Data Science AI ML Course in Bangalore

 Introduction to Python: History, features, installation, and basic syntax.
 Variables and Data Types: Strings, integers, floating-point numbers, lists, dictionaries, and sets.
 Control Flow: If-Else statements, For and While loops, functions, and comprehensions.
 File Input/Output: Reading and writing text and binary files, handling exceptions, and working with file paths.
 Modules and Packages: Creating, importing, and using modules and packages in Python.
 Object-Oriented Programming: Classes, objects, inheritance, polymorphism, and encapsulation.
 Regular Expressions: Pattern matching and text processing using regular expressions.
 Databases: Connecting to databases, executing SQL queries, and managing transactions using Python.
 Web Development: Creating and deploying web applications using frameworks such as Flask or Django.
 GUI Development: Building graphical user interfaces using libraries such as Tkinter .
 Scientific Computing: Using libraries such as NumPy, Pandas, and Matplotlib for data analysis and visualization.
 Advanced Topics: Decorators, generators, coroutines, and asynchronous programming using the asyncio library.
 Best Practices: Debugging, testing, optimization, code documentation, and version control.

 A course on Numpy and Pandas in Python typically covers the following topics:

Numpy:
 Introduction to Numpy: Numpy arrays, operations on arrays, and Numpy functions.
 Array Manipulation: Indexing, slicing, reshaping, and stacking arrays.
 Broadcasting: Broadcasting rules in Numpy and broadcasting operations.
 Vectorized Operations: Element-wise operations and universal functions.
 Linear Algebra: Matrix operations, eigenvalues, and singular value decomposition (SVD).

Pandas:
 Introduction to Pandas: Series, Data Frames, and Data Structures in Pandas.
 Data Processing: Indexing, filtering, and grouping data.
 Data Cleaning: Handling missing values, duplicates, and outliers.
 Data Transformation: Merging, joining, and concatenating data.
 Data Visualization: Plotting data using Matplotlib and Pandas.
 Time Series: Indexing and handling time series data.
 Advanced Techniques: Handling large datasets, memory optimization, and performance tuning.
 Case Studies and Applications: Using Numpy and Pandas for real-world data analysis problems such as regression analysis, hypothesis testing, and data visualization.

Statistics:
 Probability Theory: Introduction to probability theory, including basic concepts such as probability distributions, random variables, and expected values.
 Descriptive Statistics: Descriptive statistics methods such as measures of central tendency (mean, median, mode), measures of variability (standard deviation, range, variance), and percentiles.
 Inferential Statistics: Inferential statistics methods such as hypothesis testing, estimation of population parameters, and confidence intervals.
 Bayesian Statistics: Bayesian statistics, including Bayes’ theorem, Bayesian estimation, and Markov Chain Monte Carlo (MCMC) methods.
 Hypothesis Testing: Hypothesis testing methods, including t-tests, ANOVA, and chi-square tests.

 Introduction to AI/ML: Overview of the field, applications, history, and current state of AI/ML.
 Mathematics for AI/ML: Linear algebra, calculus, probability and statistics, optimization.
 Data Preprocessing: Data cleaning, normalization, feature extraction, and selection.
 Supervised Learning: Linear Regression, Logistic Regression, Decision Trees, Random Forests, Support Vector Machines (SVM), k-Nearest Neighbors (k-NN), Naive Bayes, Neural Networks.
 Unsupervised Learning: K-Means, Hierarchical Clustering, Density-Based Clustering, Principal Component Analysis (PCA), Autoencoders.
 Reinforcement Learning: Markov Decision Processes (MDP), Q-Learning, SARSA, Deep Reinforcement Learning.
 Natural Language Processing (NLP): Tokenization, Stemming, Lemmatization, Named Entity Recognition (NER), Part-of-Speech (POS) Tagging, Sentiment Analysis, Word Embeddings.
 Computer Vision: Image Processing, Object Detection, Image Segmentation, Convolutional Neural Networks (CNNs).
 Deep Learning: Artificial Neural Networks, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs).
 Model Selection and Evaluation: Overfitting, underfitting, bias-variance trade-off, cross-validation, performance metrics (e.g. accuracy, precision, recall, F1 score).
 Ethics and Privacy in AI/ML: Bias in AI, privacy and security concerns, responsible AI, interpretability and transparency.
 Applications of AI/ML: Image classification, object detection, speech recognition, natural language processing, robotics, autonomous systems.

Working on Two Real Time industry projects with actual dataset