Artificial intelligence is intelligence demonstrated by machines, as opposed to natural intelligence displayed by animals including humans.
What you'll learn
- The basics of Machine Learning.
- The implementation of Logistic Regression.
- The basics of training a Deep Neural
- The implementation of DNN on any dataset.
- The implementation of a complete DNN using Numpy.
- The basics of Neural Networks.
- The architecture of Neural Networks
Requirements
- Desktop/ Laptop
- Internet connectivity min 2 Mbps
Target Audience
- Data Scientist
- Data Analyst
- Data Engineer
- Software Engineer
- Overview and Application of Data Science
- Terminologies commonly used in data science
- Data Science roles
- Data Science Life Cycle
- Formulation of problems and framework to choose the right problems
- Hypothesis Formation
- Build Comprehensive Hypothesis set
- Excel Basics, Formulas and Functions
- Excel Charts, Pivot Tables, Sort, Filter, What-if Analysis tool
- Build business simulation using Excel
- Python Basics
- Python Data Structures
- Python Programming Fundamentals
- Working with Data in Python
- Working with NumPy Arrays
- Python Numpy Arrays
- Creating, Accessing, Manipulating Numpy Array
- Numpy Data Types
- Array Attributes
- Data Operations
- Common Arithmetic and Statistical Methods Sort, Search, Count
- Creating, Accessing, Manipulating Pandas Data
Series and The Series and DataFrame
DataFrame Attributes & Basic Functions
Iteration on Data - Logical Indexing; Sorting & Reindexing
Merging, Joining & Concatenation of Data
Pandas File Handling - Grouping Data
Function Application
Missing Data & Treatment
Date & Time Functionality
- Visualization Libraries in Python
- MATPLOTLIB PYPLOT: line, scatter, pie, box, area etc
- Decorating the plots using Matplotlib (labels, colors, markers, legend, grids, figure sizes etc)
- Easy and advanced Data Visualization from Seaborn
- Categorical, Distributive, Regression, Matrix, Grid Plots
- Customizing Color Themes
- Introduction
- Sample and Population
- Measures of Central Tendency
- Covariance and Correlation
- Exploratory Data Analysis
- Practical Example – Descriptive Statistics/EDA
- Introduction
- AI and ML as tools to solve Data Science Problems.
- Various Types of Machine Learning Models
- Introduction to Classification using Logistic Regression and its Mathematical Basis
Case Study – Build a Logistic Regression Model to solve Data Science Problem
- Why Unsupervised Learning?
- Tasks that can be solved using Unsupervised Learning
- Clustering Types: K-Means
- Hierarchical Clustering and Spectral Clustering
- Fundamental of KNN
- Case Study – Build a Logistic Regression Model to solve Data Science Problem
This course does not require any prior expertise but we need a passionate mind that wants to learn and experience things with us.
The course delivered will be completely online.
The course will be delivered in hybrid mode, which means our trainers will be taking sessions online along with recorded lectures on our platform.
The fee once paid is not refundable or adjustable under any circumstances in future.
