Contents

1) DATA SCIENCE

• Module 1: PYTHON PROGRAMMING :: > A] Python Introduction & Setting UP the Environment. B] Python Basic Syntax and Data Types. C] Operators in Python. D] Strings in Python. E] Slicing, Indexing & using methods on Lists. F] Working with Tuples. G] Performing Set operation in program. H] Creating Dictionaries & using dictionaries Methods. I} Uses of Python Conditional Statements. J} [ ] Iterating with Loops in Python. K] Getting Started with HackerRank use cases and working on them. L] Using List & Dictionaries comprehension. M] Functions, Anonymous Functions with lambda, filter, map reduce in python. N] Creating & Using Generators. O] Modules, Exceptions and error handling. P] Class and Objects( OOPS). Q] Date and Time, Regex. R] Reading , Writing , Appending, Opening and closing Files. S] APIs the Unsung Hero of the Connected World. T] Python for WEB DEVELOPMENT- Flask, Hands-on Projects.
• MODULE 2- Data Analysis ::- A] Packages- Creating and Importing. B] Web Scraping - Working on Scraping Data from Static Dynamic Websites. C] Exploratory data analysis(EDA) using Pandas and NumPy. D] Data Visualization using Matplotlib, Seaborn, & Plothy, Instagram Reach Analysis. E] Database Access :- Working on SQL Queries. F] MS Excel :- Worksheet, Calculation, Fill Handle, Formula, Quick Functions, Charts & Visualizations, Advance Excel, Tableau and Power BI- Instagram Reach Analysis.
• MODULE 3:: - STATISTICS::- 1] Descriptive Statistics:: >- (Data- Types of data, A Measure of Central tendency -Mean- Median- Mode, A Measure of shape- Variance - standard deviation, Range, IQR, The measure of shape - Skewness, & kurtosis, Probability Distribution- Discrete and Continuous. Uniform Distribution. Expected values, Variance & Means. Gaussian/ Normal Distribution. Properties, mean, variance, empirical rule of normal distribution. Standard normal distribution and Z-Score). 2> Inferential Statistics : (Central Limit Theorem. Hypothesis testing - Null & Alternate hypothesis. Type -I & Type-II error. Critical Value, Significance level, p-value. One - tailed & two-tailed test. T- test- one sample, two-sample, and paired t-test, f-test. One way and two way ANOVA, Chi-Square test.
• MODULE 4 ::- Machine Learning ::> Introduction to machine learning, Introduction to data science and its applications, Data Engineering and pre processing, Model evaluation and Hyperparameter Tuning, Supervised Learning - Regression (house price prediction), Supervised Learning - Classification (Heart disease detection and food order prediction), SVM, KNN & Naive Bayes - contact Tracing & Sarcasm Detection. Ensemble Methods and Boosting- Medical Insurance Price Prediction. Unsupervised Learning - Clustering(Credit card clustering). Unsupervised Learning - Dimensionality Reduction, MNIST Data. Recommendation Systems, News Recommendation System. Reinforcement Learning- working with Open AI Gym. Developing API using Flask / Webapp with Sreamlit. Deployment of ML Models. Project work and Consolidation.
• MODULE 5 ::- NLP- Natural Language Processing , # Introduction to NLP, understand the basic of NLP, & its application, challenges, # Named Entity Recognition(NER), Understand the various approaches and tools used for NER, such as rule-based systems, statistical models, and deep learning. # Text Preprocessing : Learn about tokenization, stemming, lemmatization, stop word removal, and other techniques for text preprocessing. # Text Representation: Explore techniques such as Bag-of-Words(BOW), TF-IDF, and word embeddings(e.g., Word2Vec, Glove) for representing text data. # Sequential Models: Introduction to RNN, LSTM, Hands on Keras LSTM. # Sentiment Analysis: Study sentiment analysis techniques, build a sentiment analysis model using supervised learning, and evaluate its performance. # Real Time Sentiment Analysis.
• MODULE 6:: - Deep Learning -- # RISE OF THE DEEP LEARNING: - # Introduction # History of Deep Learning # Perceptrons # Multi-Level Perceptrons # Representations # Training Neural Networks # Activation Functions. ARTIFICIAL NEURAL NETWORKS:- # Introduction # Deep Learning # Understanding Human Brain # In-Depth Perceptrons # Example of Perceptron # Multi Classifier # Neural Networks # Input Layer # Output Layer # Sigmoid Function # Introduction to Tensorflow and Keras # CPU vs GPU # Introduction to Google collaboratory # Training Neural Networks # Understanding Notations # Activation Functions # Hyperparameter tuning in Keras # Feed-Forward Networks # Online offline mode # Bidirectional RNN # Understanding Dimensions # Back Propagation # Loss function # SGD # Regularization # Training for batches -- Facial Emotion Recognition. ** CONVOLUTION NEURAL NETWORKS{CNN} - (In Detail) ** CNN Transfer Learning:- # Introduction # AlexNet # GoogleNet # Resnet # Transfer learning using Keras ## face mask detection. ** RNN - Recurrent Neural Networks ** ( Details) - Covid-19 Cases Prediction. ** GENERATIVE MODELS & GANs:- # Introduction to Generative Models, # Understanding GANs( Generative Adversarial Networks) # GAN Architecture, # GAN Training, # Evaluating GAN Performance, # GAN Variants and Applications.
• MODUL1E 7:: - COMPUTER VISION :: > 1// Computer Vision # Intro to Open CV # Reading & Writing Images # Saving images # Face detection and Eye detection using CV # VGC # Real time Pose Estimator . 2// Projects & Case Study ::- # Real-time Rain Prediction using ML # Real-time Drowsiness Detection Alert System # House Price Prediction using LSTM # Customizable Chabot using OpenAI API # Fire and Smoke Detection using CNN