PYTHON & ADVANCED PYTHON

Introduction to Python, PyCharm, Language Fundamentals, Conditional Statements, Looping, Control Statements 4,
String Manipulation, Lists, Tuple, Dictionaries, Functions, Modules, Input-output, Exception Handling, OOPS Concepts, Regular Expressions, Map, Reduce, Filter tools.

MYSQL

Introduction – What is SQL? Installing MySQL on Windows, Creating the Databases, SELECT Statement, SELECT Clause, The WHERE Clause, The AND, OR, and NOT Operators, The IN Operator ,The BETWEEN Operator, The LIKE Operator, The REGEXP Operator, The IS NULL Operator, The ORDER BY Operator, The LIMIT Operator, Inner Joins, Joining Multiple Tables, Outer Joins, Outer Join Between Multiple Tables, Self Outer Joins, The USING Clause, Natural Joins, Cross Joins, Unions, Column Attributes, Inserting a Single Row, Inserting Multiple Rows, Creating a Copy of a Table, Updating a Single Row, Updating Multiple Rows, Using Subqueries in Updates, Deleting Rows.

MATHEMATICS FOR DATA SCIENCE

Vector spaces – 5.1.2. Subspaces, 5.1.3. Span, 5.1.4. basis and dimension, 5.1.5. Matrices and linear transformations – Linear map as a matrix, 5.1.6. rank and nullity of a matrix, 5.1.7. matrix multiplication, 5.1.8. inverse and transpose, 6.1.1. Mean 6.1.2. Median, 6.1.3. Mode, 6.1.4. variance and standard deviation, 6.1.5. co-variance and correlation, 7.1.1. Permutations and combinations, 7.1.2. unions and intersections, 7.1.3. random experiment, 7.1.4. sample space, 7.1.5. events, 7.1.6.
probability axioms, 7.1.7. conditional probability, 7.1.8. Bayes’ theorem, 7.1.9. random variables, 7.1.10. Discrete and continuous distributions-Uniform, 7.1.11. Binomial, 7.1.12. Poisson and Normal distributions.

DATA SCIENCE MODULES

8.1.2. Numpys, 8.1.3. Pandas, 8.1.4. Data Frame, 8.1.5. Sci-kit, 8.1.6. Exploratory Data Analytics using Python (EDA), 8.1.7. Data Wrangling, 8.1.8. Data Visualization, 8.1.9. Matplotlib, 8.1.10. Seaborn.

MACHINE LEARNING

Supervised Learning – Regression (Simple Linear Regression, Logistic Regression, 8.1.13. Multiple Linear Regression, 8.1.14. Polynomial Regression, 8.1.15. Decision Tree Regression, 8.1.16. Evaluating Regression Model Parameters), 8.1.17.
Classification ( K Nearest Neighbors ( KNN ), 8.1.18. Naive Bayes Classifier, 8.1.19. Decision Tree Algorithm, 8.1.20. Random Forest Algorithm, 8.1.21. Unsupervised Machine Learning – Introduction To Clustering Algorithms, 8.1.22. K-Means Clustering, 8.1.23. Elbow Method for the optimal value of k in K-Means, 8.1.24. Hierarchical Clustering, 8.1.26. Dimensionality Reduction, 8.1.27. Principal Component Analysis.

DEEP LEARNING

AI vs ML vs DL vs Data Science , Why Deep Learning Is Becoming Popular?, Introduction To Perceptron, Working of Perceptron With Weights And Bias, Forward Propagation, Backward Propagation And Weight Updation Formula, Chain Rule of Derivatives, Vanishing Gradient Problem , Different types of Activation Functions, Different types of Loss functions, Different type of Optimizers. CNN 8.1.33. CNN Alexnet 8.1.34. RNN 8.1.35. LSTM, 8.1.36. Keras 8.1.37. Tensorflow 8.1.44. Object Detection, image classification, sentiment analysis, recommendation system.

NLP

1. Text Preprocessing Level 1- Tokenization, Lemmatization, StopWords, POS
2. Text Preprocessing Level 2- Bag of Words, TFIDF, Unigrams, Bigrams, n-grams
3. Text Preprocessing – Gensim, Word2vec, AvgWord2vec
4. Solve Machine Learning Usecases
5. Get the Understanding of Artificial Neural Network
6. Understanding Recurrent Neural Networks, LSTM,GRU
7. Text Preprocessing Level 3- Word Embeddings, Word2vec
8. Bidirectional LSTM RNN, Encoders And Decoders, Attention Models
9. Speech recognition
10. Speech recognition
11. Chatbot

OPEN CV

Read, Display and Write an Image using OpenCV, Reading and Writing Videos using OpenCV Image Resizing with OpenCV, Cropping an Image using OpenCV, Image Rotation and Translation Using OpenCV, Annotating Images Using OpenCV, Color spaces in OpenCV, Image Filtering Using Convolution in OpenCV, image Thresholding in OpenCV, Blob Detection Using OpenCV, Edge Detection Using OpenCV, Contour Detection using OpenCV.

POWER BI

Installation Power BI Interface & Settings Creating Power BI dashboard Data Modelling Sorting & Conditional Formatting TOP N Filtering Card Visualization Dax Function Power of Slicer, Connect and organize your data, Create data cleansing processes in Power BI, Organize your data model. Build Interactive Reports, Deploy and share your solution.