Machine Learning (ML) is basically that field of computer science with the help of which computer systems can provide sense to data in much the same way as human beings do. In simple words, ML is a type of artificial intelligence that extract patterns out of raw data by using an algorithm or method. The key focus of ML is to allow computer systems to learn from experience without being explicitly programmed or human intervention.
What you'll learn
- Train machine learning algorithms to predict house prices,
- Identify handwriting, detect cancer cells & more
- Solve any problem in your business,
- Job or personal life with powerful Machine Learning models
- Go from zero to hero in Python, Seaborn, Matplotlib,
- Scikit-Learn, SVM, unsupervised Machine Learning etc
Requirements
- Desktop/ Laptop
- Internet connectivity min 2 Mbps
Target Audience
- Data Scientist
- Data Analyst
- Data Engineer
- ML Engineer
- Software Developer
- Overview and Application of Data Science
- Terminologies commonly used in data science
- Data Science roles
- Data Science Life Cycle: Essential Stages
- Data Science project organization challenges
- 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
- 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
- Logical Indexing
- Grouping Data
- 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)
- The Subplots and axes in matplotlib Showing Images
- Introduction
- Sample and Population
- Measures of Central Tendency
- Covariance and Correlation
- Exploratory Data Analysis
- Practical Example – Descriptive Statistics/EDA
- Introduction to Artificial Intelligence and Machine Learning
- Unsupervised Machine Learning
- Essential Mathematics for Data Science and Statistics
- Data Mining using Python
- Data Wrangling
- Feature Engineering and Feature Selection
- Supervised Machine Learning – A deeper dive
- Classification
- Decision Trees and Random Forests
- Unsupervised Learning
- Ensemble Learning
- Time Series Modeling
- Building a Recommender System
- Deep Learning with Keras and Tensor Flow
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.
