Python A-Z, Data Analytics, Linear Algebra, Statistics, Probability Concepts, Machine Learning, Deep Learning, Data Science, Artificial Intelligence, Tableau.
Python & Advanced Python
Introduction to Python, PyCharm, Language Fundamentals, Conditional Statements, Looping, Control Statements, String Manipulation, Lists, Tuple, Dictionaries, Functions, Modules, Input-output, Exception Handling, OOPS Concepts, Regular Expressions, Multithreading, Functional Programming, Map, Reduce, Filter, Iter tools, Python to DB.
Mathematcis for Data Science
Linear Algebra:
Vector spaces, subspaces, span, basis and dimension,
Matrices and linear transformations- Linear map as a matrix, rank and nullity of a matrix, matrix multiplication, inverse and transpose, eigen values, eigen vectors
Introductory statistics:
Mean, median, mode, variance and standard deviation, co-variance and correlation
Probability concepts:
Permutations and combinations, unions and intersections, random experiment, sample space, events, probability axioms, conditional probability, Bayes’ theorem, random variables,
Discrete and continuous distributions-Uniform, Binomial, Poisson and Normal distributions
(sampling, central limit theorem)
Data Science & ML
A-Z of Python(Core and Advanced), Numpys, Pandas, Data Frame, Sci-kit, Exploratory Data Analytics using Python (EDA), Data Wrangling, Data Visualization, Matplotlib, Seaborn, Machine Learning, Supervised Learning - Regression (Simple Linear Regression, Logistic Regression, Multiple Linear Regression, Polynomial Regression, Decision Tree Regression, Evaluating Regression Model Parameters), Classification ( K Nearest Neighbors ( KNN ), Naive Bayes Classifier, Decision Tree Algorithm, Random Forest Algorithm, SVM), Unsupervised Machine Learning - Introduction To Clustering Algorithms, K-Means Clustering, Elbow Method for the optimal value of k in K-Means, Hierarchical Clustering, Capstone Project, Dimensionality Reduction, Principal Component Analysis.
Deep Learning & AI
Natural Language Processing(NLP), NLTK, Neural Networks, CNN, CNN Alexnet, RNN, LSTM, TFIDF, Keras, Tensorflow, Speech recognition, Transfer Learning, Chatbot, Microsoft bot framework, AI chatbot, Rasa chatbot, Open Computer Vision (OpenCV), Optical Character Recognition (OCR), Capstone Project.
Data Visualization - Tableau
Working with Tableau Public, Connecting Data with Tableau, Relationships in Tableau, Filters in Tableau, Adding Dimensions in Tableau, Granularity in Tableau Analysis and Calculations in Tableau, Plotting in Tableau, Logical Operations on Tableau, Visualisations in Tableau Dashboard and Stories
Case Studies
Capstone Project
Mode: Online & Offline
⏰ Duration: 4 Months, 6 Days a Week, 2-3 Hours/day
📅 Next Batch : May 19th 2021
🎟️ Admissions : https://wa.me/918086651651
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