Why should you learn Machine Learning?
As per Indeed, the average income of Machine Learning Engineers is about US$140,579 per annum in the United States
- The growth rate for ML jobs is about 350%!
- Automation is the trending face of technology.
In the world we live in today, ML has proved itself to be among the hottest and demanding technologies available out there.
Hence, by leveraging Edutech Skills Machine Learning training course, you will be exposed to numerous high-paying job opportunities.
What will you learn in this Machine Learning certification training?
As part of this ML program, you will master the skills mentioned below, and you will become a successful Machine Learning Engineer:
- Teaching machines using data
- Representation of artificial neural networks
- Understanding machine learning models like supervised and unsupervised learning in depth
- Categorizing data using Python and logistic regression
- Understanding k-means clustering, decision trees, and Naive Bayes
- Mastering random forest and designing various applications
- Performing linear regression on multiple variables using Python
- Natural Language Processing and text mining using Python
- Mastering the fundamentals of Deep Learning
- Time series analysis and creating models for the analysis
Who should take up this Machine Learning Training?
Our ML certification training is curated and designed for:
- Professionals working in the domains of Data Science, Analytics, and BI
- Professionals employed in fields of search engines and e-commerce
- Professionals seeking a career change
- Undergraduates and freshers
What are the prerequisites for taking up this Machine Learning classes?
Everyone can take up this Machine Learning online course regardless of their prior knowledge and experience.
What are the objectives of Edutech Skills Machine Learning certification course?
Through our Machine Learning training online, you will master the key concepts of this trending field, such as Python programming, supervised and unsupervised learning, Naive Bayes, NLP, Deep Learning fundamentals, time series analysis, and more. Each session ends with assignments and tasks that you need to solve based on the available dataset. Further, you will work on many industry-specific projects that will solidify your skills and help you find a rewarding job! Also, we will help you in your career with our exclusive job support services.
How do you become a certified Machine Learning engineer?
Edutech Skills offers one of the best Machine Learning training courses that cover all the skills required to become proficient in the ML domain. You will be working on real-world projects that would further enhance your understanding.
What kind of projects will you work on in this applied Machine Learning online program?
- As part of this ML program, you will work on real-world projects in the fields of e-commerce, automation, marketing, sales, banking, Internet, insurance, and more.
- Our projects include building a chatbot to answers customer queries, building a recommendation system, fare prediction for taxi booking, analyzing the trends of COVID-19 with Python, customer churn classifier, etc.
- Upon the successful project completion, your skills will be equivalent to 6 months of comprehensive industry experience.
Module 01 – Introduction to Machine Learning
1.1 Need of Machine Learning
1.2 Introduction to Machine Learning
1.3 Types of Machine Learning, such as supervised, unsupervised, and reinforcement learning, Machine Learning with Python, and the applications of Machine Learning
Module 02 – Supervised Learning and Linear Regression
2.1 Introduction to supervised learning and the types of supervised learning, such as regression and classification
2.2 Introduction to regression
2.3 Simple linear regression
2.4 Multiple linear regression and assumptions in linear regression
2.5 Math behind linear regression
1. Implementing linear regression from scratch with Python
2. Using Python library Scikit-Learn to perform simple linear regression and multiple linear regression
3. Implementing train–test split and predicting the values on the test set
Module 03 – Classification and Logistic Regression
3.1 Introduction to classification
3.2 Linear regression vs logistic regression
3.3 Math behind logistic regression, detailed formulas, the logit function and odds, confusion matrix and accuracy, true positive rate, false positive rate, and threshold evaluation with ROCR
1. Implementing logistic regression from scratch with Python
2. Using Python library Scikit-Learn to perform simple logistic regression and multiple logistic regression
3. Building a confusion matrix to find out accuracy, true positive rate, and false positive rate
Module 04 – Decision Tree and Random Forest
4.1 Introduction to tree-based classification
4.2 Understanding a decision tree, impurity function, entropy, and understanding the concept of information gain for the right split of node
4.3 Understanding the concepts of information gain, impurity function, Gini index, overfitting, pruning, pre-pruning, post-pruning, and cost-complexity pruning
4.4 Introduction to ensemble techniques, bagging, and random forests and finding out the right number of trees required in a random forest
1. Implementing a decision tree from scratch in Python
2. Using Python library Scikit-Learn to build a decision tree and a random forest
3. Visualizing the tree and changing the hyper-parameters in the random forest
Module 05 – NaÃ¯ve Bayes and Support Vector Machine (self-paced)
5.1 Introduction to probabilistic classifiers
5.2 Understanding Naïve Bayes and math behind the Bayes theorem
5.3 Understanding a support vector machine (SVM)
5.4 Kernel functions in SVM and math behind SVM
1. Using Python library Scikit-Learn to build a Naïve Bayes classifier and a support vector classifier
Module 06 – Unsupervised Learning
6.1 Types of unsupervised learning, such as clustering and dimensionality reduction, and the types of clustering
6.2 Introduction to k-means clustering
6.3 Math behind k-means
6.4 Dimensionality reduction with PCA
1. Using Python library Scikit-Learn to implement k-means clustering
2. Implementing PCA (principal component analysis) on top of a dataset
Module 07 – Natural Language Processing and Text Mining (self-paced)
7.1 Introduction to Natural Language Processing (NLP)
7.2 Introduction to text mining
7.3 Importance and applications of text mining
7.4 How NPL works with text mining
7.5 Writing and reading to word files
7.6 Language Toolkit (NLTK) environment
7.7 Text mining: Its cleaning, pre-processing, and text classification
1. Learning Natural Language Toolkit and NLTK Corpora
2. Reading and writing .txt files from/to a local drive
3. Reading and writing .docx files from/to a local drive
Module 08 – Introduction to Deep Learning
8.1 Introduction to Deep Learning with neural networks
8.2 Biological neural networks vs artificial neural networks
8.3 Understanding perception learning algorithm, introduction to Deep Learning frameworks, and TensorFlow constants, variables, and place-holders
Module 09 – Time Series Analysis (self-paced)
9.1 What is time series? Its techniques and applications
9.2 Time series components
9.3 Moving average, smoothing techniques, and exponential smoothing
9.4 Univariate time series models
9.5 Multivariate time series analysis
9.6 ARIMA model and time series in Python
9.7 Sentiment analysis in Python (Twitter sentiment analysis) and text analysis
1. Analyzing time series data
2. The sequence of measurements that follow a non-random order to recognize the nature of the phenomenon
3. Forecasting the future values in the series