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Machine Learning with Python Certification Training

1 day, 6 hours
5/5

Machine Learning with Python Certification training at Zx Academy gives you a complete understanding of the essential topics covered in the Machine Learning with Python Certification Training course. In addition, getting certified in Machine Learning and increasing your income potential, Zx Academy demonstrates the skills required to be an effective Machine Learning Engineer. The Machine Learning with Python Certification validates the ability to produce high-quality results with increased efficiency.

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Course Overview

Zx Academy's Machine Learning with Python Certification Course training helps a candidate attain expertise in many Machine Learning algorithms like clustering, regression, random forest, decision trees, Q-Learning, and Naive Bayes. This training ensures the candidate understands the concepts of Time Series, Statistics, and various classes of Machine Learning algorithms like unsupervised, supervised, and reinforcement algorithms. The comprehensive Machine Learning (ML) and Python course explain the basic programming and statistics required to work on the problems of ML. The candidate will learn ML with Python by beginning with the packages required for Machine Learning. Also, it covers statistical distributions and explains various types of data. Machine Learning with Python course teaches the candidate a type of ML called reinforcement learning. Highlights of Zx Academy Training:
  • 24/7 Support throughout the training period
  • Live online classes
  • Training under industry experts
  • Free study material
  • Flexible timings to all our students
What will you learn in Machine Learning with Python training? After completion of the Machine Learning with Python training, you will learn:
  • Python and its Libraries
  • Filtering and Sorting
  • Loops and Functions
  • Filtering and Sorting
  • Summarising Data
  • Working on Filtering and Adding Columns
  • Visualization Libraries
  • Types of Data
  • Descriptive Statistics
  • Basics of Statistics
  • Measures of Dispersion
  • Measures of Central Tendency
  • Q-Learning
  • Framework of Reinforcement Learning
Who should take this Machine Learning with Python training? The Machine Learning with Python training course is suited for:
  • Developers aspiring to become a "Machine Learning Engineer"
  • Business Analysts who want to know the techniques of Machine Learning
  • Analytics Managers
  • Python Professionals
What are the prerequisites for taking Machine Learning with Python training? The prerequisites for taking Machine Learning with Python Certification training are:
  • Fundamentals of Data Analysis
  • Basics of Python Language
Why should you go for Machine Learning with Python training? The Machine Learning with Python certification course employs theories and techniques drawn from several fields within the areas of statistics, mathematics, computer science, and information science. This course exposes the candidate to various classes of Machine Learning algorithms such as unsupervised, supervised, and reinforcement algorithms. After completion of this training, you will be able to automate data analysis using Python, validate Machine Learning algorithms, and learn techniques for predictive modeling. Salary as per market According to Glassdoor, the average salary of a Machine Learning Engineer is Rs.10,00,000 per annum.

Curriculum – Machine Learning with Python

  • What is Data Science?
  • What does Data Science involve?
  • Era of Data Science
  • Business Intelligence vs Data Science
  • Life cycle of Data Science
  • Tools of Data Science
  • Introduction to Python
  • Data Analysis Pipeline
  • What is Data Extraction
  • Types of Data
  • Raw and Processed Data
  • Data Wrangling
  • Exploratory Data Analysis
  • Visualization of Data
  • Python Revision (numpy, Pandas, scikit learn, matplotlib)
  • What is Machine Learning?
  • Machine Learning Use-Cases
  • Machine Learning Process Flow
  • Machine Learning Categories
  • Linear regression
  • Gradient descent
  • What is Classification and its use cases?
  • What is Decision Tree?
  • Algorithm for Decision Tree Induction
  • Creating a Perfect Decision Tree
  • Confusion Matrix
  • What is Random Forest?
  • Introduction to Dimensionality
  • Why Dimensionality Reduction
  • PCA
  • Factor Analysis
  • Scaling dimensional model
  • LDA
  • What is Naïve Bayes?
  • How Naïve Bayes works?
  • Implementing Naïve Bayes Classifier
  • What is Support Vector Machine?
  • Illustrate how Support Vector Machine works?
  • Hyperparameter optimization
  • Grid Search vs Random Search
  • Implementation of Support Vector Machine for Classification
  • 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?
  • How Hierarchical Clustering works?
  • What are Association Rules?
  • Association Rule Parameters
  • Calculating Association Rule Parameters
  • Recommendation Engines
  • How Recommendation Engines work?
  • Collaborative Filtering
  • Content Based Filtering
  • What is Reinforcement Learning
  • Why Reinforcement Learning
  • Elements of Reinforcement Learning
  • Exploration vs Exploitation dilemma
  • Epsilon Greedy Algorithm
  • Markov Decision Process (MDP)
  • Q values and V values
  • Q – Learning
  • α values
  • What is Time Series Analysis?
  • Importance of TSA
  • Components of TSA
  • White Noise
  • AR model
  • MA model
  • ARMA model
  • ARIMA model
  • Stationarity
  • ACF & PACF
  • What is Model Selection?
  • Need of Model Selection
  • Cross – Validation
  • What is Boosting?
  • How Boosting Algorithms work?
  • Types of Boosting Algorithms
  • Adaptive Boosting

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