Machine Learning Training
Tecklearn’s Machine Learning training will help you develop the skills and knowledge required for a career as a Machine Learning Engineer. It helps you gain expertise in various machine learning algorithms such as regression, clustering, decision trees, random forest, Naïve Bayes and Q-Learning. This Machine Learning Certification Training exposes you to concepts of Statistics, Time Series and different classes of machine learning algorithms like supervised, unsupervised and reinforcement algorithms. With these key concepts, you will be well prepared for the role of Machine Learning (ML) engineer. In addition, it is one of the most immersive Machine Learning online courses, which includes hands-on projects
Why Should you take Machine Learning Training?
• The average machine learning salary, according to Indeed's research, is approximately $146,085 (an astounding 344% increase since 2015). The average machine learning engineer salary far outpaced other technology jobs on the list.
• IBM, Amazon, Apple, Google, Facebook, Microsoft, Oracle & other MNCs worldwide are using Machine Learning for their Data analysis
• The Machine Learning market is expected to reach USD $8.81 Billion by 2022, at a growth rate of 44.1-percent, indicating the increased adoption of Machine Learning among companies. By 2020, the demand for Machine Learning engineers is expected to grow by 60-percent.
Curriculum
Introduction to Machine Learning
• Need of Machine Learning
• Types of Machine Learning - Supervised, Unsupervised and Reinforcement Learning
• Applications of Machine Learning
Concept of Supervised Learning and Linear Regression
• Concept of Supervised learning
• Types of Supervised learning: Classification and Regression
• Overview of Regression
• Types of Regression: Simple Linear Regression and Multiple Linear Regression
• Assumptions in Linear Regression and Mathematical Concepts behind Linear Regression
• Hands On
Concept of Classification and Logistic Regression
• Overview of the Concept of Classification
• Comparison of Linear regression with Logistic regression
• Mathematics behind Logistic Regression: Detailed Formulas and Functions
• Concept of Confusion matrix and Accuracy Measurement
• True positives rate, False positives rate
• Threshold evaluation with ROCR
• Hands on
Concept of Decision Trees and Random Forest
• Overview of Tree Based Classification
• Concept of Decision trees, Impurity function and Entropy
• Concept of Impurity function and Information gain for the right split of node and
• Concept of Gini index and right split of node using Gini Index
• Overfitting and Pruning Techniques
• Stages of Pruning: Pre-Pruning, Post Pruning and cost-complexity pruning
• Introduction to ensemble techniques and Concept of Bagging
• Concept of random forests
• Evaluation of Correct number of trees in a random forest
• Hands on
Naive Bayes and Support Vector Machine
• Introduction to probabilistic classifiers
• Understanding Naive Bayes Theorem and mathematics behind the Bayes theorem
• Concept of Support vector machines (SVM)
• Mathematics behind SVM and Kernel functions in SVM
• Hands on
Concept of Unsupervised Learning
• Overview of Unsupervised Learning
• Types of Unsupervised Learning: Dimensionality Reduction and Clustering
• Types of Clustering
• Concept of K-Means Clustering
• Mathematics behind K-Means Clustering
• Concept of Dimensionality Reduction using Principal Component Analysis (PCA)
• Hands on
Natural Language Processing and Text Mining Concepts
• Overview of Concept of Natural Language Processing (NLP)
• Concepts of Text mining with Importance and applications of text mining
• Working of NLP with text mining
• Reading and Writing to word files and OS modules
• Text mining using Natural Language Toolkit (NLTK) environment: Cleaning of Text, Pre-Processing of Text and Text classification
• Hands on
Introduction to Deep Learning
• Overview of Deep Learning with neural networks
• Biological neural network Versus Artificial neural network (ANN)
• Concept of Perceptron learning algorithm
• Deep Learning frameworks and Tensor Flow constants
• Hands on
Time Series Analysis
• Concept of Time series analysis, its techniques and applications
• Time series components
• Concepts of Moving average and smoothing techniques such as exponential smoothing
• Univariate time series models
• Multivariate time series analysis and the ARIMA model
• Time series in Python
• Sentiment analysis using Python (Twitter sentiment analysis Use Case) and Text analysis
• Hands on
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