Machine Learning

Course Description

Machine learning is a branch of artificial intelligence technology that combines a set of algorithms and statistical models that are used for future predictions and identifying patterns in data. Machine learning enables computer systems to learn automatically with less human involvement. ML is leading technology applicable in the fields of data science, analytics, business intelligence, search engine, and e-commerce. 

Machine learning with AI and data mining technology makes a more effective way to analyze huge quantities of data. ML applies complex calculations to big data to produce reliable, faster, and accurate results. Machine Learning is a fast-growing technology that has much future scope with many job opportunities.

Hachion Machine Learning online training offers in-depth knowledge of all basic and advanced topics. Our ML tutorial includes all key modules such as python, algorithms, supervised & unsupervised learning, statistics & probability, decision trees, random forests, linear & logistic regression. Beginners can also learn ML easily to get placed as a Machine Learning Engineer.

Course Fee : 320 USD

Machine Learning Learners from Hachion: 68
Course Schedule

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Choose the best training mode which suits to your requirement
Live online training

USD 320

Training Fee: USD 400 20% Discount

  • Live interactive online training
  • Daily Assignments and Lab exercises
  • Resume and certification guidance
  • Mock interview and live project assistance
  • Resume marketing and job assistance
Mentoring mode training
  • Live interactive online training
  • Daily Assignments and Lab exercises
  • Resume and certification guidance
  • Mock interview and live project assistance
  • Resume marketing and job assistance
Live online training and internship
  • Live interactive online training
  • Daily Assignments and Lab exercises
  • Resume and certification guidance
  • Mock interview and live project assistance
  • Resume marketing and job assistance

Course Content

  • Keywords and Identifiers
  • Comments, Indentations, and Statements
  • Variables and Data Types in Python
  • Standard Input and Output
  • Operators Control Flow: If Else
  • Control Flow: While Loop
  • Control Flow: For Loop
  • Control Flow: Break and Continue
  • Lists, Tuples Part
  • Tuples Part 2: Sets
  • Dictionary Strings
  • Types of Functions and Function Arguments
  • Recursive Functions, Lambda Functions, Modules
  • Packages, File Handling
  • Exception Handling, Debugging Python
  • Numpy Introduction
  • Numerical Operations on Numpy
  • Need and Use of EDA
  • Exploring the IRIRS Dataset
  • 2D Scatter Plot
  • 3D Scatter Plot
  • Pair Plots, Histogram
  • PDF, Univariate Analysis using PDF
  • CDF - Cumulative Distributive Function
  • Mean, Variance, and Standard Deviation, Median
  • Percentiles and Quantiles IQR ( Inter Quartile Range) 
  • MAD ( Mean Absolute Deviation)
  • Box- Plot with Whiskers, Violin Plots
  • Univariate, Bivariate and Multi-Variate Analysis
  • Multivariate Probability Density, Contour Plot
  • Introduction to Dimensionality Reduction
  • Representing Datasets using Row and Column Vectors
  • Representation of Datasets as a Matrix
  • Data Pre Processing - Feature Normalisation
  • Mean of Data Matrix
  • Column Standardization
  • Co-Variance of Data Matrix
  • PCA, PCA with a Code Example
  • Introduction and use of PCA, Geometric Intuition of PCA
  • The mathematical objective function of PCA
  • Distance Minimization
  • Eigen Values and Eigen Vectors ( PCA): Dimensionality Reduction
  • PCA for Dimensionality Reduction and Visualization
  • Limitation of PCA and PCA with a Code Example
  • Supervised Learning
  • Geometric Intuition of Logical Regression
  • Squashing using Sigmoid Function
  • Objective Function mathematical formulation
  • Weight Vector
  • L2 Regularization: Overfitting and Underfitting
  • L1 Regularization and sparsity
  • Probabilistic Interpretation: Gaussian Naive Bayes Loss minimization representation
  • Hyperparameter Search: Grid Search and Random Search
  • Column Standardization
  • Feature Importance and Model Interpretability
  • Collinearity of Features
  • Test/ Run Time Space and Time Complexity
  • Real-World Cases
  • Non-Linearly separable data and Feature Engineering
  • GridSearchCV, RandomSearchCV
  • Extensions to Logistic Regression: Generalised Linear Models
  • Working of Biological Neurons
  • Growth of Biological Neural Networks
  • Diagrammatic representation: Logistic Regression and Perceptron, Multi-Layered Perceptron (MLP)
  • Notation, Training a Single-Neuron Model
  • Training an MLP: Chain Rule Training an MLP: Memorization, Back Propagation, Activation Functions
  • Vanishing Gradient Problem
  • Bias-Variance tradeoff, Decision Surfaces, Playground
  • Axis Parallel Hyperplanes, Sample Decision Tree
  • Building a Decision Entropy
  • Building a Decision Tree: Information Gain
  • Building a Decision Tree: Gini Impurity
  • Building a Decision Tree: Constructing a DT
  • Building a Decision Tree: Splitting Numerical Features, Features Standardization
  • Building a Decision Tree: Categorical Features with many possible values
  • Overfitting and Underfitting
  • Train and Run Time Complexity
  • Regression using Decision Trees
  • Cases, Code Samples
  • Conditional Probability
  • Independent Vs Mutually Exclusive Events
  • Bayes Theorem with Examples
  • Exercise Problems on Bayes Theorem
  • Naive Bayes Algorithm
  • Toy Example: Train and Test stages
  • Naive Bayes on Test Data
  • Laplace/ Additive Smoothing
  • Log Probabilities for Numerical Stability
  • Bias and Variance Tradeoff
  • Feature Importance and Interpretability
  • Imbalanced Data, Outliers, Missing Values
  • Handling Numerical Features ( Gaussian NB)
  • Multiclass Classification, Similarity or Distance Matrix
  • Large Dimensionality, Best and Worst Cases
  • Geometric Intuition
  • Why we take values of +1 and -1 for Support Vector Planes
  • Mathematical derivation
  • Loss Function (Hinge Loss) based Interpretation
  • Dual Form of SVM Formulation
  • Kernel Trick, Polynomial Kernel, RBF-Kernel
  • Domain-specific Kernels
  • Train and Run Time Complexities
  • nu-SVM: Control Errors and Support Vectors
  • SVM Regression Cases
  • Clustering
  • What is Clustering?
  • Unsupervised Learning
  • Applications
  • Metrics for Clustering
  • K-Means: Geometric Intuition
  • Centroids
  • K-Means: Mathematical Formulation: Objective Function
  • K-Means: Algorithm
  • How to initiate K-Means++.
  • Failure Cases/ Limitations
  • K-Medoids?
  • Determining the Right K
  • Code Samples
  • Time and Space Complexity
  • Agglomerative and Divisive
  • Dendrograms
  • Agglomerative Clustering
  • Proximity Methods: Advantages and Limitations
  • Time and Space Complexity
  • Limitations of Hierarchical Clustering
  • Code Sample
  • Density-based Clustering
  • MinPts and Eps: Density
  • Core
  • Border and Noise Points
  • Density Edge and Density Connected Points
  • DBSCAN Algorithm
  • Hyper Parameters: MinPts and Eps

Machine Learning Training FAQs

  • AI (including ML) has the potential to create $1.4T to $2.6T of value in marketing and sales across the world’s businesses. -Machine learning has the capability of improving product quality up to 35% in discrete manufacturing industries, according to Deloitte's recent report.
  • Almost 50% of companies that adopted AI over the next 5 to 7 years have the potential to double their cash flow with manufacturing leading all industries due to its heavy reliance on data according to McKinsey.
  • AI and machine learning-based product defect detection and quality assurance show the potential to increase manufacturing productivity by 50% or more. Machine Learning is among the top tech skill in 2020 by Gartner research.

As the term suggests, it is a research and technology-oriented job and that’s what makes it interesting and different from other jobs. Machine Learning professionals are the highest paid IT professionals since it needs in-depth knowledge of statistics, love for data, logical reasoning, critical thinking, understanding of mathematics, and a fair amount of programming skills.

  • If you love to explore and have the attitude to learn then you don’t need any masters or the Ph.D. degree to become a Machine Learning Engineer.
  • Professionals in different job functions or industries who want to help their company leverage Machine Learning should learn Machine Learning.
  • Apart from students, other professionals who can benefit from learning Machine Learning are database administrators, business analysts, Statisticians, researchers, computer scientists, and data engineers.

Machine Learning & Deep Learning are the subset of Artificial Intelligence (AI). Data Science includes Machine learning & Deep Learning as predictive models along with Exploratory Data Analysis. A Data Scientist needs to have good ML skills and intuition to raise right question to solve business problems. Whereas, ML & DL engineers are only focus towards specific prediction problems like image processing, Natural language processing (NLP), pattern recognition, Semantics, Crowd Computing, Sentiment Analysis etc. So, in brief a DS knows the fundamentals of all ML & DL techniques for basic problem solving whereas ML engineers are more focused on specific problems.

Hachion provides self-certification to the candidates upon successful completion of their course. Unlike earlier days industry values subject knowledge over certification hence one should be thorough with the subject

We provide 100% job assistance to the Hachion students, once they complete the course. We also provide resume writing, mock interviews and resume marketing services as part of our job assistance program.

We offer three modes of training in Machine Learning online training program.

  • Self Placed
  • Mentorship
  • Instructor-Led

According to indeed, the average salary for a Machine Learning Engineer is $138,582 per year in the United States

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