Students Trained

800+

Course Duration

60+ Hours

Real Project's

5+

New Batch Starting
24th June & 1st July 2019

Discount Offer

Now Fee: ₹ 6000/-

GROUP DISCOUNT: For group payment of min. 4 students Get 10% Discount (per head)

Key features
  • 60+ hours of instructor-led training.
  • 5+ projects During Training.
  • Certification from Technex'19.
  • Module wise projects.
  • Module .
What is Python Machine Learning Course about?
  • Master the concepts of supervised and unsupervised learning, recommendation engine, and OpenCV Image Processing.
  • Gain practical mastery over principles, algorithms, and applications of machine learning through a hands-on approach that includes working on five major end-to-end projects and 10+ hands-on exercises
  • Implement models such as support vector machines, naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-means clustering and more in Python.

Frequently asked questions

Machine learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of machine learning.

1. Data Analyst who want to gain expertise in Predictive Analytics.
2. Developers.
3. Data Architects.
4. Tech Leads handling a team of Analysts.

1. Basic Python programming knowledge and fundamentals of data analysis required.
2. Basic knowledge of statistics and mathematics is good to have. .

Course Content

Step - 1
Introduction to 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 Essentials
  • Introduction to Anaconda
  • Jupyter Notebook Introduction
  • Installing Packages
  • Lists, Tuple ,Dictionaries
  • Conditional Statements
  • Looping, Control Statements
  • Functions
Step - 3
Data Visualization and play with Python Libraries
  • Introduction to Pandas, Matplotlib, NumPy
  • Data Structures & Data Frame,
  • Pandas File Read and Write Support
  • 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
Step - 4
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 Zomato and Justdial
Step - 5
Classification Techniques
  • Introduction of Classification
  • Loading MNIST Data Sets
  • Visualization of Hand Written Digits
  • Training a Binary Classification
  • Multiclass Classification
  • Load Iris Data Sets
  • Naive Bayes Classifier
  • Multinomial NB
Step - 6
Evaluation and improvement techniques of Classification Models
  • Accuracy measurement of classifiers
  • Confusion Matrix
  • Precision, Recall
  • F1-Score, RoC, AuC
  • N-fold cross validation
Step - 7
Training Models and Regression Techniques
  • Types of learning
  • Training and Testing Data
  • Simple Linear Regression
  • Multiple Linear Regression
  • Apply Regression on Bank_Loan Datasets
  • Hands-on Polynomial Regression
  • Logistic Regression
  • Digit Recognition using Logistic Regression
Step - 8
Comprehensive Classification Models
  • Support Vector Machine (SVM)
  • K-Nearest Neighbors (K-NN)
  • Decision Tree Classification
  • Training and Visualizing a Decision Tree
  • Random Forests and Extremely Random Forests?
  • Implementation of the above models for real-world Dataset.
Step - 9
Unsupervised Learning and Clustering
  • What is Un-Supervised Learning?
  • Clustering data with K-Means algorithm
  • Mean Shift algorithm
  • Gaussian Mixture Models?
Step - 10
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
Steep - 11
Hands on Projects
  • Plotting different type of charts on Real data
  • Automate your Browser
  • Scrape Zomato Data
  • Classify Hand Written Digits
  • Classify Iris Data set
  • Predict Interest Rate on Loan
  • Predict Loan Approval
  • Predicting Traffic Using Random Forest
  • Building Movie or YouTube Video Recommender System
  • Face Detection System

Programming Language and Tools we Used

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