, Poisson, Exponential) using #cran_R; Hypothesis Testing ( #NULL & #ALTERNATE), Bayesian #HT, P-values, Student's #t-Test, One Sample Z-test, ANOVA and its variants, Accuracy and Precision, Limits and Derivatives of functions, Chain-Rule and L'-Hospital Rule, #SciPy Library in Python3, Linear and #LASSO Regression ( Shrinkage and Regularization), #Logistic Regression, #RF ( Random Forest ML Algorithms), Non-linear and Kernel Tricks using Hyper-Parameters ( C, #gamma , Choosing the appropriate #K value in K-means Clustering), #Tf ( TensorFlow), Capstone Projects: Automatic-ChatBot using NLP-AI and Keras-Tensorflow and Kubernetes Capstone Projects for your Github.
