Nikita ChoudharyTechnical Head
AI & ML Courses
Data Analytics and Machine Learning with Python
Become an expert in the exciting new world of AI & Machine Learning, get trained in cutting edge technologies and work on real-life industry projects with TechSim+.
- Lectures 30
- Duration 30 Days
- MemberShip Yes
- Projects Yes
- Skill level Basic to Advanced
- Language English
- Assessments Yes
Data Analytic and Machine Learning Certification Training using Python helps you gain expertise in various machine learning algorithms such as regression, Classification, clustering, decision trees, random forest, Naïve Bayes K-NN and More. This Machine Learning using Python Training exposes you to concepts of Statistics, Time Series and different classes of machine learning algorithms like supervised, unsupervised algorithms. In This training you will work different Python Library like Pandas, Matplotlib, Sklearn, Numpy, Selenium, and OpenCV.
- Hands on Practical based Training.
- Get Placement Opportunity in AI - ML Companies.
- Live project based on any of the selected use cases, involving implementation of Data Science with Python.
- TechSim+ certifies you in Data Analytic and Machine Learning with Python
- Get Certified by IIT's and NIT's
- Get one Year Membership with TechSim+.
- Make different Types of Projects during Training
Step - 1: Introduction to Artificial Intelligence & Machine Learning
- What is ML?
- Applications of ML
- Why Machine Learning is the Future
- Difference between Machine Learning and Deep Learning
- Types of ML, AI, and Deep Learning
- Introduction and a brief history of AI
- Demo: AI Solution 1 and Solution 2
- Installing Python and Anaconda (MAC & Windows)
Step - 2: PYTHON: Environment Setup and Basic
- Introduction to Anaconda
- Jupyter Notebook Introduction
- Installing Packages
- What is Python?
- Programming Language.
- Getting Started: Downloading and Installing.
- Variables types and properties.
- Strings types (raw, Unicode), properties, methods, indexing.
- sequencing, slicing, finding string in strings.
- Extracting Links from a webpage.
Step - 3: Python Data Types: List, Tuples, Dictionaries
- Python Lists, Tuples, Dictionaries
- Accessing Values
- Basic Operations
- Indexing, Slicing, and Matrixes
- Built-in Functions & Methods
- Exercises on List, Tuples And Dictionary
- Python program to convert a list to a tuple
Step - 4: Making Decisions – If Statements
- The Relational Operators
- The Logical Operators
- Simple if Statement, if-else Statement
- if-elif Statement
- More Advanced If, ElIf & Else Processing
Step - 5: Loop Control - for, while
- Introduction To while Loops
- Count-Controlled while Loops
- Event-Controlled while Loops
- Using continue, Using break
- Introduction To for Loops
- For loops with files, list, tuples and dictionaries
Step - 6: Functions and Modules
- Introduction To Functions – Why
- Defining and Calling Functions
- Functions With Multiple Arguments
- Function Objects, Generators, Decorators
- Anonymous Functions, Higher-Order Functions
- Using Built-In Modules
- User-Defined Modules
Step - 7: File Handling
- Opening and Closing Files
- open Function, file Object Attributes
- close() Method ,Read, write, seek
- Rename, remove,
- Mkdir, chdir, rmdir and more
Step - 8: Classes & Error Handling
- Overview of OOP-Creating Classes
- Creating Instance Objects
- Class Inheritance, Overriding Methods
- Base Overloading Methods
- Overloading Operators, Data Hiding
- What is Exception, Handling an exception
- The except Clause with No Exceptions ,the try-finally Clause
- User-Defined Exceptions
Step - 9: Introduction to Data Science
- What is Data Science?
- What does Data Science involve?
- Era of Data Science
- Business Intelligence vs Data Science
- Business Intelligence vs Data Science
- Life cycle of Data Science
- Tools of Data Science
Step - 10: Introduction to NumPy & Pandas
- NumPy – arrays
- Operations on arrays
- Indexing slicing and iterating
- Reading and writing arrays on files
- Pandas - data structures & index operations
- Reading and Writing data from Excel/CSV formats into Pandas
Step - 11: Data Extraction, Wrangling (Data Analysis)
- Basic Functionalities of a data object
- Merging of Data objects
- Concatenation of data objects
- Types of Joins on data objects
- Exploring a Dataset
- Analyzing a dataset
- Raw and Processed Data
- Data Wrangling
- Exploratory Data Analysis
Step - 12: Data Visualisation and play with Python Libraries
- Introduction to Pandas, Matplotlib, Seaborn, Scipy
- Plot a Line, Legends and Labels
- Plot Different type of Charts and Histograms
- Loading data from files
- 3D Graphs
- Selection, Filtering, Combining and Merging Data Frames
- Plotting different plot with Seaborn
Step - 13: Closer Look at ML Working
- What Is Machine Learning?
- Types of Machine Learning Systems
- Working with Real Data
- Get the Data
- Discover and Visualize the Data to Gain Insights
- Prepare the Data for Machine Learning Algorithms
- Handling Text and Categorical Attributes
- Select and Train a Model
- Fine-Tune Your Model
Step - 14: Supervised Learning - I
- Machine Learning Categories
- Regression and Classification
- Gradient descent
- What is Classification and its use cases?
- What is Naïve Bayes?
- How Naïve Bayes works?
- • Implementing Naïve Bayes Classifier
- What is Support Vector Machine?
- Illustrate how Support Vector Machine works?
- Implementation of Naïve Bayes, SVM
Step - 15: Supervised Learning - II
- K-Nearest Neighbors (K-NN)
- What is Decision Tree?
- Algorithm for Decision Tree Induction
- Creating a Perfect Decision Tree
- Confusion Matrix
- Random Forests and Extremely Random Forests?
Step - 16: Unsupervised Learning
- What is Clustering & its Use Cases?
- What is K-means Clustering?
- How K-means algorithm works?
- How to do optimal clustering
- What is C-means Clustering?
- What is Hierarchical Clustering?
- Implementing K-means Clustering
- Implementing Hierarchical Clustering
Step - 17: Browser Automation and Data Scrapping
- Introduction to Web drive
- Guide to install Web driver
- Accessing Forms in Web driver
- Accessing Links and Table content in Web driver
- Automation of Facebook and Indeed
- Data Analysis of Indeed Data
Step - 18: Image Processing and Face Detection
- What is OpenCV?
- What is Image Processing.
- Loading Video Source
- Writing on Image
- Image Arithmetic’s and Logic
- Detecting Face and Eye
- Training a Model with Your Face
- Matching your Face
Step - 19: Make Chat Bot by Google
- What is Google dialogflow.
- How it works.
- How to Train Pre Builded Agent with your Data
- Create New Agents to Interact with You.
- Get API and Write Python Script for your Agent
- Create ChatBot Completely
- Voice Chatting with your Chatbot
Step - 20: Hands on Projects
- Plotting different type of charts on Real data
- Send Message to all your Facebook Friends
- Real Time Classify Hand Written Digits
- Predict Interest Rate on Loan
- Analysis of Data Science Jobs Postings From Indeed | Web Scraping Selenium
- Building Movie or YouTube Video Recommender System
- Face Detection System
Nikita ChoudharyTechnical Head
Nikita have a very vibrant and dynamic personality with great teaching skills. She had worked in a number of industrial project and hence have a very good knowledge of the current demand of recruiters. She have very good teaching skills that helps students to gain interest in the topic.
Prateek MishraFounder & Chief of TechSim+
Prateek is an entrepreneur and thought Leader in Artificial Intelligence deep-tech industries. He is a leading trainer with expertise in AI, Machine Learning, Data Analytic, Deep Learning, Python, Embedded and IOT, Julia Programming, Blockchain and Tableau. Prateek has Successfully conducted 200+ workshops.